Forecasting and Planning for Volcanic Hazards, Risks, and Disasters 012818082X, 9780128180822

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Forecasting and Planning for Volcanic Hazards, Risks, and Disasters

Hazards and Disasters Series

Forecasting and Planning for Volcanic Hazards, Risks, and Disasters Volume 2 Series Editor

John F. Shroder Emeritus Professor of Geography and Geology Department of Geography and Geology University of Nebraska at Omaha Omaha, NE 68182

Volume Editor

Paolo Papale Istituto Nazionale di Geofisica e Vulcanologia Pisa, Italy

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818082-2 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

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Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Agudelo Restrepo Adriana (585), Colombian Geological Survey—Volcanological and Seismological Popaya´n Observatory, Bogota, Colombia Narva´ez Zun˜iga Andr
es (585), Colombian Geological Survey—Volcanological and Seismological Popaya´n Observatory, Bogota, Colombia ´ Alvaro Aravena (239), Dipartimento di Scienze della Terra, Universita` di Firenze, Florence, Italy M
endez Fajury Ricardo Arturo (585), Colombian Geological Survey— Volcanological and Seismological Manizales Observatory, Bogota, Colombia Joel C. Bandibas (565), Geological Survey of Japan, AIST, Tsukuba, Japan S
ebastien Biass (329, 473), Earth Observatory of Singapore, Nanyang Technological University, Singapore, Singapore Costanza Bonadonna (329), Department of Earth Sciences, University of Geneva, Geneva, Switzerland R. Caldero´n (617), School of Earth and Environment, University of Canterbury, Christchurch, New Zealand C. Cardona (617), SERNAGEOMIN, Obsevatorio Volcanolo´gico de los Andes del Sur, Temuco, Chile Raffaello Cioni (285), Earth Sciences Department, University of Florence, Florence, Italy Mattia de0 Michieli Vitturi (239), Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy E. Del Bello (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy Marie Edmonds (117), Earth Sciences Department, University of Cambridge, Cambridge, United Kingdom F. Flores (617), SERNAGEOMIN, Red Nacional de Vigilancia Volca´nica, Santiago, Chile D. Gaudin (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy; Ludwig Maximilians Universit€at, Munich, Germany Chris E. Gregg (329), Department of Earth Sciences, University of Geneva, Geneva, Switzerland; Department of Geosciences, East Tennessee State University, Johnson City, TN, United States

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xvi Contributors

Masato Iguchi (635), Kyoto University, Disaster Prevention Research Institute, Sakurajima Volcano Research Center, Kagoshima, Japan Susanna Jenkins (473), Earth Observatory of Singapore; Asian School of the Environment, Nanyang Technological University, Singapore, Singapore Castan˜o Vasco Leidy Johana (585), Colombian Geological Survey—Volcanological and Seismological Manizales Observatory, Bogota, Colombia Jackie E. Kendrick (153), Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom David Lallemant (473), Earth Observatory of Singapore; Asian School of the Environment, Nanyang Technological University, Singapore, Singapore L.E. Lara (617), SERNAGEOMIN; CIGIDEN, Research Center for integrated Disaster Risk Management, Santiago, Chile Yan Lavall
ee (153), Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom Tian Ning Lim (473), Asian School of the Environment, Nanyang Technological University, Singapore, Singapore Ivan Lokmer (25), School of Earth Sciences, University College Dublin, Dublin, Ireland Calvache Velasco Marta Lucı´a (585), Colombian Geological Survey—Geohazard Direction, Bogota, Colombia Monsalve Bustamante Marı´a Luisa (585), Colombian Geological Survey— Volcanological and Seismological Manizales Observatory, Bogota, Colombia Warner Marzocchi (545), University of Naples Federico II, Department of Earth, Environmental and Resources Science, Napoli, Italy Go´mez Martı´nez Diego Mauricio (585), Colombian Geological Survey— Volcanological and Seismological Pasto Observatory, Bogota, Colombia Lo´pez V
elez Cristia´n Mauricio (585), Colombian Geological Survey—Volcanological and Seismological Manizales Observatory, Bogota, Colombia Scira Menoni (329), Department of Earth Sciences, University of Geneva, Geneva, Switzerland; Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milan, Italy C. Dan Miller (493), Volcano Disaster Assistance Program, U.S. Geological Survey, Vancouver, WA, United States Christopher G. Newhall (493), Volcano Disaster Assistance Program, U.S. Geological Survey, Vancouver, WA, United States John S. Pallister (493), Volcano Disaster Assistance Program, U.S. Geological Survey, Vancouver, WA, United States Narva´ez Obando Paola (585), Colombian Geological Survey—Volcanological and Seismological Pasto Observatory, Bogota, Colombia Paolo Papale (1, 545), Istituto Nazionale di Geofisica e Vulcanologia, Pisa, Italy

Contributors xvii

Cort
es Jim
enez Gloria Patricia (585), Colombian Geological Survey—Volcanological and Seismological Manizales Observatory, Bogota, Colombia Marco Pistolesi (285), Earth Sciences Department, University of Pisa, Pisa, Italy Michael P. Poland (75), U.S. Geological Survey, Cascades Volcano Observatory, Vancouver, WA, United States Massimo Pompilio (285), INGV, National Institute of Geophysics and Volcanology, Section of Pisa, Pisa, Italy T. Ricci (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy Dmitri Rouwet (439), Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy Gilberto Saccorotti (25), Istituto Nazionale di Geofisica e Vulcanologia, Pisa, Italy Laura Sandri (545), Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy Bruno Scaillet (285), ISTO, Institute of Earth Sciences Orl
eans, CNRS-University of Orl
eans-BRGM, Orl
eans, France P. Scarlato (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy Jacopo Selva (545), Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy Freysteinn Sigmundsson (413), Nordic Volcanological Center, Institute of Earth Sciences, University of Iceland, Reykjavik, Iceland L. Spina (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy J. Taddeucci (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy Shinji Takarada (565), Geological Survey of Japan, AIST, Tsukuba, Japan P-Y Tournigand (379), Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy George Williams (473), Earth Observatory of Singapore; Asian School of the Environment, Nanyang Technological University, Singapore, Singapore Sang-Ho Yun (473), Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States Elske de Zeeuw-van Dalfsen (75), Royal Netherlands Meteorological Institute (KNMI), R&D Department of Seismology and Acoustics, De Bilt, Netherlands

Preface Hazards and risks related to active volcanoes directly involve some 800 million people throughout the world, including big cities and highly industrialized areas in Japan, Italy, New Zealand, and many other countries. Famous volcanoes like Etna and Sakurajima dominating the landscape of Catania and Kagoshima, respectively, and regularly covering them with volcanic ash, Vesuvius and Campi Flegrei surrounding Naples, Krakatau in the Sunda Strait, Yellowstone in Wyoming, United States, and many others, constantly remind us of the power of Nature and the need for us to coexist with it. Understanding volcanoes and volcanic processes, monitoring volcanoes and volcanic areas, forecasting volcanic hazards, and supporting civil authorities in the management of volcanic crises, is the difficult endeavor by volcanologists from all over the world, and one that is constantly and rapidly progressing. It is difficult, as the volcanic processes are highly nonlinear and a substantial part of them is hidden from direct observation, making the forecasts often highly uncertain. And it is constantly and rapidly progressing, as new technologies increasingly allow additional measurements and better accuracy for existing ones, new models disclose additional aspects of the physics related to magma motion and volcanic eruptions, new ideas create novel opportunities for more advanced science, and increasing levels of international cooperation lead to improved standards for volcano surveillance and hazard forecasts, and overall for the scientific management of volcanic crises. This 2nd volume of Volcanic Hazards, Risks, and Disasters follows the first one published in 2015. When I was proposed to edit a new volume on the same general topic, I briefly reviewed the contents in the 2015 book. Compared to the rate of scientific production, a few years can be a significant time; however, I found the 2015 volume to be generally updated and still a valid reference. At the same time, newly published results, additional aspects related to volcanic hazards and risks, and the flourishing discussion on the methods and issues related to the management of volcanic crises, led me to the conclusion that a new effort was more than justified. This volume, which collects the contributes of scientists who are international renown leaders in their respective fields, should be regarded as a complement to the 2015 volume more than just an update. In this spirit we have agreed to term it the “2nd volume,” rather than “2nd edition,” as we believe that the two volumes together provide a compendium of science, technology, open issues, and debate related to Volcanic Hazards, Risks, and Disasters in the second decade of the 21st century, projecting into the third one. xix

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With respect to the previous volume, the methods and problems related to the science of volcanoes that are central for interpreting monitoring data and observations and for forecasting volcanic hazards are treated here with substantially increased detail. In addition, examples of multidisciplinary applications to volcanic emergencies and volcanic forecasts are provided. Chapter 1 introduces the issues and challenges related to volcanic hazard forecasts, including those related to the roles of scientists and the best practices during volcanic emergencies. Chapters 2–7 review the scientific methods employed for understanding volcanoes and forecasting their evolution, including those based on seismicity (Chapter 2), geodesy (Chapter 3), geochemistry (Chapter 4), experimental volcanology (Chapter 5), numerical modeling (Chapter 6), and field and laboratory analysis (Chapter 7). Chapter 8 deals with the risks associated with dispersion and accumulation of tephra from explosive eruptions, while Chapters 9 and 10 take into consideration mildly explosive and effusive eruptions from basaltic volcanoes. Chapter 11 is dedicated to the dynamics of volcanic lakes and their associated hazards. Chapter 12 describes the application of remote sensing to assess the impacts that result from the interaction between volcanic processes and societal assets. Chapters 13 and 14 deal with the difficult issues related to managing the scientific aspects of volcanic crises: the former analyses the problem of getting organized with the myriad of tasks that must be accomplished at a volcano observatory before, during, and after a volcanic crisis; the latter explores the roles of scientists and the drawbacks of an unclear distinction between the processes of scientific analysis and decision making. Finally, Chapters 15–18 illustrate relevant efforts in dealing with volcanic hazards and risks in Asia and Latin America, from volcano emergency planning to web-based information systems to raising awareness among the populations at risk. Paolo Papale Volume Editor

Chapter 1

Some relevant issues in volcanic hazard forecasts and management of volcanic crisis Paolo Papale Istituto Nazionale di Geofisica e Vulcanologia, Pisa, Italy

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Introduction

There are about 800 million people all over the world who are exposed to the hazards from volcanic eruptions, according to the 2015 Global Assessment Report by the United Nations Office for Disaster Risk Reduction (UNISDR, 2015). That assessment takes into account typical impact areas around volcanoes, extending up to tens of km, and in a few cases, to more than 100 km from the vent. People within such areas live under direct menace from a variety of hazardous volcanic phenomena: lava flows, pyroclastic flows, volcanic ash concentrations and accumulation, lahars, tsunamis, landslides and collapses of the volcanic structure, ground deformation, ground shaking, harmful gas concentrations, etc. Notably, the sources of volcanic hazards, and the kinds of risks they involve, are many, in contrast with earthquakes for which the direct risk is mostly associated with ground shaking (e.g., Boore, 2003), even though other kinds of hazardous events may not be neglected (e.g., surface faulting and liquefaction, Wesnousky, 2008; tsunamis, Fujii et al., 2011; etc.). Besides pure scientific curiosity, volcanic risks are a substantial driver for volcano-related research. On one side, scientists strive to describe with increasing accuracy several aspects of the volcano dynamics that can help anticipate volcanic eruptions and describe the space-time evolution of volcanic phenomena. On the other side, much of the volcano-related discussion concerns the roles of scientists during volcanic crises, and the paradigms for efficient interaction and appropriate communication with the stakeholders and the society. Under the dual impulse from scientific understanding and civil defense applications, volcano science has been progressing quickly during the last decades and years. Only 30 years ago volcanology was still, dominantly, a branch of geology. Today it is a fully multidisciplinary science whereby the approaches Forecasting and Planning for Volcanic Hazards, Risks, and Disasters https://doi.org/10.1016/B978-0-12-818082-2.00001-9 Copyright © 2021 Elsevier Inc. All rights reserved.

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2 Forecasting and planning for volcanic hazards, risks, and disasters

and methods of geological research are complemented by those of physics, chemistry, applied mathematics, engineering, as well as any other discipline that can contribute to understanding the substantial complexities of volcanoes, volcanic systems, and volcanic eruptions. Such a multidisciplinary approach fully acknowledges the object-driven, as compared to approach-driven, character of volcano science. Similarly, structural engineering and society-driven elements of investigation are increasingly relevant as a consequence of the substantial impacts from volcanoes and volcanic eruptions, contributing to expand the community of volcano-related experts, and making modern conferences on volcanoes a melting pot of backgrounds, experiences, expertise, and ideas. Thanks to new methods and understanding, we can do much better today than we did in the past. Still, our capabilities are not fully satisfactory when compared to the complexities of high volcanic risk situations. Improvements are needed both in scientific understanding and management, the latter including the roles of scientists and their relationships with the society. In the following I discuss some aspects of the above, which emerge from published papers as well from the vivid discussion that animates many volcano-related meetings during the past few years.

2 Volcanoes as complex dynamic systems Volcanoes and volcanic processes are certainly among the most complex natural processes on Earth. They are multiscale to an extreme: the spatial scales that are relevant to properly deal with just an individual volcanic eruption go from the kilometers of the volcanic system, tens of kilometers of the directly impacted areas, or hundreds to thousands of kilometers of potentially dangerous ash concentrations in the atmosphere, to the submillimeter scales of processes like gas and solid (crystals) phase nucleation and growth affecting the eruption dynamics on a first order. Similarly, the relevant temporal scales span from tens of years characterizing volcanic unrest, to fractions of a second relevant to shock formation and supersonic acceleration during explosive eruptions. Magmas are multiphase, multicomponent materials undergoing continuous phase changes often far from thermodynamic equilibrium, and they display extremely complex rheological behaviors from close to Newtonian to pseudo-plastic or dilatant, with or without a yield strength, depending on melt composition, phase distributions, and local thermodynamic and flow dynamic conditions. Magma visco-elasticity causes a range of responses to thermal and mechanical solicitations, including magma fragmentation transforming the liquid continuum with dispersed crystals and gas bubbles into a gas continuum with dispersed multiphase droplets and fragments of rock, creating volcanic pyroclasts and ash. These and many other complexities characterize the volcanic processes (for a more complete description and discussion of the implications in forecasting

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volcanic hazards, see Sparks, 2003). Most importantly, a large portion of any volcanic system is not accessible to direct observation, and we can only infer its first order characteristics, through inversion of geological data and geophysical and geochemical signals that we record, basically, from the surface. We do not directly observe, as an example, new magma batches rising from depth, opening a path through plastic or brittle rocks, reaching shallower levels while they release gas and nucleate new bubbles and crystals, accumulate within shallow magma chambers, mix with the resident magma or propagate to the surface giving rise to a new eruption. Atmospheric scientists observe and measure in real time the atmosphere from below (i.e., from the Earth’s surface), from within (e.g., through weather balloons and other means of direct measurement) and from above (through satellites), and they update their models and forecasts with a continuous stream of high-quality data and observations. Different from them, volcano scientists must rely on just inference for even the most fundamental characteristics, properties, and processes that lead to eruptions, and with such uncertain knowledge they formulate their forecasts. It is not by chance that once an obvious volcanic threat becomes observable, for example, in the case of dome-forming eruptions that follow the appearance of a dome at the surface, the ensuing dome collapse events leading to destructive pyroclastic flows can be forecast with much more confidence. That was the case for the dome collapses at Merapi in 2006 and 2010 (Surono et al., 2012; Ratdomopurbo et al., 2013; Pallister et al., 2013). In these cases, we shift from the highly uncertain framework of inference regarding subsurface processes, to directly observable surface processes. The processes governing the thermo-fluid and thermo-elastic dynamics of magmas in the underground environment (including their interaction with visco-elasto-plastic surrounding rocks), during their rapid ascent to the surface, within the atmosphere as volcanic plumes, or along the ground as lava flows or density currents of mixed gas and pyroclasts, are deeply nonlinear (Sparks, 2003). Nonlinearity implies aleatoric uncertainty (e.g., Marzocchi et al., 2004), due to the large dependency of the dynamics on initial and boundary conditions beyond any precision that we may attain in measuring them. Epistemic uncertainty derives instead from limited knowledge and understanding of the system configuration, its controlling factors, and the dynamics regulating its processes and outcomes. Both aleatoric and epistemic uncertainties contribute substantially to the uncertainty that characterizes volcanic hazard forecasts (Sparks, 2003; Marzocchi et al., 2004; Jaquet et al., 2008; Lindsay et al., 2010; Marzocchi and Bebbington, 2012; Doyle et al., 2014; Selva et al., 2014; Sheldrake et al., 2017; and many others). Uncertainties do not affect all forecasts to a similar level. For example, forecasts related to just the occurrence of a new eruption can be highly confident in some favorable situations. That may be the case for frequently erupting, open conduit volcanoes like Etna in Sicily, where the application of machine learning techniques to continuous data from the permanent multiparametric monitoring network is proving

4 Forecasting and planning for volcanic hazards, risks, and disasters

accurate in anticipating by tens of minutes up to a few hours the occurrence of violent lava fountains (F. Cannavo`, personal communication). For closed conduit volcanoes undergoing unrest, re-opening is preceded by signals attributed to propagation of magma to shallow levels (e.g., White and McCausland, 2019); shortly before reaching the surface, that normally results in a significant increase in the rate of change of recorded physical and chemical quantities, lowering the uncertainty in forecasting the eruption occurrence (Voight, 1988, 1989; Kilburn, 2012; Selva et al., 2012a; Vasseur et al., 2015; White and McCausland, 2019). In a number of situations, however, and especially for volcanic calderas, large and rapid increase in the unrest level is observed without any eruption shortly following, whereas much less pronounced signals and apparently less rapid evolutions can be seen before a postcaldera eruption (Acocella et al., 2015). That was the case for the Rabaul eruption, Papua New Guinea, in 1994, that was preceded, 10 years in advance, by much stronger earthquakes and more intense earthquake swarms and ground deformation with respect to the same phenomena preceding the eruption (Saunders, 2001; Acocella et al., 2015). Similar complexities are definitely hard to be dealt with. With volcanic eruptions, uncertainties appear to largely dominate the scene. We are increasingly able to recognize several processes occurring in the underground volcanic system through more sensible multiparametric instrumentation and sophisticated analyses and models (see Saccorotti et al., 2015; Chapters 2–4 for seismic, geodetic, and geochemical monitoring, respectively; and Chapters 9 and 10 for real case applications to mafic volcanoes). Thanks to our monitoring systems, and in many cases even with just basic instrumentation, reawakening of potentially dangerous volcanoes is usually recognized with large anticipation, and risk mitigation measures are successfully put in place saving countless lives (e.g., McCausland et al., 2019). Still, high confidence that a point of no return has been passed is often confined to the last few hours or even tens of minutes before the eruption; and forecasts of the size of the impending eruption are extremely difficult, if possible at all (see below). Again, a parallel with the atmospheric sciences is useful: with similar level of complexity characterizing the physical processes, but overwhelmingly higher quantity and quality of data from direct observations, weather forecasts are today accurate to only an order of days. In spite of the large uncertainties characterizing volcanic processes, aposteriori evaluations of the abundant signals preceding an eruption often seem to show that the eruption could be, and should have been, anticipated with more than reasonable confidence. The famous psychologist and Nobel prize-winner Daniel Kahneman describes the attitude of seemingly finding notable predictive elements a posteriori (i.e., after the event has happened) as a cognitive bias that he called the “hindsight bias” (Kahneman, 2011). This is the typical bias that affects us for highly nonlinear, inherently unpredictable processes. As human beings, we strive to make predictions: living in an unpredictable world is highly

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uncomfortable, as it makes us feel defenseless, and at the mercy of events. We constantly scan the world around us in search of cause-effect relationships, and thanks to this powerful resource deeply rooted in us, we have ultimately evolved as the dominant species on Earth. We instinctively refuse the idea of deterministic unpredictability and feel uncomfortable with uncertainties, let alone accepting inherent uncertainties that cannot be eliminated, not even in principle. That strong impulse to find the causes behind the events can bias our minds and lead us to believe that we do see the obvious chain of processes and relationships unavoidably leading to one specific outcome, once we know that outcome. It is worth stressing here that the existence and relevance of uncertainties does not imply that our efforts in scientific understanding are useless. On the contrary, that is the only way to further penetrate the complexities of volcanoes and volcanic processes, improve our hazard forecasts, and better contribute to mitigating and managing volcanic crises and their associated risks. Continuing with the parallel, if atmospheric scientists gave up after realizing the highly nonlinear nature of atmospheric dynamics and the limits of their forecasts, we would not have today the advanced capabilities that we know and from which we benefit (cfr. Bauer et al., 2015), with detrimental or dramatic consequences on endless aspects of our life. As for weather forecasts, and even more for volcanic eruptions, we should progress in our science and at the same time accept that uncertainties are an integral part of the problem. As a matter of fact, forecasting volcanic eruptions equally involves reducing the uncertainties and properly treating them. Uncertainties can be reduced (the epistemic component) but they cannot be eliminated (the aleatoric component). They are a fundamental constituent of volcanic hazard forecasts, and they must be dealt with most transparently and effectively.

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Volcanic alert levels at high-risk volcanoes

The issue of managing volcanic risks largely depends on the magnitude of risk. Relatively low-risk situations can be managed efficiently by employing a precautionary approach, because the benefits from quick action usually more than overcome the costs associated with the action itself (for cost/benefit analysis in volcanic risk, see Marzocchi and Woo, 2007; Marzocchi et al., 2012; Woo, 2015). One clear example is the 2015 volcanic crisis at Mount Shindake, Kuchinoerabu Island, Southern Japan, where the entire population living in the island, amounting to about 140 people, was preventively evacuated soon after the volcano showed signs of possible reactivation, then culminating in a phreatomagmatic eruption (Geshi and Itoh, 2018). The conditions would be radically different in case of a similar crisis at Campi Flegrei in Southern Italy, or at the Auckland Volcanic Field in New Zealand, or at other volcanoes around the world which menace highly urbanized, industrialized areas. In all such cases, actions undertaken during the emergency should be evaluated against their potentially detrimental consequences. A critical case is that of an alarm

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in a large city not followed by any eruption, and determining by itself substantial economic, social, and political costs. Volcanic emergency plans for such situations search for a balance between not too early evacuation call so to minimize the occurrence of a false alarm, and not too late call so to ensure completion of the evacuation operations before the eruption starts. The current volcanic emergency plan for Campi Flegrei of the Italian National Civil Protection foresees an evacuation call three days in advance before an eruption (http://www. protezionecivile.gov.it/attivita-rischi/rischio-vulcanico/vulcani-italia/flegrei/ piano-nazionale-di-protezione-civile). Such a time balance represents a big challenge, as it requires quite accurate forecasting capabilities. In fact, while many signs of a possible new eruption at Campi Flegrei would likely be recognized well in advance, the final escalation toward the eruption may be seen on a shorter time frame. The complex set up of highly industrialized, heavily urbanized areas poses additional challenges, requiring actions largely distributed over times much longer than the relatively short time of the evacuation. In fact, suddenly abandoning a city without sufficient preparation, for example, by progressively decreasing the reserves of flammable or dangerous materials, completing the shutdown procedures for industrial operations, displacing valuable items, etc., could be itself cause of economic disruption, losses, and disasters that may potentially overcome those from a small-medium eruption, if an eruption should then occur. In order to deal with the unrest phases of a reawakening volcano, volcano observatories adopt strategies that include reinforcement of monitoring networks, and of data processing and numerical modeling capabilities; frequent meetings among the observatory personnel and other scientists from partner Universities and Institutions; meetings with representatives from civil authorities and decision-makers; and set up and implementation of proper communication plans. Preparation is essential, as the pressure from the media and the society during the crisis can be substantial and adversely affect the efficacy of the response. An approach for supporting operations during volcanic crises is that of adopting a checklist (see Chapter 13), similar to checklists that have been in use since long by airplane pilots to ensure that the correct sequence of necessary operations is always put in place, regardless of the stress conditions under which the pilot is operating. Checklists can include virtually everything, from practical actions for daily implementations to more advanced elements involving data management and processing, interactions with stakeholders, etc. However, they should be kept as straight forward as possible and actions delegated (Newhall et al., this volume). The advantages of adopting a checklist approach start with the initial efforts that are required for their compilation. This is done by scientists projecting themselves into a hypothetical volcanic crisis and reviewing necessary implementations, elements that may potentially undermine the observatory response, and the most effective countermeasures. In this manner, scientists at volcano observatories can better prioritize their activities, identify weaknesses, consider unexpected outcomes, establish their chain of

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activities as well as their communication strategies, evaluate multiple evolutions and the required actions thereby, and ultimately maximize the effectiveness of their response during the crisis. The primary means of communication by volcano observatories about the state of the volcano and its possible evolutions are to-date constituted by the volcanic alert levels. Volcanic emergency plans commonly adopt a volcanic alert level system (VALS) whereby the state of the volcano is discretized in a number of levels associated with colors, usually referred to as “background” or normal (green), “advisory” or above background (yellow), “watch” or escalation of signals (orange), and “warning” or eruption imminent/in progress (red) (more elaborated versions with a larger number of levels are also used). Such a set up may support decision-makers in the implementation of a plan involving progressive actions (e.g., when the alert level equals X, decrease the reserves of flammable materials, the production of dangerous chemicals, the activity of blast furnaces, etc., to prescribed levels; decrease the air/ground traffic; decrease or stop the flux of tourists, etc., up to complete stop in these activities at the highest alert level). To be effective, similar actions should be defined in advance, and linked to the VALS. I have largely discussed elsewhere (Papale, 2017) that even when not clearly stated or established, actions by decisionmakers, as well as consequences for the society, inevitably stem from alert-level changes (examples include reduction of closure of touristic activities close to the volcanic area, closure of schools, commercial activities in exposed areas, etc.). All of these actions have economic and social impacts, including loss of income, depreciation of properties, speculation, etc. A VALS discretizes the state of the volcano with the declared purpose to “alert”; it is therefore obvious, and desired, that such an alert is accompanied by decisions and actions aimed at increasing safety and mitigating risk. However, volcanic processes are highly uncertain and not necessarily evolving through a sequence of discrete steps or jumps in their status. Accordingly, VALSs attempt to account for the overwhelmingly difficulty of predicting the evolutions leading to an eruption by mostly referring to qualitative observations, for example, by making use of terms like “large,” “rapid,” “increasing,” etc. when describing the criteria for alert level changes. In that way, evaluating if the observed changes are “large” enough, “rapid” enough, etc. to justify an alert level change is left to expert judgment by volcanologists during the emergency. Together with the strict linkage between alert levels and economic and social impacts, that discretionality often results in a sense of inadequacy and distress by volcanologists (Fearnley, 2013), who feel involved in evaluations (albeit often in the form of recommendations to civil authorities) that immediately translate into decisions for the society transcending their roles and expertise. I think at least part of the confusion leading to different opinions about the use of VALSs is due to their unclear definition. Defenders of present VALSs sometimes refer to such levels as a discrete description of the status of the

8 Forecasting and planning for volcanic hazards, risks, and disasters

volcano, separate to an extent from the alert component, rather rooted in purely scientific evaluations. I do not think that view is justified: alert levels clearly serve to put alerts, and that is in fact what they are used for. On the contrary, trying to discretize the continuous evolution of a volcano looks useless if not for civil defense purposes. If we accept, as it looks obvious to me, that the definition of alert levels is entirely motivated by the need to implement emergency actions for risk mitigation, then it should be clear that defining a VALS is supported by scientific knowledge but it is not a scientific endeavor itself. I have proposed elsewhere (Papale, 2017) a different approach to VALSs, based on quantitative evaluation of the uncertainties and full exploitation of diverse expertise in a collaborative effort which respects the different roles of the involved actors (see also Chapter 14). In the following I summarize and develop further that approach. A discretization of the state of the volcano is useful to establish when actions should be taken, and calibrate those actions with reference to the costs they involve (economic, social, etc.) and the benefits they bring (reduction of risk). Knowledge of the volcano, forecasts on short-term evolutions, and hazard maps should be provided in a form that is mostly useful to other experts and decision-makers, without leading volcanologists, explicitly or implicitly, outside the boundaries of their expertise and technical competence. Fig. 1 shows

FIG. 1 A rational approach to volcanic alert level system. The thin colored lines show the temporal evolution of the computed probability corresponding to different eruption states (computations from Selva et al., 2012a). The example comes from a-posteriori analysis of the 1982–84 crisis at Campi Flegrei. Uncertainties on those probabilities are not reported for simplicity. The blue lines (probability equal to 1 during most of the time frame in the figure) is the probability that the observed state is above background (“unrest”). The green line is the probability that the observed unrest relates to magma transfer dynamics (“magmatic”). The red line is the probability of occurrence of an eruption within a given temporal window, 1 month in this case (“eruption”). Volcanic hazard forecasts constitute an input for additional evaluations from other experts. The black arrows exemplify the flow of information at any moment before, during and after a volcanic crisis. The thick, colored horizontal lines, in colors typical of alert levels, identify probability thresholds (here purely hypothetical), defined by decision-makers, which prompt risk mitigating actions. Such lines correspond to volcanic alert levels as they are proposed here.

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the proposed approach. The computations in the figure come from Selva et al. (2012a), and show the temporal evolution of the probability (in this example, retrospectively evaluated) corresponding to different states and possible short-term evolutions of the Campi Flegrei volcano during its crisis in 1982–84. Estimating such probabilities is a purely scientific endeavor, motivated by risk mitigation but not involving, neither explicitly nor implicitly, any timing for decisions, and even less any decision, about mitigating actions. Probability distributions are in the form most suitable to be treated quantitatively within subsequent evaluations by other experts, including economists, engineers, social scientists, etc. who translate the hazards (probabilities) by volcano scientists into evaluations of the impacts. Diverse and complementary expertise should be exploited and exalted while keeping the experts focused on their own field of knowledge (cfr. Marzocchi et al., this volume). Such a complementary approach would also allow an evaluation of the impacts of specific mitigating actions, providing risk experts with the necessary inputs for cost/benefit analyses in relation to any action at any specific time during the crisis. Decisions are made by political decision-makers, supported by the large pool of multidisciplinary experts, in agreement with their societal roles and mandates. The levels at which specific actions are triggered are indicated for simplicity as probability thresholds in Fig. 1. Those levels constitute the rational VALS proposed here, which is this time established by decision-makers, it acknowledges the vast spectrum of expertise required for managing volcanic risks, and does not force any actor outside the boundaries of their roles, expertise, technical competence, and societal mandate. Rather, it best exploits the variety of roles and technical competences in a collective effort in support of effective risk mitigation and robust decision-making. It is worth stressing that an approach to VALS like the one described above and in Fig. 1 is more beneficial for high volcanic risk situations. In low-risk situations, or more generally, for situations corresponding to low cost/benefit ratio (which include most recommendations for air traffic operations close to active volcanoes, as discussed in Papale, 2017), simpler approaches can still work, as the decision would be, anyhow, that of implementing low-cost actions bringing high benefits (e.g., complete evacuation of the Kuchinoerabu Island in Southern Japan as in the example at the beginning of this section; airplane detour to avoid volcanic ash; etc.). On the contrary, in high-risk situations where any action brings about substantial costs, making decisions without properly considering those costs may have dramatic consequences. There are at least three noteworthy aspects that clearly distinguish a VALS like the one exemplified in Fig. 1 and described above, and current VALSs in use at most volcano observatories worldwide. The first one is that this VALS is quantitative, and thus it eliminates vague or ambiguous definitions in current VALSs. The second relevant aspect, which involves volcanologists, other experts, and the decision-makers, is that a VALS like the one described here allows decisions based on cost/benefit analysis, and can be scrutinized against

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objective, quantitative evaluations. This is a warranty for the population at risk, as any decision is demonstrably made so to maximize their benefits, and at the same time, it helps when scientists and decision-makers dealing with natural risks are increasingly asked for accountability (e.g., Marzocchi, 2012; Bretton and Aspinall, 2017). Finally, the third relevant aspect, and possibly the one with more profound implications, is that a VALS like this one rules out the concept of a false alarm as it is commonly perceived: there would be no false alarms, only rational decisions based on objective, quantified, auditable risk evaluations that fully and transparently account for the uncertainties characterizing the volcanic processes and, ultimately, their impacts. If established before a crisis (as it should be), and depending on agreements with decisionmakers, the VALS described in Fig. 1 would allow quick and efficient communication of current alert levels directly from volcano observatories based on their expert evaluation of current probabilities, without implying any decision or responsibility by volcano scientists besides those that are solely and directly related to their scientific expertise.

4 Forecasting eruption size at reawakening volcanoes The eruption magnitude is one of the most relevant quantities directly determining the hazard and the impacts of a volcanic eruption. Eruption magnitude is a measure of the amount of magma discharged during an eruption, and it is usually adopted to classify the eruptions in terms of their size (Newhall and Self, 1982; Mason et al., 2004; Crosweller et al., 2012; Pyle, 2015). There are two scales of magnitude that are most commonly employed: the VEI scale (Newhall and Self, 1982) groups volcanic eruptions into nine discrete classes from 0 to 8, and it refers, with some exception for the lowest classes, to the bulk volume of the volcanic products. At least for the explosive VEI classes 4–8, the VEI corresponds to the integer part of the logarithm to base 10 of the volume of the discharged magma expressed in cubic meters, decreased by 4. The M scale (or more simply, the Magnitude) is instead a continuous one, and corresponds to the logarithm to base 10 of the mass of the discharged magma expressed in kilograms, decreased by 7 (Pyle, 2015). While there are good reasons to use or criticize one or the other (e.g., Deligne et al., 2010; Newhall et al., 2018; Papale, 2018), the two scales generally provide comparable measures of the size of an eruption, as it should be expected that a VEI X eruption has a value of M between X and X + 1 (Deligne et al., 2010). In practice, however, uncertainties in eruption volume estimates and in average densities to convert from volume to mass result in poorly consistent attributions of VEI and M in a number of cases (e.g., see the relevant LaMEVE database of eruptions with M4 +: https://www.bgs.ac.uk/vogripa/view/controller.cfc?method¼lameve, described in Crosweller et al., 2012). Forecasting the size of a next eruption at closed system volcanoes undergoing unrest has revealed quite difficult. The rareness of large explosive eruptions

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is an obvious limit to the possibility of generalizing the observations: during modern volcano monitoring times we have observed only one VEI 6 eruption (at Pin˜atubo, Philippines, in 1991), and only four VEI 5 eruptions (data source: Global Volcanism Program, https://volcano.si.edu/, accessed on February 26, 2020). In fact, the relative frequency of dominantly explosive eruptions (VEI 3–8) over the global scale distributes as a power law (Newhall et al., 2018; Papale, 2018). In a sense, that may facilitate forecasts: given a long-dormant volcano undergoing unrest, even without knowing anything about its previous eruptions and the current dynamics, one may safely assert that in case of eruption, the probability of VEI 3 is roughly 10 times higher than that of VEI 4, that the latter is roughly 10 times higher than VEI 5, etc.; and that there is a > 99% probability that in case of eruption, the eruptive scale will be VEI 4 or less (Papale, 2018). As a matter of fact, that might represent a robust starting point––a “base-rate”––over which more specific information can be added, for example, from the size distribution of known previous eruptions from the volcano, or from other data or models that may add significant specific information. That would help avoid a well-known cognitive bias that psychologists call “base-rate fallacy” (Kahneman, 2011), which describes the tendency of people to ignore or undervalue general information in favor of less informative but more appealing specific information on the case under consideration. Forecasts should be consistent with the geological knowledge and the past history of the volcano, but also consider that in most (if not all) cases, the reconstructed history may be poorly representative of the possible spectrum of eruptions and eruptive conditions. Enlarging that spectrum through consideration of similar or “analog” volcanoes from all over the world (e.g., as in Wright et al., 2019; see also Tierz et al., 2019) is therefore good practice, and it should be seen as increasing the robustness of forecasts. A recent comprehensive analysis of preeruption seismicity has been presented in White and McCausland (2019). They have reviewed the information available from 36 cases coming from 26 different dormant (repose time >20 years) volcanoes, highlighting quite interesting repetitive patterns that deserve consideration. In particular, they show that when distal (2–30 km) VT seismicity, preceding nearly all of the considered eruptions, is characterized by very large, very brief energy release, then quickly a “large” explosive eruption follows, as opposed to small energy release or energy release spread over 1 year or more, followed by passive dome extrusion. Similarly, a persistent, long-lasting character of what they call “repetitive seismicity” characterizing the final stages of magma ascent is associated with dome extrusion, whereas nonpersistent repetitive seismicity lasting for only minutes is found to precede explosive eruptions. Although mostly limited to gross separation between explosive and dome-forming eruptions (at least partly due to limited range of instrumentally observed explosive eruptions), the above forms a basis for further tests at re-awakening volcanoes. The same authors discuss similar elements that seem to apply, to some extent, also for frequently erupting volcanoes when

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the volcanic conduit is partly obstructed; and describe a linear relationship, in a log-log plot, between the cumulative seismic moment of distal VT seismicity and the intruded volume estimated from nonseismic methods, coinciding with a similar relationship involving induced seismicity and injected fluid volume at deep wells. While the above elements are encouraging, the forecasts they allow mostly concern gross separations between explosive (Plinian-like) and dome-forming eruptions (with or without some additional qualitative characterization). However, explosive eruptions can differ by many orders of magnitude and be associated with similarly orders of magnitude different impacts. As a consequence, forecasting the size of an impending eruption is of tremendous relevance for risk mitigation. Unfortunately, that seems to be quite a hard task. In fact, no clear relationship has emerged to link preeruptive observations and signals to the scale of an explosive eruption. On one side this may be due, at least in part, to too little sample size (too few instrumentally observed eruptions of larger size) preventing meaningful comparison between observations related to different eruption scales. On the other side, power-law-distributed phenomena in nature are intimately linked to an intrinsic unpredictability concerning their size: power-law distributions appear to emerge in systems governed by nonlinear processes with many degrees of freedom, giving rise to self-similar, scale-invariant phenomena (Bak et al., 1988; Clauset et al., 2009; Markovi
c and Gros, 2014). Explosive volcanic eruptions display all of the necessary ingredients (Papale, 2018): they are governed by highly nonlinear dynamics in systems with many degrees of freedom; their relative frequencies distribute as a power law; and they are self-similar and scale invariant: a relatively small explosive eruption is indistinguishable from a large one for practically any respect and over several orders of magnitude in size, except that it is a scaled-down version of the latter. While too few observations to-date on medium-large explosive eruptions prevent strong conclusions, the existence of an inherent limit to estimating the size, thus the impacts, of a next volcanic eruption from unrest observations would have profound implications for shortterm volcanic forecasts, and it is worth of serious consideration by volcanologists as well as by civil defense officials.

5 Forecasting the impacted areas Forecasts of areas subject to volcanic hazards are commonly based on past eruptions from the considered volcano and their reconstructed impacts. An approach that has been largely adopted by volcanologists consists in referring to what has been called “the maximum expected event” (e.g., Barberi et al., 1992; Cioni et al., 2003) or similarly, but less straightforward, “the most probable maximum expected event” (e.g., Orsi et al., 2004), extracted from the past eruptive history of the volcano and usually coinciding with the largest eruption known, or with some large eruption below the largest one. Accordingly, many volcanic hazard

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maps are constructed with reference to the areas that in the past have been reached by hazardous phenomena from the event assumed as the maximum expected. Mapping the hazards from past eruptions is, quite obviously, critical information and a reasonable starting point for medium-long-term volcanic hazard forecasts. There are, however, substantial limits in relying on just the past volcanic history. The concept of selecting a maximum from the past history of the volcano seems to embed the idea that for each volcano there is a maximum in eruption size that has occurred already; or in some unstructured and usually unquantified way, that there is some eruption size threshold corresponding to already observed events, above which the likelihood is so small that is not worth being concerned about it. The former is clearly unreasonable: that we have observed already the largest-ever eruption at any individual volcano is not justified by any evidence or reasoning, and a look at volcanic eruption databases shows that the largest known eruption at a given volcano can happen at any stage of the volcanic history (that is also true for VEI 8 super-eruptions: about half of them are first events of that size for the corresponding volcano, so, we may expect that a next super-eruption has roughly 50% of probability to occur at a volcano that did not display similar extreme events previously). The latter–– the neglect by volcanologists of events that look too rare to be a concern–– implies a decision regarding the acceptable risk, which is clearly not a decision that volcanologists should assign to themselves. In any case, similar evaluations should consider the risks associated with hazardous scenarios, not just the hazards; for example, the probability of core melting at a nuclear reactor is usually very low, but because the associated risk is very high, no reactor could be in operation with a core melting probability higher than one out of one million, or 106 (IAEA-TECDOC Report 1332, available at www-pub.iaea.org/ MTCD/Publications/PDF/te_1332_web.pdf). In other words, probability thresholds can be established (with specific societal mandate) after consideration and with reference to the associated risks. There are several elements that contribute to determining decisions on risk mitigation strategies; emergency plans may be scaled depending on the circumstance of the situation (e.g., a volcano that may impact an industrial area and trigger a cascading series of adverse events; a volcano in a country exposed to other severe risks, for example, from war, famine, disease, etc.; a crisis during the summer season potentially involving tourists; etc.), or with reference to actions that can be effectively implemented based on cultural, sociological, logistical, and other constraints. All of those elements, and many others that can deeply affect decision making, are largely independent from hazard evaluations. Deciding a reference maximum event, therefore excluding events above a certain size or below a given probability, is definitely not a matter of scientific knowledge on the volcano; accordingly, in their hazard evaluations volcanologists should consider the full spectrum of possible events with their associated probabilities, and transparently communicate them (Pallister et al., 2019).

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The maximum expected event approach (or similar) is rapidly giving away to more rational evaluations of the probabilities associated with different eruption scales, styles, and phenomena. Modern approaches to volcanic hazard zonation take into account the probabilities and their uncertainties in many relevant elements characterizing a next eruption, including vent location and conditions (e.g., Selva et al., 2012b, 2018; Bevilacqua et al., 2015; Tadini et al., 2017a,b; Rutarindwa et al., 2019), properties of the discharged materials (e.g., Sandri et al., 2016; Costa et al., 2009; Folch et al., 2016), expected phenomena and their magnitudes (e.g., Selva et al., 2010; Neri et al., 2008, 2015a,b; Newhall and Pallister, 2015; Bevilacqua et al., 2017; Paris et al., 2019), meteorological conditions (Witham et al., 2007; Macedonio et al., 2008; Bonasia et al., 2011; Folch et al., 2012; Bonadonna et al., 2012; Scollo et al., 2013, 2019; Watson et al., 2017; Martı` et al., 2017; Toulkeridis and Zach, 2017; Poulidis et al., 2018), and many others; and employ either highly sophisticated, computational demanding numerical approaches to explore specific scenarios with advanced detail (e.g., Todesco et al., 2002; Esposti Ongaro et al., 2008), or simpler numerical codes that can be run extensively to obtain probabilistic hazard maps (e.g., Favalli et al., 2009, 2012; Harris et al., 2011; Neri et al., 2015a; Takarada, 2017; Bevilacqua et al., 2017; see also Chapter 15) (for approaches to pyroclastic flow simulations with various level of sophistication, see also Neri et al., 2015b). In all cases, such approaches are guided, complemented or constrained by reconstructions of the deposits and of the characteristics of magmas from past eruptions at the volcano under inspection (e.g., Chapter 7).

6 Best practices Subaerial volcanoes are widespread over the globe: most of them concentrate along convergent plate boundaries, and most of these volcano types distribute according to the so-called “ring of fire” along the American and Asian borders of the Pacific Ocean, while others mark other convergent regions, for example, the Mediterranean region. Many other volcanoes are associated with divergent plates boundaries, like in Iceland or along the African Great Rift Valley. Finally, intraplate volcanoes are located far from plate boundaries, in correspondence of large mantle plumes like at Hawaii, the Azores, Galapagos, Yellowstone, Cameroon, and many others. Active volcanoes are largely widespread, consequently, they are located in countries with significantly different cultural, social, economic, and political situations. These differences can result in equally different approaches to volcanic hazard evaluations, procedures for managing the scientific and logistical aspects of volcanic crises, and roles and responsibilities of scientists with respect to society. Such differences constitute a significant limit to the possibility of identifying general best practices to deal with volcanic hazards and risks. As an example, it seems likely to me that a VALS like the one described above will be less attractive in countries with little

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resources for volcano science and volcanic surveillance, or where scientists are explicitly asked to take part to decision making for civil defense implementations; while the same VALS may raise interest in developed countries with articulated set ups including more structured roles and responsibilities. In spite of the above limitations, the success of the international workshop series VOBP––Volcano Observatory Best Practices (Pallister et al., 2019), which started in 2011 and it is currently approaching its 5th edition, and the dedicated participation of representatives from practically every volcano observatory from all over the world, demonstrates an overwhelming desire of shared references for common developments and progress in managing volcanic crises. An exhaustive description of best practices emerging from the collaborative work of volcano observatories gathering at VOBP workshops is presented in Pallister et al. (2019), and it would be impossible to be satisfactorily summarized here. However, a number of common elements emerge in recommended best practices for hazard forecasts as well as for communication between volcano observatories and stakeholders. Below I extract a few of those elements that are useful for the present discussion. The interested reader is addressed to the original paper, referenced above, for a more complete description of best practices emerging from VOBP workshops. Probabilistic forecasts are essential components of both long-term and shortterm volcanic hazard evaluations, and a most effective means of communication. Such forecasts, typically obtained through approaches like Bayesian Event Trees (e.g., Newhall and Hoblitt, 2002; Marzocchi et al., 2012; Selva et al., 2012a; Garcia-Aristizabal et al., 2013; Christophersen et al., 2018; and many others), should be accompanied by careful consideration of their uncertainties, and communicating those uncertainties is as important as communicating the forecasts. Stakeholders should be given complete information including low or very low probability events. Volcanologists cooperate at any level and any moment with all other components of risk management, with roles that are bounded by their specific expertise and societal mandate. While acknowledging the different societal and cultural contexts of volcano observatories worldwide, clear identification of separate roles and responsibilities for scientists and decision-makers is a highly felt community need, and one that is recognized as an essential component of a clear, smooth, efficient, and transparent risk management organization and set up. I remark here that such recommendations are in full agreement with the contents in this chapter, and particularly with the VALS described above. Such a VALS exploits the roles and expertise of any actor involved (volcanologists and other experts, like economists, engineers, social scientists, etc., as well as society officers and political decision-makers) in a cooperative effort which ensures distinct roles and responsibilities deriving from expertise and societal mandate. While such a VALS is proposed here as a reference model, I stress that the intent is not that or criticizing existing VALSs or recommending their abandonment. Many risky situations in various countries have been dealt with more than

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satisfactorily with the existing VALS, and local officers as well as country governments are used to them and might lose clear references, possibly undermining the efficiency of their action, if too rapid changes would happen. As for best practices from VOBP workshops, the aim is definitely not that of criticizing, and less than ever, that of imposing changes. Rather, the aim is that of contributing to the identification of developments, mostly based on experience and common sense that can be taken as healthy for further implementations, bringing benefits ultimately to the population at risk.

7 Global volcanic hazards I started this chapter by noting that about 800 million people are exposed to volcanic hazards. Although that sounds huge, volcanoes are such that their associated risks virtually extend to the entire population on Earth. In fact, the above estimate accounts only partially for the consequences of volcanic supereruptions, the impacts of which may be considerable over areas as vast as an entire hemisphere (Sparks et al., 2005). Volcanic super-eruptions, together with the even more impacting but far rarer flood basalt eruptions (White and McKenzie, 1989), are the only known endogenous events with consequences that are so vast that they can arguably menace the human civilization as we know it today (Sparks et al., 2005). While the debate is still ongoing on the effective extent of the impacts from volcanic super-eruptions (see Self, 2015, for a critical review), there is no doubt that modern humans of the globally interconnected society would profoundly suffer from such an occurrence. We are, with reason, developing a number of options to avoid the impact from a large asteroid and save the Earth from a global disaster. However, in the case of volcanic super-eruptions we are entirely helpless, and besides largely speculative suggestions (e.g., Deckenberger and Blair, 2018) we have no real clues on how to control the gigantic power released during such extreme events (Sparks et al., 2005). For a long time we have been spared by the relative rareness of supereruptions. We know only four of them in the last 100,000 years, and the last one occurred (in New Zealand) about 26,000 years ago. On a first sight, such numbers appear to refer to hazards that are far beyond the time scale we are used to worry about. Unfortunately, that is not the case, for two reasons that I discuss below. First, the annual probability of occurrence of a volcanic super-eruption somewhere in the world is not small, being one out of one hundred thousand (10–5) (Papale and Marzocchi, 2019) (Fig. 2). To have an idea of the meaning of such a probability, individual nuclear reactors must demonstrably be associated with an annual probability of core melting at least 10 times lower than that (IAEA-TECDOC Report 1332, available at www-pub.iaea.org/MTCD/ Publications/PDF/te_1332_web.pdf; note however that this is reported only as a reference, while no direct comparison can be done between global and

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FIG. 2 Probability of occurrence at the global scale of eruptions with different size. Solid symbols and lines refer to mean values. Open symbols and dashed lines (nearly superimposed to solid symbols) refer to one-sigma variations. Calculations are made from exponential rate parameters in Papale (2018). (Redrawn after Papale, P., Marzocchi, W., 2019. Volcanic threats to global society. Science 363, 1275–1276.)

individual probabilities). By comparison, the annual probability of impact by a large (order 1 km) celestial body with consequences comparable to those from a volcanic super-eruption is one out of one million (106) (https://sservi.nasa. gov/wp-content/uploads/2014/02/NEO-for-AGC-Seminar-Feb-2014.pdf); and the annual probability of commercial airplane crashing accident is only one out of 10 million (107) (https://www.boeing.com/resources/boeingdotcom/ company/about_bca/pdf/statsum.pdf), 100 times smaller than that of a volcanic super-eruption. Still, many people are afraid of flying. If we refer to the length of an individual human life, each of us has one out of one thousand (103) chances to experience a volcanic super-eruption (Papale and Marzocchi, 2019), just a little less than the life-time odds of dying in a traffic accident in the United States (https://www.iii.org/fact-statistic/facts-statistics-mortality-risk). You should take about 10,000 flights in your life for the probability of a fatal accident to become comparable to that of experiencing a volcanic supereruption. The second reason why we should worry about volcanic super-eruptions comes from the fact that what really matters is not the hazard (probability of occurrence), but the risk from that hazard, emerging by a combination of the probability of occurrence and the consequences of the event. In other words, we are worried because of the detrimental consequences of a negative event,

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not because of the event itself. Accordingly, multirisk evaluations express different risks through common metrics to compare them and allow decisions to be made on how to best use the necessarily limited resources available for risk mitigation (Thierry et al., 2008; Selva, 2013; Sandri et al., 2014; Scaini et al., 2014). Risk evaluations can refer to individual risk (the risk for an individual person) or more often to societal risk, that is, the risk measured with reference to a significant group (e.g., a city, an area, a nation, a region of the world, or the human population as a whole). Volcanic super-eruptions put the entire world under the risk of a crisis so profound that it may undermine the framework of our current civilization. Besides devastation over an area as large as Europe or the United States, the possible consequences of a volcanic super-eruption may include climate forcing and volcanic winter extending over a hemisphere and lasting for years (Rampino and Self, 1992; Ambrose, 1998; Rampino and Ambrose, 2000), with destruction of global food resources, economic disruption, and large scale political instability, possibly triggering decades to centuries long global cooling (Robock, 1979; Jungclaus et al., 2010; Miller et al., 2012; Newhall et al., 2018). Although a quantification of the global risks from a volcanic super-eruption is not available yet, we know that the associated losses could be extreme. The risk associated with a nuclear accident somewhere in the world is deemed as not acceptable. We have experienced serious nuclear accidents, and we know their devastating consequences. The consequences of a volcanic super-eruption, although never experienced in historical times, would be global and overwhelmingly negative for our deeply interconnected, fragile society. As a matter of fact, volcanic super-eruptions are the most concrete source of global risk from natural phenomena on Earth. We humans may simply avoid considering them: after all, we do not know how to avoid, stop or limit them; and even if we would have the capability one day in the future, the day in which we could engineer a volcanic system and avoid or mollify a super-eruption is far away. In addition, volcanoes throughout the world with the potential of producing a super-eruption are too many and not necessarily all known in advance. We should certainly improve our capability to forecast the potential occurrence of a super-eruption, and progress in modeling their evolution and impacts. Even most importantly, I believe we should develop an ability to mitigate their consequences and increase our resilience. As a global society we should start developing resilience plans from volcanic super-eruptions, whereby food resources are guaranteed, economic disruption is contained, and political disorder and instabilities are controlled. It is not an easy task, and it may take long time, huge efforts and considerable resources before a similar global plan is conceived, produced, evaluated, and tested. On the other hand, the risk is that of wreaking havoc upon humanity, turning back the clock of civilization to dark ages. Sooner or later a volcanic super-eruption will occur somewhere on Earth, and we should ask ourselves if we are fatalistically waiting for it, or if, instead, we are getting prepared.

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Acknowledgments This chapter summarizes much of the experience gained in many years during which I was responsible of research at the national level aimed at strengthening the scientific response to volcanic crises, and had international roles and responsibilities that led me to co-organize several initiatives aimed at reinforcing the capabilities of volcano observatories and research centers worldwide in dealing with the scientific management of volcanic crises. I owe much to very many colleagues, from Italy and abroad, for the views and ideas that I developed and collected here. Acknowledging here the many important people is not possible, but I cannot help citing at least three of them: Chris Newhall, John Pallister, and Warner Marzocchi. We do not necessarily always agree, but a discussion with them is always a source of new wonders, a stimulus to look from other perspectives, and a trigger of new ideas. They also contributed to this chapter with their thorough and generous comments and suggestions, which are a reflection of their brilliant mind and exceptional experience.

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20 Forecasting and planning for volcanic hazards, risks, and disasters Cioni, R., Longo, A., Macedonio, G., Santacroce, R., Sbrana, A., Sulpizio, R., Andronico, D., 2003. Assessing pyroclastic fall hazard through field data and numerical simulations: example from Vesuvius. J. Geophys. Res. Solid Earth 108, 2063. https://doi.org/10.1029/2001JB000642. Clauset, A., Shalizi, C.R., Newman, M.E.J., 2009. Power-law distributions in empirical data. SIAM Rev. 51, 661–703. Costa, A., Dell’Erba, F., Di Vito, M.A., Isaia, R., Macedonio, G., Orsi, G., Pfeiffer, T., 2009. Tephra fallout hazard assessment at the Campi Flegrei caldera (Italy). Bull. Volcanol. 71, 259–273. Crosweller, H.S., et al., 2012. Global database on large magnitude explosive volcanic eruptions (LaMEVE). J. Appl. Volcanol. 1, https://doi.org/10.1186/2191-5040-1-4. Deckenberger, D.C., Blair Jr., R.W., 2018. Interventions that may prevent or mollify supervolcanic eruptions. Futures 102, 51–62. Deligne, N.I., Coles, S.G., Sparks, R.S.J., 2010. Recurrence rates of large explosive volcanic eruptions. J. Geophys. Res. 115, https://doi.org/10.1029/2009JB006554. Doyle, E.E.H., McClure, J., Paton, D., Johnston, D.M., 2014. Uncertainty and decision making: volcanic crisis scenarios. Int. J. Disaster Risk Reduct. 10 (A), 1609–1632. Esposti Ongaro, T., Neri, A., menconi, G., de’Michieli Vitturi, M., Marianelli, P., Cavazzoni, C., Erbacci, G., Baxter, P.J., 2008. Transient 3D numerical simulations of column collapse and pyroclastic density current scenarios at Vesuvius. J. Volcanol. Geotherm. Res. 178, 378–396. Favalli, M., Chirico, G.D., Papale, P., Pareschi, M.T., Boschi, E., 2009. Lava flow hazard at Nyiragongo volcano, D.R.C. 1. Model calibration and hazard mapping. Bull. Volcanol. 71, 363–374. Favalli, M., Tarquini, S., Papale, P., Fornaciai, A., Boschi, E., 2012. Lava flow hazard and risk at Mt. Cameroon volcano. Bull. Volcanol. 74, 423–439. Fearnley, C., 2013. Assigning a volcano alert level: negotiating uncertainty, risk, and complexity in decision-making processes. Environ. Plan. A: Econ. Space 45, 1891–1911. Folch, A., Costa, A., Basart, S., 2012. Validation of the FALL3D ash dispersion model using observations of the 2010 Eyjafjallajokull volcanic ash clouds. Atmos. Environ. 48, 165–183. Folch, A., Costa, A., Macedonio, G., 2016. FPLUME-1.0: an integral volcanic plume model accounting for ash aggregation. Geosci. Model Dev. 9, 431–450. Fujii, Y., Satake, K., Sakai, S., Shinohara, M., Kanazawa, T., 2011. Tsunami source of the 2011 off the Pacific coast of Tohoku earthquake. Earth Planets Space 63, 815–820. Garcia-Aristizabal, A., Selva, J., Fujita, E., 2013. Integration of stochastic models for long-term eruption forecasting into a Bayesian event tree scheme: a basis method to estimate the probability of volcanic unrest. Bull. Volcanol. 75, 689. Geshi, N., Itoh, J., 2018. Pyroclastic density currents associated with the 2015 phreatomagmatic eruption of the Kuchinoerabujima volcano. Earth Planets Space. 70, 119. Harris, A.J., Favalli, M., Wright, R., Garbeil, H., 2011. Hazard assessment at Mount Etna using a hybrid lava flow inundation model and satellite-based land classification. Nat. Hazards 58, 1001–1027. Jaquet, O., Connor, C., Connor, L., 2008. Probabilistic methodology for long-term assessment of volcanic hazards. Nucl. Technol. 163, 180–189. Jungclaus, J.H., Lorenz, S.J., Timmreck, C., Reick, C.H., Brovkin, V., Six, K., Segschneider, J., Giorgetta, M.A., Crowley, T.J., Pongratz, J., Krivova, N.A., Vieira, L.E., Solanki, S.K., Klocke, D., Botzet, M., Esch, M., Gayler, V., Haaki, H., Raddatz, T.J., Roeckner, E., Schnur, R., Widmann, H., Claussen, M., Stevens, B., Marotzke, J., 2010. Climate and carbon-cycle variability over the last millennium. Clim. Past 6, 723–737. Kahneman, D., 2011. Thinking, Fast and Slow. Farrar, Straus and Giroux, New York. ISBN: 9780374275631, 499 pages.

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Saunders, S.J., 2001. The shallow plumbing system of Rabaul caldera: a partially intruded ring fault? Bull. Volcanol. 63, 406–420. Scaini, C., Biass, S., Galderisi, A., Bonadonna, C., Folch, A., Smith, K., H€oskuldsson, A., 2014. A multi-scale risk assessment for tephra fallout and airborne concentration from multiple Icelandic volcanoes––part 2: vulnerability and impact. Nat. Hazards Earth Syst. Sci. 14, 2289–2312. Scollo, S., Coltelli, M., Bonadonna, C., Del Carlo, P., 2013. Tephra hazard assessment at Mt. Etna (Italy). Nat. Hazards Earth Syst. Sci. 13, 3221–3233. Scollo, A., Prestifilippo, M., Bonadonna, C., Cioni, R., Corradini, S., Degruyter, W., Rossi, E., Silvestri, M., Biale, E., Carparelli, G., Cassisi, C., Merucci, L., Musacchio, M., Pecora, E., 2019. Near-real-time tephra fallout assessment at Mt. Etna, Italy. Remote Sens. (Basel) 11, 2987. Self, S., 2015. Explosive super-eruptions and potential global impacts. In: Papale, P. (Ed.), Volcanic Hazards, Risks, and Disasters. Elsevier, Amsterdam, pp. 399–418. Selva, J., 2013. Long-term multi-risk assessment: statistical treatment of interaction among risks. Nat. Hazards 67, 701–722. Selva, J., Costa, A., Marzocchi, W., Sandri, L., 2010. BET_VH: exploring the influence of natural uncertainties on long-term hazard from tephra fallout at Campi Flegrei (Italy). Bull. Volcanol. 72, 717–733. Selva, J., Marzocchi, W., Papale, P., Sandri, S., 2012a. Operational eruption forecasting at high-risk volcanoes: the case of Campi Flegrei, Naples. J. Appl. Volcanol. 1, 5. Selva, J., Orsi, G., Di Vito, M.A., Marzocchi, W., Sandri, L., 2012b. Probability hazard map for future vent opening at the Campi Flegrei caldera, Italy. Bull. Volcanol. 74, 497–510. Selva, J., Costa, A., Sandri, L., Macedonio, G., Marzocchi, W., 2014. Probabilistic short-term volcanic hazard in phases of unrest: a case study for tephra fallout. J. Geophys. Res. Solid Earth 119, 8805–8826. Selva, J., Costa, A., De Natale, G., Di Vito, M.A., Isaia, R., Macedonio, G., 2018. Sensitivity test and ensemble hazard assessment for tephra fallout at Campi Flegrei, Italy. J. Volcanol. Geotherm. Res. 351, 1–28. Sheldrake, T.E., Aspinall, W.P., Odbert, H.M., Sparks, R.S.J., 2017. Understanding causality and uncertainty in volcanic observations: an example of forecasting eruptive activity on Soufrie`re Hills Volcano, Montserrat. J. Volcanol. Geotherm. Res. 341, 287–300. Sparks, R.S.J., 2003. Forecasting volcanic eruptions. Earth Planet. Sci. Lett. 210, 1–15. Sparks, S., Self, S., Grattan, J., Oppenheimer, C., Pyle, D., Rymer, H., 2005. Super-Eruptions: Global Effects and Future Threats. Report of a Geological Society of London Working Group, www.geolsoc.org.uk/supereruptions. Surono, Jousset, P., Pallister, J., Boichu, M., Buongiorno, F., Budisantoso, A., Costa, F., Andreastuti, S., Prata, F., Schneider, D., Clarisse, L., Humaida, H., Sumarti, S., Bignami, C., Griswold, J., Carn, S., Oppenheimer, C., Lavigne, F., 2012. The 2010 explosive eruption of Java’s Merapi volcano––a ‘100-year’ event. J. Volcanol. Geotherm. Res. 241– 242, 121–135. Tadini, A., Bisson, M., Neri, A., Cioni, R., Bevilacqua, A., Aspinall, W.P., 2017a. Assessing future vent opening locations at the Somma-Vesuvio volcanic complex: 1. A new information geodatabase with uncertainty characterizations. J. Geophys. Res. Solid Earth 122, 4336–4356. Tadini, A., Bevilacqua, A., Neri, A., Cioni, R., Aspinall, W.P., Bisson, M., Isaia, R., Mazzarini, F., Valentine, G.A., Vitale, S., Baxter, P.J., Bertagnini, A., Cerminara, M., de Michieli Vitturi, M., di Roberto, A., Engwell, S., Esposti Ongaro, T., Flandoli, F., Pistolesi, M., 2017b. Assessing future vent opening locations at the Somma-Vesuvio volcanic complex: 2. Probability maps of the caldera for a future Plinian/sub-Plinian event with uncertainty quantification. J. Geophys. Res. Solid Earth 122, 4357–4376.

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Chapter 2

A review of seismic methods for monitoring and understanding active volcanoes Gilberto Saccorottia and Ivan Lokmerb a

Istituto Nazionale di Geofisica e Vulcanologia, Pisa, Italy, bSchool of Earth Sciences, University College Dublin, Dublin, Ireland

1

Introduction

Since the pioneering observations conducted on Vesuvius (Italy) during the second half of the 19th century, seismometers have been the most common type of monitoring equipment deployed on volcanoes. This is mainly due to the fact that, until recently, seismic instruments were the most portable, in turn allowing continuous recording and data collection from remote locations. Moreover, almost all eruptions in seismically monitored volcanoes have been preceded by some sort of seismic anomaly, which in several cases gave rise to successful forecasts (e.g., McNutt et al., 2015; short-term seismic precursors to Icelandic eruptions during the past 40 years are also comprehensively reviewed by Einarsson, 2018). Nonetheless, it was only by the early 80s of the past century, with the establishment of physically sound source models and the advent of digital, continuous-recording instruments, that volcano seismology evolved toward an independent and quantitative discipline (e.g., Chouet, 2003). In early seismological investigations at volcanoes, instruments and methodologies were inherited from what had been originally developed for earthquake studies at the local and regional scales. However, seismic wave fields in volcanic environments present peculiarities which call for specific considerations and procedures. Seismic sources on volcanoes arise from a variety of mechanisms, ranging from the complex interaction between multiphase fluids and their hosting rock, to ductile deformation and brittle failure, all influenced by gravity forces associated with mass transport and/or sector instabilities. The resulting signals exhibit multiple signatures, whose characteristic frequencies range over a wide frequency interval. Owing to both the low amplitude of these signals and the almost-ubiquitous presence of volcanic tremor, the signal-toForecasting and Planning for Volcanic Hazards, Risks, and Disasters https://doi.org/10.1016/B978-0-12-818082-2.00002-0 Copyright © 2021 Elsevier Inc. All rights reserved.

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noise ratio (SNR) is generally poor, thus implying the need of conducting observations close to the source. In addition, seismo-volcanic sources are generally confined within the uppermost few kilometers of crust, and propagation occurs in markedly heterogeneous terrains, often bounded by pronounced topographical features. All these elements hinder the use of those approximations traditionally adopted in earthquake seismology, such as the omission of near-field effects, and the modeling of propagation in simplified, layered Earth models. Notwithstanding the fast evolution it experienced over the past few decades, volcano seismology still has to face several challenging perspectives, including (i) the improvement and generalization of physical models describing the source dynamics, (ii) the development of efficient processing environments for the real-time analysis of the huge amount of data continuously produced by monitoring networks, and (iii) the enhancement in the resolution of subsurface images of different seismological parameters, and their quantitative interpretation in terms of the specific physical conditions of volcanic materials. Several review papers and books already exist in the literature covering various aspects of volcano seismology (e.g., Chouet, 1996, 2003; Wassermann, 2012; Chouet and Matoza, 2013; McNutt et al., 2015; Thompson, 2015; Zobin, 2016). As the title indicates, this chapter mostly focuses on the specific methodologies which have been adapted to, or specifically developed for the quantitative analysis of volcanic signals. While presenting updates to what already discussed in the aforementioned review papers, we dedicate particular emphasis on the new/emerging methodologies, and on those still presenting unresolved issues.

2 Seismic sources and related signals The ultimate goal of volcano seismology is understanding the dynamics of a volcanic system, driven by magmatic and hydrothermal processes beneath volcanoes. These processes lead to different types of seismic activity observable at the surface. Although seismicity can differ dramatically from one volcano to another, there are common characteristics of seismic signals which allow for classifying them into four main categories: high-frequency (HF) events, long-period (LP) events, very-long-period (VLP) events, and volcanic tremor (Fig. 1). For these signals, the characteristic periods and corresponding wavelengths typically span the 10
1–102 s and 102–105 m orders of magnitude, respectively. Each of these categories, along with some sub-categories, are described below. A more comprehensive overview can be found in Zobin (2016), McNutt (2002, 2005), Chouet (2003).

2.1 High-frequency events High-frequency events (HF) occurring at volcanoes, also known as volcanotectonic (VT) events, are characterized by their impulsive onsets and dominant

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FIG. 1 Waveform examples and spectrograms for common signal types recorded on volcanoes. (A) Volcano-tectonic (VT) event recorded at Ba´rðarbunga, Iceland in 2014. (B) Long Period (LP) event recorded on Mount Etna, Italy, in 2008, (C) Very Long Period (VLP) event recorded on White Island Volcano, New Zealand, in 2011. (D) Volcanic tremor recorded during the 2014 Ba´rdarbunga eruption in Iceland. (VLP data courtesy of GNS Science, New Zealand; all other data recorded by the Seismology Lab, University College Dublin. After Thun, J., 2017. Constraining Volcano Seismic Source Properties Using Near-Field Observations (PhD thesis), University College Dublin.)

frequency between 2 and 20 Hz (mostly above 5 Hz), with the source depths between 1 and 20 km (e.g., Sanchez et al., 2004; Battaglia et al., 2004; Wyss et al., 2001; Chiarabba et al., 2000; Murru et al., 1999; Wiemer et al., 1998). Similar to ordinary tectonic earthquakes, the source mechanism of this event type is a brittle shear failure, although in some cases non-double-couple source mechanisms were found (e.g., Julian et al., 1997). They usually occur in swarms rather than following typical main shock-aftershock sequence; they are clustered in space and time without a single outstanding quake (McNutt, 2002). The stress perturbation required for triggering VT events is related to magma intrusion, pore pressure, hydrofracturing in combination with tectonic stresses,

28 Forecasting and planning for volcanic hazards, risks, and disasters

and gravitational loading (McNutt, 2005). For this reason, an increase in VT seismicity is often one of the earliest detectable precursors to volcanic eruptions (e.g., Roman and Cashman, 2006; Einarsson, 2018). Triggering of volcanic seismicity and eruptions by large distant earthquakes has also been reported by numerous studies (e.g., Cannata et al., 2010; West et al., 2005; Hill et al., 2002; Gomberg et al., 2001), implying in these cases a critical state of stress in the volcanic edifice. High-frequency (up to 40 Hz), very shallow VT events were reported by De Barros et al. (2013), at Piton de la Fournaise Volcano (La Re`union), before the October 2010 eruption. These events were located at the future magma eruptive site, with magnitudes much smaller than those of a preceding swarm of deeper earthquakes. De Barros et al. (2013) interpreted the low-magnitude, shallow VTs as the generic response of the upper part of the volcanic edifice to the stress perturbation caused by magmatic migration at depth, and highlighted those same events as a promising tool for the short-term forecasting of the eruption location. Super-shallow (60 m) microseismic HF events were also reported by Thun et al. (2016), in association with rifting during the 2014 Ba´rðarbungaHoluhraun basaltic dike intrusion episode in Iceland.

2.2 Long-period events Long-period (LP) events (Fig. 1B), also known as low-frequency (LF) events commonly have dominant frequencies between 0.5 and 2 Hz (Woods et al., 2018; Zecevic et al., 2016; Eyre et al., 2015; Chouet and Matoza (2013); De Barros et al., 2009; Lokmer et al., 2008; Saccorotti et al., 2007a; Neuberg et al., 2000; Chouet, 1996). Owing to their long wavelengths (relative to VT events) and small hypocentral distances (most observations locate them above 1 km depth, although there are deeper exceptions), P and S waves are intertwined and cannot be distinguished in most cases. The properties of the nearfield radiation of LP events are described in Lokmer and Bean (2010). LP events typically have more emergent onsets than VT events, which can be followed by a coda of slow-decaying harmonic oscillations (e.g., Nakano et al., 2003; Kumagai et al., 2002a), or have an impulse-like shape (e.g., Bean et al., 2014; De Barros et al., 2011; Lokmer et al., 2007). The comparison of these two types of signals is given in Fig. 2A and B. The events with exceptionally long (a few tens of seconds) monochromatic coda (Fig. 2A) are called “tornillos” (Fazio et al., 2019; Go´mez et al., 1999), and are mainly observed on andesitic volcanoes. As LP seismicity usually increases before volcanic eruptions (e.g., Chouet, 1996), it is considered a promising forecasting tool and has become the focus of numerous studies. Despite many proposed source models, the LP source processes are still not well understood. The reason is a wide variety of LP waveforms observed at different volcanoes (or even at the same volcano at different periods), as well as the difficulty in separating their source from path effects.

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FIG. 2 (A) LP event waveforms recorded on Kusatsu-Shirane (Japan), exhibiting different dominant frequencies and coda durations. (B) Pulse-like LP signals at different volcanoes, observed only at close stations with minimal path effect. (C) LP seismicity recorded on Mt. Etna (Italy) in June 2008 (family 1 from De Barros et al., 2011). The waveforms represent a stack of 63 events, plotted against the distance from the summit below which the LP source was located. Note the increase in signal duration with the distance from the summit. ((A) After Kumagai, H., Chouet, B.A., Nakano, M., 2002a. Temporal evolution of a hydrothermal system in Kusatsu-Shirane volcano, Japan, inferred from the complex frequencies of long-period events. J. Geophys. Res. 107 (B10), 2236. https://doi. org/10.1029/2001JB000653. (B) Modified after Bean, C.J., De Barros, L., Lokmer, I., M
etaxian, J.-P., O’Brien, G., Murphy, S., 2014. Long-period seismicity in the shallow volcanic edifice formed from slow-rupture earthquakes. Nat. Geosci. 7 (1), 71–75. https://doi.org/10.1038/ngeo2027.)

The most prevailing LP source models in the literature are based on the longduration harmonic oscillations observed on the seismograms, and involve resonating fluid-filled cracks or conduits related to magmatic and hydrothermal activity, differing mainly in the source geometry and the way the resonance

30 Forecasting and planning for volcanic hazards, risks, and disasters

is triggered and sustained. The proposed models include oscillations of magmafilled cracks triggered by crack tip extension (Chouet, 1986, 1988; Chouet and Julian, 1985; Aki et al., 1977) or by acoustic emission from collapsing bubbles (Chouet, 1992), resonance of a steam-filled crack triggered by unsteady choked flow (Morrissey and Chouet, 2001; Chouet et al., 1994), resonance of magma pipe (Chouet, 1985), nonlinear fluid flow through elastic channel (Julian, 1994), resonance of a cylindrical conduit triggered by brittle failure of high-viscosity magma near a conduit wall (Neuberg et al., 2006) or by sudden degassing event (Neuberg and O’Gorman, 2002; Neuberg et al., 2000), resonance of a spherical magma conduit (Fujita and Ida, 2003; Crosson and Bame, 1985), periodic magma flow (Ukawa and Ohtake, 1987) and rapid discharge of gases (Steinberg and Steinberg, 1975). A comprehensive overview of fluid-filled source models is given by Chouet and Matoza (2013). A conceptually different (non-resonating) model was proposed by Bean et al. (2014), where the pulse-like LP seismicity (Fig. 2B) was explained by a slow-slip brittle failure in the exceptionally weak uppermost part of the volcanic edifice. On the basis of the absence of the long oscillating coda at the signals recorded closer than 1–2 km from the source (Eyre et al., 2015; De Barros et al. 2009, 2011), the authors attributed it to the path effect rather than the source effect. The importance of path effects on volcanoes is illustrated by Fig. 2C, where 3 s-long signal at the summit of Mt. Etna (beneath which the source was located) develops a long coda as the distance from the summit increases (see also Fig. 2 in Saccorotti et al., 2007a). Strong effects of volcano topography on volcanic signals were demonstrated by synthetic studies of O’Brien et al. (2009) and Ripperger et al. (2003). Using numerical simulations, Bean et al. (2008) showed that combined effects of topographical features and near-surface low-velocity structure (commonly observed at volcanoes) play a critical role in controlling the coda of LP signals. If not accounted for, these path effects can be misinterpreted as the source features. A similar conclusion was reported by Mora et al. (2001), who studied site effects using linear array on Arenal volcano, Costa Rica. An excellent field example of the pronounced site effects near Mammoth Mountain, California, is shown in Fig. 9 of McNutt (2005), where VT events recorded by a station located on an old dome appear like LP harmonic signals in the recordings from another station located on soft sediments. The standard classification of events in volcano seismology is somewhat lacking, as the LP class is based only on the signal frequency content and the lack of distinct P and S phases; however, tornillo and pulse-like events (Fig. 2A and B, respectively) do not appear to be caused at all by the same process, so the classification will likely be refined in the future. This section would not be comprehensive without mentioning hybrid events. They appear to be a combination of VT and LP events, with a sharp highfrequency onset followed by low-frequency coda. They have been interpreted as LP events, where the resonance is triggered by a brittle failure (Lahr et al.,

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1994). Neuberg et al. (2000) found that hybrid and LP events on Soufrie`re Hills Volcano, Montserrat, form a continuum in terms of their frequency content, rather than being two separate classes of events. The results of Harrington and Brodsky (2007), who studied hybrid seismograms associated with 2004 Mount St. Helens eruption, suggest that these are brittle failure events whose coda is the consequence of a path effect—similar to the LP source model proposed by Bean et al. (2014). Hybrid events are likely caused by various processes depending on the volcano (or time period) at which they are recorded.

2.3

Volcanic tremor

Volcanic tremor (Fig. 1D) is a sustained volcanic signal which can last from minutes to months, and it is often observed during both quiescent and eruptive periods. Typically, its frequency content is 0.1–10 Hz (sometimes higher), representing either a fundamental frequency and higher harmonics (harmonic tremor) or a continuous spectrum with no separate discrete frequencies standing out (inharmonic tremor); frequencies of the peaks in harmonic tremor often change with time, thus forming “gliding spectral lines” in spectrograms (e.g., Hotovec et al. (2013) and references therein). The source depth of tremor can vary considerably, from several hundred meters to 40 km (see Konstantinou and Schlindwein (2003) for a comprehensive review of tremor studies). The most common interpretation of tremor sources involve a complex interaction of magma and/or hydrothermal fluids with the surrounding host rock, similar to the LP models outlined in the previous section, but with repetitive or sustained triggering mechanism (e.g., De Angelis and McNutt, 2007; Lesage et al., 2006; Hellweg, 2000; Neuberg et al., 2000; Julian, 1994; Chouet, 1992). Merging swarms of LP events into tremor was observed on many volcanoes. In such cases, the tremor source was explained by a fast succession of earthquakes in time, either by repetitive pressure fluctuations (Hellweg, 2000; Neuberg et al., 2000; Chouet, 1992), or a sequence of slip-stick events (Kendrick et al., 2014; Hotovec et al., 2013; Iverson et al., 2006). If the repetition is regular, the tremor is referred to as harmonic. The spectral gliding can be attributed to the change in the acoustic velocity of the resonator (e.g., Jousset et al., 2003; Neuberg and O’Gorman, 2002), or change in the trigger frequency (Neuberg, 2000). There are also “dry” models explaining the tremor generation: Eibl et al. (2017) interpreted the tremor observed before the Ba´rðarbunga 2014 eruption as the swarms of microseismic brittle events in the upper 2–3 km of the crust, seen as the elastic response to the magma ascent at depth.

2.4

Very-long period events

Very-long period (VLP) events (Fig. 1C) are characterized by periods from about 3 s to 100 s and more. They were only discovered in the early 1990s, after

32 Forecasting and planning for volcanic hazards, risks, and disasters

the deployment of broadband seismometers on volcanoes. Since then, they have been observed on many different volcanoes, such as Aso, Japan (Kawakatsu et al., 2000; Legrand et al., 2000; Kaneshima et al., 1996), Stromboli, Italy (Chouet et al., 2003; Neuberg et al., 1994), Merapi, Indonesia (Hidayat et al., 2002), Sakurajima, Japan (Uhira and Takeo, 1994), Kilauea, Hawaii (Almendros et al., 2002; Ohminato et al., 1998), Popocat
epetl, Mexico (Chouet et al., 2005), Etna, Italy (Zuccarello et al., 2013; Cannata et al., 2009), White Island, New Zealand (Jolly et al., 2017) and others. They can occur in swarms and form families of similar events. VLP events are thought to originate from the action of inertial forces associated with perturbations in multi-phase fluid flow through conduits, such as gas slug ascent (e.g., O’Brien and Bean, 2008; Chouet et al., 2003; Yamamoto et al., 1999; Neuberg et al., 1994; Uhira and Takeo, 1994) or eruption and magma recharge (Aster et al., 2003). Substantial variations in VLP frequency, amplitude and waveform are usually seen as an indicator of the pressure changes in the magmatic system and potentially useful tool for forecasting eruptions (Zuccarello et al., 2013; Chouet et al., 2010; Cannata et al., 2009; Patane` et al., 2008). The estimated VLP source depths range from several hundred meters (Rowe et al., 1998; Chouet et al., 1999, 2003; Hidayat et al., 2000; Aster et al., 2008; Cannata et al., 2009; Lyons and Waite, 2011; Maeda and Takeo, 2011) to 1– 3 km (Arciniega-Ceballos et al., 1999; Nishimura et al., 2000; Almendros et al., 2002; Molina et al., 2008; Haney, 2010; Haney et al., 2013; Jousset et al., 2013; Jolly et al., 2017, 2018). A few observations report even deeper sources: 8 km at Kilauea, Hawaii (Almendros et al., 2002), and 30 km at the Klyuchevskoy volcanic group in Kamchatka, Russia (Shapiro et al., 2017). VLP events are sometimes coupled with VT and LP signals, indicating the response of the volcanic edifice and/or embedded plumbing system to mass transport beneath a volcano. The example of such coupling is shown in Fig. 3.

2.5 Explosions and other volcanic signals Other types of events recorded on volcanoes, not explicitly outlined above, include volcanic explosions, rockfalls, and lava flows. The signals associated with volcanic explosions often include a mix of seismic waves and air-shock waves coupled with the ground: for this reason they are usually studied using the combination of seismic and infrasound instruments (e.g., Yokoo et al., 2019; De Angelis et al., 2012; Johnson and Ripepe, 2011; Johnson and Aster, 2005; Johnson et al., 2004; Hagerty et al., 2000; Johnson and Lees, 2000). The seismic waveforms related to explosion-quakes usually have the oscillating character and the frequency content in the range of about 1–7 Hz, so if they are not recorded by infrasound or broadband acoustic sensors (or even cameras around volcanic vent), it may be difficult to distinguish them from certain types of LP

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FIG. 3 Example of a coupled VLP/LP/HF event recorded on White Island volcano (New Zealand) in 2011. (A) Normalized unprocessed seismogram (B) corresponding spectrum with marked peaks of the VLP, LP and HF contribution; (C) Filtered traces for these different contributions. (After Jolly, A., Lokmer, I., Christenson, B., Thun, J., 2018. Relating gas ascent to eruption triggering for the April 27, 2016, White Island (Whakaari), New Zealand eruption sequence. Earth Planet Space, 70 (177). https://doi.org/10.1186/s40623-018-0948-8.)

events. Johnson and Aster (2005) have developed methods for determining the relative acoustic and seismic contributions during explosive eruptions. It is also important to mention that some of the anthropogenic signals can look similar to the volcanic signals described in the previous sections. Such an example is given by Eibl et al. (2015), where a helicopter flying over Hekla volcano, generated a signal resembling high-frequency gliding tremor.

34 Forecasting and planning for volcanic hazards, risks, and disasters

3 Seismic monitoring The main goal of a seismo-volcanic monitoring program is the timely detection of changes in the size, rate, and location of seismic events, which may herald an impending eruption. This section presents a short overview of instruments and techniques adopted for attaining these objectives; more detailed descriptions of the type of seismological instruments dedicated to volcano monitoring, and the requirements for achieving monitoring levels of increasing accuracy, can be found in McNutt et al. (2015), Thompson (2015), and Moran et al. (2008).

3.1 Instrumentation for recording of seismicity The capability of a seismic deployment to determine seismological parameters with an increasing level of complexity depends upon several critical aspects. The first is the frequency bandwidth of the seismometers. Broad-band instruments are required to correctly sample volcanic signals over their whole spectral range, say frequencies in between 0.01 Hz and 10 Hz or, equivalently, periods in between 0.1 s and 100 s (Fig. 4A and B). The second key element is the dynamic range, that is, the ratio between the smallest and largest detectable amplitude of ground motion, which are both determined by the geometrical and electronic characteristics of the seismometer. In modern, high-quality sensors, the amplitude of the output signal at the clipping (saturation) level is about seven orders of magnitudes larger than the electronic noise generated by the instrument itself. Such a wide interval is fully exploited by 24-bit analog-to-digital converters and, most of the time, it is sufficient for correctly recording the amplitude interval spanned by volcanic signals at close distances (2–10 km) from the source. The graph of Fig. 4C shows typical self-noise and clipping levels of short-period and broad-band instruments, compared with sample amplitudes of volcanic signals. For a more technical and exhaustive discussion on the type and characteristics of seismological instruments, the reader is referred to Havskov and Alguacil (2016) and Bormann and Wielandt (2013). A third critical factor concerns the number of seismometers. While, in principle, just a single seismometer is sufficient for detecting the occurrence of seismic swarms or an increase of tremor activity, at least 4 sensors are required for locating earthquakes. Accuracy of the location, and the minimum detectable magnitude depend then on further factors, such as (i) the source-to-receiver distance, (ii) the noise conditions at the recording sites, and (iii) the geometry of the network. The same holds for more sophisticated analyses such as moment tensor inversions, whose reliability requires an even larger number of broad-band instruments. Nonetheless, both dimension and density of permanent monitoring networks are intrinsically limited by the great effort and resources which are required for their maintenance in the usually harsh and dangerous volcanic environments. Therefore, except for small-scale or short-duration deployments, seismic networks on volcanoes usually suffer from poor spatial sampling issues.

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FIG. 4 (A) Frequency bands spanned by the different types of volcanic events as described in Section 2, compared with the response functions of typical short-period (1 s) and broad-band (120 s) seismometers (blue and gray lines, respectively). Squares indicate the low frequency limit, or cut-off frequency, which for an electromagnetic sensor corresponds to the free-period of the massspring system. At that frequency, the amplitude of the output signal is about 70% of that recorded over the flat region of the response function. Note that, in the seismological jargon, this cut-off frequency (or corresponding period) is often used for characterizing the sensitivity bandwidth of a seismometer. (B) Effect of seismometer bandwidth on the recording of a VLP waveform. The uppermost seismogram shows a VLP signal recorded by a 60s instrument on White Island volcano (New Zealand) in 2011. The other three traces show how the same signal would be recorded by sensors with cut-off periods of 5 s, 1 s and 0.2 s, respectively. (C) Example of typical power spectral densities (PSD) of volcanic signals from Mt. Etna volcano (Italy). Red and yellow curves are spectra of volcanic tremor recorded during the 2002–2003 eruption and a quiescent period in 2005, respectively. Gray patches mark the frequency-power ranges spanned by LP, VLP and a VEI ¼ 1 explosion (EXP) signals observed during non-eruptive periods on the year 2005. All data were gathered at a distance of approximately 2 km from the source. The reported power-frequency ranges may vary significantly once accounting for different periods, volcanoes, distance from the source. For reference, Peterson’s (1993) Low- and High-Noise models are shown by dashed lines. The purple line corresponds to the model spectrum for a M ¼ 3.5 earthquake at a distance of 10 km. The self-noise (continuous lines) and clipping level (dashed lines) of typical short-period and broad-band seismometers are shown in blue and gray, respectively. ((C) After Clinton, J.F., Heaton, T.H., 2002. Potential advantages of a strong-motion velocity meter over a strong-motion accelerometer. Seismol. Res. Lett. 73 (3), 332–342. https://doi.org/10.1785/gssrl.73.3.332.)

3.1.1 Distributed acoustic sensing Recent advances in the field of fiber-optic technologies now offer a promising complement to seismometer deployments for the time-space measurement of seismic wavefields. These techniques, usually referred to as distributed acoustic sensing (DAS), are based on the same principles of optical time-domain

36 Forecasting and planning for volcanic hazards, risks, and disasters

reflectometers. These devices consist of injecting coherent laser pulses into an optical fiber coupled to the ground. At the same end of the fiber, a sensing element extracts the light that is scattered or reflected from specific points along the fiber. Therefore, any longitudinal deformation of the fiber can be determined by measuring changes in the travel-time of successive, backscattered light pulses. This technology provides outstanding temporal and spatial resolutions: as an example, the DAS system operated at the Brady Hot Springs, Nevada, measured the dynamic strain at points spaced by 1 m along a 8-km-long fiber, with a sampling frequency of 10 kHz (Wang et al., 2018; see Fig. 5). DAS applications to seismology are very recent (e.g., Lindsey et al., 2017; Jousset et al., 2018; Ajo-Franklin et al., 2019) and, to date, just a single application has been dedicated to the recording of volcano seismic signals (Contrafatto et al., 2019; Jousset et al., 2019). The huge amount of data produced by these devices poses however new challenges toward their use for continuous monitoring.

3.1.2 Rotational sensors During the transit of a seismic wave, the ground is subjected not only to translations, but also to tiny rotations. In particular, dynamic tilt (i.e., the rotational motion around horizontal axes) change the projection of local gravity onto the horizontal components of seismometers; as a consequence, the signals from these components will represent a combination of both translational and rotational motions. In principle, these two contributions could be separated by

FIG. 5 (A) DAS system operated at the at Brady Hot Springs, Nevada, in March 2016. The system consisted of a 8600-m-long fiber-optic cable, with sampling locations spaced by 1 m. The arrow is directed along the source-to-receiver azimuth for the earthquake shown in (B). Red segments indicate the DAS channels used for plotting the panels in (B). (B) Strain recorded by three sets of 80 consecutive sampling channels (red lines in (B)) for a ML ¼ 4.3 earthquake on March 21 at 07:37:10 at an epicentral range of about 150 km (https://earthquake.usgs.gov/earthquakes/eventpage/ nn00536374/ Accessed 31 May 2019). The P-wave arrival is about at second 16.3. (All data are from University of Wisconsin, 2017. PoroTomo Natural Laboratory Horizontal Distributed Acoustic Sensing Data [data set]. Retrieved from http://gdr.openei.org/submissions/980 (Accessed 9 September 2019).)

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gathering co-located measurements from a seismometer and tiltmeter. However, it has been shown that the signals recorded by bubble-level tiltmeters are in turn affected by ground translations, at least within the characteristic period range of VLP sources (Fournier et al., 2011). To solve this double ambiguity, an interesting perspective is offered by ring-laser gyroscopes. On the basis of active interferometry principles, these sensors are free from moving parts and in principle measure rotations completely rejecting linear accelerations and gravitational force. Large-area gyroscopes, such as the G at Wettzell, Germany (Schreiber et al., 2009) and the Gingerino at the Gran Sasso Laboratories, Italy (Belfi et al., 2017), measure ground rotation velocity with sensitivities on the order of 10
12 and 10
11 rad/s, respectively. Owing to their large dimension, cost, and difficult maintenance, these instruments cannot be installed in the near field of active volcanic sources. Recently, however, portable instruments based on similar technologies have been developed commercially with sensitivities on the order of 2  10
8 rad/s (Bernauer et al., 2018), which in principle is sufficient for resolving ground tilt in the near-field of VLP sources.

3.2

Signal detection

With the advent of digital, continuous-recording seismic networks and the establishment of a solid theoretical background on the correlation properties of seismic noise, the continuous ground vibrations of either volcanic (i.e., tremor) or external (atmospheric, anthropogenic) origin are now recognized as useful signals for both imaging and monitoring purposes (see Sections 5.2–5.4). Nonetheless, a major task persists, and it involves the automatic detection of those transient signals previously described in Section 2. Such detection task is usually conducted by applying an appropriate threshold to the ratio between the short-term average (STA) and long-term average (LTA) of the seismic amplitudes (Allen, 1978). This procedure, however, fails in case of events occurring in close sequence, signals with emergent onsets, or signals whose dominant frequency is significantly different from that for which the triggering procedure was tuned. To overcome this issue, in recent years matched-filter approaches have become popular. This class of methods relies on “template matching,” where continuous seismic data streams are cross-correlated with known seismic signals taken from a template database specific for the particular volcano accounted for. Such a procedure leads to the recovery of signals even at very low SNR, allowing for a drastic increase in the number of detections (e.g., Zhang and Wen, 2015), in turn permitting detailed statistical studies of catalogs (Cauchie et al., 2015), and accurate relocations (Shelly et al., 2013a,b; Lenglin
e et al., 2016). The main drawback of the matched-filter technique is the reliance on identifying pre-defined signals in the continuous data stream; the completeness of the final, refined catalog is therefore heavily biased toward the wavelets from the initial template archive.

38 Forecasting and planning for volcanic hazards, risks, and disasters

3.3 Classification The classification of volcano seismicity aims to assign the different types of observed signals to a distinct source process. This task is particularly relevant not only for distinguishing among different volcanic phenomena (see Sections 2 and 4), but also for the discrimination of signals of non-volcanic origin. To this respect, it is worth mentioning ice-quakes, which are frequently observed on the numerous volcanoes of the world that are ice-covered (e.g., Allstadt and Malone, 2014, and references therein). Classification can be performed manually by experienced analysts; the subjectiveness of this approach may however lead to significant disagreements between what obtained by different operators. In addition, the visual inspection is rapidly becoming unfeasible due to the exponential increase in the amount of recorded data. As in other disciplines, machine learning (ML) methods of varying complexity are now adopted for assigning the observed signals to distinct categories associated with semantic information (e.g., Malfante et al., 2018a). Using a variety of algorithms, ML-based classifications have been applied to a number of volcanoes worldwide, including Deception, Antarctica (Titos et al., 2018); Nevado del Ruiz (Orozco et al., 2006) and Galeras (Bicego et al., 2013) in Columbia; Stromboli (Iba´n˜ez et al., 2009; Esposito et al., 2008), Mt. Etna (Langer et al., 2009; Falsaperla et al., 1996), Phlegrean Fields (Del Pezzo et al., 2003) and Vesuvius (Scarpetta et al., 2005), in Italy; Merapi, Indonesia (K€ ohler et al., 2010); Kilauea, Hawaii (Dawson et al., 2010); Soufriere Hills, Montserrat (Langer et al., 2003, 2006; Hammer et al., 2012); Krakatau, Indonesia (Ibs-von Seht, 2008); Villaricca, Chile (Curilem et al., 2009), and Ubinas, Peru (Malfante et al., 2018b). Maggi et al. (2017) analyzed signals at Piton de la Fournaise volcano using a multi-station approach, thus reducing propagation and site effects on the results of signal classification. Of particular interest are also those applications acting on continuous data streams and incorporating the detection and classification tasks into a single processing procedure (e.g., Iba´n˜ez et al., 2009; Hammer et al., 2012). A further issue concerns the vast number of often inconsistent terms which are adopted for classifying seismic events at individual volcanoes. To this purpose, an interesting perspective is offered by unsupervised learning, a class of algorithms that do not rely on any predefined category, rather inferring the natural subdivision in classes which is present within data themselves (e.g., Del Pezzo et al., 2003; K€ohler et al., 2010; Langer et al., 2006; Titos et al., 2018). Finally, a robust assessment of uncertainties is recommended, such as that provided through the incorporation of a Bayesian formalism into the classification procedure (e.g., Bueno et al., 2019).

3.4 Location The most common solution to the location problem relies on the inversion of the arrival times of P and/or S-waves observed at a number of stations.

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State-of-the-art solutions to this classic, non-linear inverse problem include (i) travel-times calculations using numerical schemes which can fully account for complex 3D velocity structures and topography, and (ii) probabilistic, global-search throughout the model space (e.g., Lomax et al., 2009). This method has been applied, for instance, by Lomax et al. (2001) to Vesuvius Volcano and by Saccorotti et al. (2007b) to the Campi Flegrei Caldera, Italy. The precision in the estimate of body-wave arrival times can be significantly increased by applying cross-correlation to sets of signals exhibiting similar waveforms (multiplets). Application of these procedures permits to achieve locations of exceptional accuracy, enlightening the fine structure of magma pathways during intrusive phenomena (e.g., Woods et al., 2019, and reference therein). However, due to their emergent onsets and lack of clear body-wave phase arrivals, LP and tremor signals cannot be located using travel-time inversion. A number of alternative methods have thus been developed and are synthesized in the following section.

3.4.1 Coherence-based methods When multiple seismometers are deployed at different azimuths and small distances (i.e., shorter than the dominant wavelength) from a source radiating almost isotropically, then the different seismograms will exhibit a similar signature, except for the delay times associated with the different source-to receiver path lengths. Under these conditions, the location problem can be solved by calculating the theoretical travel times from a number of trial sources to the different stations. For each trial source, seismograms are time-shifted according to the corresponding travel times, and a multichannel measure of coherency is determined; the best source location is that for which the timeshifted seismograms exhibit the largest coherence. In this respect, the most widely adopted measure of coherence is the Semblance coefficient, eventually scaled by a penalty function which accounts for the radial polarization of the signal pointing to the source (e.g., Ohminato et al., 1998; Kawakatsu et al., 2000; Dawson et al., 2004). Performance and limitations of this approach have been carefully addressed by Almendros and Chouet (2003). 3.4.2 Back-propagation methods This class of methods constitutes an extension of what described in the previous sub-section, with the difference that the inter-station coherence of waveforms is not mandatory. Specific characteristic functions are calculated from the seismograms recorded by individual stations. These time series are then migrated onto a search grid using precalculated travel times and finally stacked. Local maxima in the resulting 4D space-time grid correspond to the locations and origin times of seismic events. Different characteristic functions have been used thus far, including the weighted time-average of the absolute signal’s amplitude

40 Forecasting and planning for volcanic hazards, risks, and disasters

(Kao and Shan, 2004), or the kurtosis of the signal (Langet et al., 2014). This procedure exploits waveform information (both arrival times and amplitudes) without the need of preassembled phase-picking data; therefore, it is particularly well-suited for emergent signals and/or real-time applications.

3.4.3 Amplitude-based methods The amplitude source location (ASL) method is another example of location procedure not relying on travel-time inversion. Using an attenuation model for a single wave type (surface vs body waves) propagating isotropically in a homogeneous medium, ASL searches for those source location and strength which best fit the amplitudes observed at the recording stations. Specific station correction terms are usually applied, after determination of site amplification factors from earthquake recordings. Given its simplicity, the ASL approach has been used for a variety of applications, including LP events (Battaglia and Aki, 2003), volcanic tremor (Taisne et al., 2011; Ogiso and Yomogida, 2012; Ogiso et al., 2015), volcano-tectonic earthquakes (Kumagai et al., 2013), explosion events (Kumagai et al., 2011), magma intrusions (Taisne et al., 2011), lahars (Kumagai et al., 2009), debris flows (Ogiso and Yomogida, 2015), and pyroclastic flows (Yamasato, 1997; Jolly et al., 2002). An alternative procedure considers amplitude ratios between independent station pairs rather than the absolute amplitudes at individual recording sites. This formulation has been used by Caudron et al. (2018) to locate continuous seismic energy during the dike intrusion in the Ba´rdarbunga volcanic system in Iceland in 2014, and by Carbone et al. (2008) to track the continuous volcanic tremor a Mt. Etna Volcano, Italy. Using active sources with known location (sandbags thrown from a helicopter; Jolly et al., 2014) Walsh et al. (2017) estimated the accuracy of the ASL technique to be on the order of 1000 m and 500 m along the horizontal and vertical coordinates, respectively. In general, however, the reliability of ASL locations depends on several critical factors, including: (i) the geometrical spreading model; (ii) the heterogeneous distribution of both quality factor and seismic velocity within the volcano; (iii) the reliability of site correction terms drawn from aggregate local/regional seismicity; (iv) the influence of network geometry and station density, and (v) the assumed, isotropic radiation pattern of the source. Point (v) above was extensively investigated by Morioka et al. (2017), who applied the ASL approach to synthetic waveforms generated by a double-couple source and propagating in a realistic volcanic structure with intrinsic attenuation and topography. In agreement with the results from Kumagai et al. (2009), Morioka et al. (2017) found that once accounting for high-frequency (f > 5 Hz) wavefields and heterogeneous distribution of the elastic parameters, diffusion processes overwhelm the direct contribution from the source. Under

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these conditions, the basic assumption of isotropic radiation is validated, thus justifying the application of the ASL method to seismo-volcanic signals.

3.4.4 Time-reverse location methods A recent alternative to the aforementioned techniques is represented by the time reverse location method. In this approach, the time-reversed signals recorded by a number of stations are back-propagated using a numerical wave simulator. As the reversed signals propagate through the numerical model, they interfere constructively and destructively, and focus at the original source location. Therefore, the best source time-space coordinates are selected at the time and location where the simulated wavefield attains maximum convergence. The method was tested on synthetic tremor waveforms by Lokmer et al. (2009), and further applied to LP signals from Mt. Etna Volcano by O’Brien et al. (2011). Such procedure, however, requires large computational effort, and the reliability of the results depends critically on the accuracy of the velocity model. It also requires dense station coverage allowing for accurate spatial sampling of the propagating wavefield. 3.4.5 Array methods A seismic array is defined as a set of synchronized seismometers deployed in a homogeneous area with inter-station spacings smaller than a fraction of the wavelengths of interest. Under these conditions, the signal recorded by the different array elements is essentially the same, except for a time delay due to propagation (common waveform model of the signal). Most array processing procedures use a plane-wave propagation model, an assumption that holds once the array aperture is significantly smaller than the source-to-receiver distance. In that case, the differential travel times Tij between the i-th and j-th array elements are linearly related to the respective coordinates (xi
xj) through the relationship:
Tij ¼ p  xi
xj (1) where p is the horizontal slowness vector, whose direction and magnitude coincide with the propagation direction and inverse of apparent velocity, respectively. A number of methods exist for estimating the vector p either in the frequency or time domain. The problem may be solved by first estimating the differential travel-times/phase shifts between station pairs, and then inverting these data using Eq. (1). Alternative processing schemes are based on the exhaustive search for the horizontal slowness which associated time delays/ phase shifts (Eq. 1) bring in phase the array seismograms. Finally, a few studies approached array analysis of broad-band volcanic signals using multichannel wavelet coherence estimates, thus achieving in a single processing step the detection and measurement of signals in the time-frequency plane

42 Forecasting and planning for volcanic hazards, risks, and disasters

(Wassermann and Ohrnberger, 2001; Saccorotti et al., 2008) A comprehensive review of array methods is found in Rost and Thomas (2002) and Schweitzer et al. (2012). While a single array allows for deriving only the source back-azimuth, constrained estimates on source location can be retrieved from the intersection of seismic rays back-projected by different arrays recording simultaneously (e.g., Almendros et al., 2002; M
etaxian et al., 2002; Di Lieto et al., 2007; Inza et al., 2011). The main advantage of array methods is that they are able to tackle emergent/sustained signals, tracking their evolution in time. An example is shown in Fig. 6, where intense lava fountaining episodes at Mt. Etna Volcano, Italy, are clearly anticipated by the activation of a tremor source located beneath the eruptive vents.

4 Methods for source studies Investigating the source properties of volcano-seismic signals is crucial for improving our understanding of volcano dynamics. The least demanding in this respect are VT events, as their generation process—shear fracture—is well understood and the source inversion methodology is well established in earthquake seismology: either by determining focal mechanisms from the first polarities (e.g., Hidayati et al., 2008; R€ ognvaldsson and Slunga, 1993, 1994; Reasenberg and Oppenheimer, 1985) or by far-field moment-tensor inversion (e.g., Cesca et al., 2006; Dahm and Brandsdottir, 1997; Jost and Herrmann, 1989). The source locations and focal mechanisms of VT events have been used to infer the information on many different aspects of volcano dynamics, such as magma ascent and pressurization of volcanic system (Patane` et al., 2003, 2005), dike emplacement (Gardine et al., 2011), caldera collapse (Gudmundsson et al., 2016), estimating intrusive volumes (White and McCausland, 2016), triggering seismicity (Wolfe et al., 2007), tectonomagmatic interaction in a rift zone (Oliva et al., 2019), and many others. Stress tensor inversion of multiple focal mechanisms (Martı´nez-Garzo´n et al. (2016), and references therein) is sometimes used to determine stress orientation at volcanoes in order to investigate the volcano-tectonic interactions, and/or eruptive patterns (e.g., Plateaux et al., 2014; Roman et al., 2008; Sa´nchez et al., 2004). Many volcano observatories routinely locate and determine focal mechanisms of VT events, usually using the first polarities and amplitudes of the recorded signals. Determining source mechanisms of other types of volcanic seismicity, especially LP and tremor, is a more demanding task. Their exact genetic provenance is still ambiguous (see Section 2), and the strong path and site effects on volcanoes additionally hinder inversions. Thus, apart from rare attempts to perform moment-tensor inversion of tremor signals (Davi et al., 2012), the tremor sources are usually indirectly studied by analyzing the tremor wavefield in

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44 Forecasting and planning for volcanic hazards, risks, and disasters

terms of its frequency content, wave types, duration, location, released energy, relationship to gas output, etc.(e.g., Salerno et al., 2018; Lesage et al., 2006; Saccorotti et al., 2004; Almendros et al., 1997). The sources of LP and VLP seismicity have been extensively studied by combining forward modeling of their generation processes (see Section 2 and references therein), moment tensor inversion (see next section for references), laboratory experiments (Fazio et al., 2019; Harrington and Benson, 2011; James et al., 2004, 2006, 2008), and analyzing the properties of their recorded wavefield (e.g., Eyre et al., 2015; Jousset et al., 2013; Lokmer et al., 2008; Kumagai, 2006; De Angelis and McNutt, 2005; Kumagai et al., 2002b, 2005). The size of the source is determined based on the adopted source model: thus, for the brittle failure model, the source size can be determined from the corner frequency of the recorded displacement (Madariaga, 1976; Sato and Hirasawa, 1973), while for the fluid-filled crack model, the properties of the resonator are used, such as the resonating frequency, source geometry, the type of fluid and surrounding solid, and the mode of oscillation. Kumagai and Chouet (1999, 2000, 2001) and Morrissey and Chouet (2001) extensively investigated the resonance of a fluid-filled fracture under a variety of fluid and rockmatrix properties, thus providing the framework for estimating source size from the resonating frequencies and attenuation factors determined from the recorded signals. Regardless of the model (brittle or fluid-filled cavities), the LP source sizes are estimated in the range 50 m to several hundred meters (Bean et al., 2014; Lokmer et al., 2008; Kumagai et al., 2002b, 2005). The theoretical treatment of slow waves propagating along the fluid-solid interface in a fluid-filled

FIG. 6, cont’d (A) Map of Mt. Etna volcano, with location of a 5-element array operated during summer, 2011. CC and SEC mark the Central Crater and South-East Crater, respectively. (B) Top panel: tremor amplitude from July, 20, through August 31, 2011. Triangles mark paroxysmal lava fountaining episodes from the SEC. The gray band bounds the time interval shown in (C). Middle and bottom panels: Time-varying frequency distributions of propagation azimuth and horizontal slowness, respectively. Colors indicate the number of observations, according to the color scale at the right. Individual frequency distributions represent 1 h of data. (C) Detailed image of tremor amplitude, propagation azimuth and horizontal slowness for a 24-h-long time interval encompassing a lava fountaining episode. (D) Thermal images (top row) and multivariate distribution (2D histogram; bottom row) of source azimuth and depth at four subsequent, 1-h-long time intervals corresponding to the numbered gray patches in (C). Source depth has been determined from back-propagation of horizontal slowness data in the layered model proposed by Saccorotti et al. (2004). (1) Before the paroxysm, array data point to a tremor source located beneath the CC. (2) and (3): The increase of tremor amplitude marks the activation of a second, energetic source located beneath the SEC. This source acts for several hours, in association with weak strombolian activity from the same crater. (4) About 15 h after the activation of the SEC tremor source, activity climaxes with a powerful lava fountain. The same behavior was observed for the remaining lava fountaining episodes. In all the cases, the activation of the tremor source beneath the SEC indicated by array measurements precedes by several hours the paroxysmal episode.

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channel which can sustain long-period resonance of such relatively small sources is given by Ferrazzini and Aki (1987).

4.1 Remarks on moment-tensor inversion of volcanic LP and VLP sources Seismic sources in volcanic environments are usually modeled as a set of equivalent forces acting at the (point) source. These include the force dipoles and couples (moment-tensor (MT); Fig. 3.7 and Eq. (3.23) in Aki and Richards, 2002), and sometimes single forces (SFs) resulting from mass transport beneath a volcano (Ohminato et al., 1998; Takei and Kumazawa, 1995). The source-time history and magnitude of these forces can be obtained by solving the linear inverse problem: d¼Gm

(2)

where d is a vector of data from all stations and components, G is a matrix containing calculated Green’s functions (i.e., the propagation effect for both MT and SFs), and m is the vector containing the sought MT and SF components. The inversion can be performed in either time-domain (e.g., Jolly et al., 2018; Lyons and Waite, 2011; Nakano et al., 2003, 2007, 2008; Ohminato, 2006; Chouet et al., 2003; Kumagai et al., 2002b; Legrand et al., 2000; Ohminato et al., 1998) or frequency domain (e.g., Waite and Lanza, 2016; Eyre et al., 2013, 2015; Zuccarello et al., 2013; Maeda et al., 2011; Cesca et al., 2008; Lokmer et al., 2007; Auger et al., 2006; De Barros et al., 2011). The inversion can also be a priori constrained to a particular type of source mechanism (e.g., a tensile crack or a pipe), where the inversion is then performed for the source orientation and its time history (Lokmer et al., 2007; Nakano and Kumagai, 2005). An example of conceptually different source models (double couple vs. implosive crack) fitting the same LP observations is shown by Woods et al. (2018). The most common inversion procedure is to perform the inversion with and without single forces (and possibly a constrained inversion) and then select the best source model using Akaike information criterion (Akaike, 1974), AIC. This accounts for the bias of naturally better waveforms fit when the number of free model parameters increases. The inclusion of single forces into inversions is due to the mass advection processes beneath volcanoes which can generate such forces (Chouet and Matoza, 2013). However, apart from the obvious cases of recoil force during eruptions (Kanamori et al., 1984), the magnitude estimation of possible single forces due to “silent” advection depends on the number of poorly known in-situ parameters. In addition, as shown for VLP cases by Ohminato et al. (1998) and Chouet et al. (2003), and for LP cases by Trovato et al. (2016), De Barros et al. (2011) and Bean et al. (2008), the uncertainties in both source location and velocity structure usually lead to the reconstruction of strong spurious single forces in the moment-tensor solution; origin

46 Forecasting and planning for volcanic hazards, risks, and disasters

of these forces has been modeled by De Barros et al. (2013). In particular, Trovato et al. (2016) and Bean et al. (2008) show that the inclusion of single forces may completely compromise the retrieved source model (its type, magnitude, and orientation) even if the model is selected as “the best fit model” based on AIC criterion. Furthermore, O’Brien et al. (2010) reported a large underperformance of AIC criterion in the correct model selection when the retrieved source model is not close to the true model. This is usually the case due to large uncertainties in the shallow velocity model and relatively small amount of data compared to the number of free parameters used in inversions. According to the same authors, a partial improvement can be achieved by using Bayesian information criterion (BIC) instead, aided by numerical tests on multiple synthetic data sets. To date, this issue remains unresolved, especially for LP wavelengths which exhibit strong path effects on volcanoes. Another important caveat is that the retrieved source function is very likely just a filtered representation of the true time-history of the source (Maeda et al., 2015; Thun et al., 2015), owing to the limited instrument response and use of the most energetic part of the signal spectra in the inversion. All this implies that caution should be exercised to avoid overinterpretation of the inversion results. Apart from extensive numerical testing, a way forward may lie in performing inversions at the lower end of seismic spectrum, related to the (quasi)static displacement offset, observed at some near-field LP signals (Fig. 7). Detection of such offsets is generally problematic due to the small amplitude of recorded signals, where the lower end of their spectrum is contaminated with natural noise (e.g., microseisms) and instrumental noise (electronic noise, temperature fluctuation). Thun et al. (2015) suggest using either (i) median filter or (ii) stacking similar instrument-corrected unfiltered events to recover displacement steps (Fig. 7). This result shows that we may be missing an important part of LP sources by analyzing only the most energetic part of the signal, and implies the necessity of recording LP waveforms in the near-field. Finally, it is important to mention the contamination of horizontal seismograms with tilt signals within VLP spectrum. As the separation of the translational motion and tilt is difficult and requires strong assumptions (see Wielandt and Forbriger, 1999), Maeda et al. (2011) proposed a new inversion method: the separated Green’s functions for ground translation and rotation are computed and convolved with the appropriate rotational and translational instrument response prior to inversion. The approach was extended and further simplified by Van Driel et al. (2015). The methodology was successfully applied at Asama volcano, Japan (Maeda and Takeo, 2011), Kilauea, Hawaii (Chouet and Dawson, 2013), Fuego, Guatemala (Waite and Lanza, 2016) and White Island volcano, New Zealand (Jolly et al., 2017). The strong effect of tilt on VLP waveforms is illustrated by Fig. 8, showing the synthetic waveforms calculated for an oblique pipe source under White Island Volcano, New Zealand, using the source and stations locations as in Jolly et al. (2018).

FIG. 7 (A) Location of seismic network on Turrialba volcano, Costa Rica in 2009. (B) (Upper panel) Vertical LP event seismogram (velocity) recorded at station CIMA processed with a median (20 s) and band-pass filter (0.3–4 Hz) (black and red lines, respectively); (lower panel) corresponding ground displacement seismograms: note the large displacement step arising from the almost identical velocity waveforms in (A). (C) Stack of 183 unfiltered, instrument-corrected similar LP waveforms at station CIMA. The vertical scale is the same as in the upper panel of (B). For the clarity of visualization, the amplitude of the displacement waveform is exaggerated by a factor 7. Due to the high signal-to-noise ratio in the stack, a step similar to the single event in panel (B) is visible in the unfiltered, integrated stack. (After Thun, J., Lokmer, I., Bean, C.J., 2015. New observations of displacement steps associated with volcano seismic long-period events, constrained by step table experiments. Geophys. Res. Lett. 42 (10), 3855–3862. https://doi.org/10.1002/ 2015GL063924.)

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48 Forecasting and planning for volcanic hazards, risks, and disasters E

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5 Subsurface investigation Volcanic edifices exhibit an extremely complex structure resulting from repeated intrusive and eruptive events. Investigating the spatial distribution and mechanical properties of these materials is of paramount importance for (i) constraining size and location of weak zones and fluid pathways/reservoirs, and (ii) constructing precise velocity models of the subsurface, which are required for reliable hypocenter locations and source inversions. This section aims at presenting a short overview of the main methodologies adopted for imaging the interior of volcanoes.

5.1 Seismic tomography from earthquakes and active sources Seismic tomography is an imaging technique which uses some characteristics of seismic waves traveling between sources and receivers to infer the spatial distribution of the corresponding parameters. Most tomographic applications at volcanoes uses the travel time of body-waves from local earthquakes to obtain images of P- and S-wave velocities (Vp and Vs, respectively). Derived parameters, such as the [Vp,Vs] ratio and product, are diagnostic of the presence of

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fluids and their physical state, respectively (e.g., De Matteis et al., 2008). A complete description of seismic tomography methods is reported in the paper by Rawlinson et al. (2014); specific applications to volcanoes are exhaustively reviewed in Lees (2007) and Koulakov and Shapiro (2015). In addition to seismic velocities, tomographic inversions may yield information on the anisotropy of the propagation medium, providing hints on regional stresses and the spatial setting of structural discontinuities (e.g., Johnson et al., 2011; Johnson and Savage, 2012). A further, relevant parameter which can be imaged is the attenuation of body waves. Once compared to seismic velocity, in fact, attenuation is more sensitive to small-scale medium heterogeneities, damage zones, fluidfilled rock volumes. Examples of 3D attenuation imaging studies at volcanoes include those at Mt. Etna and Vesuvius in Italy (Martinez-Arevalo et al., 2005; De Gori et al., 2005; Del Pezzo et al., 2006; De Siena et al., 2009), Okmok in Alaska (Ohlendorf et al., 2014), Kilauea in Hawaii (Lin et al., 2015) and Deception Island in Antarctica (Prudencio et al., 2015). Tomographic inversions provide well-constrained images of the subsurface only for those regions of the investigated volume which are traversed by a sufficient number of differently-oriented ray segments. As a consequence, tomographic imaging from earthquake body waves may suffer lack of resolution for specific portion of the volcano, due to the heterogeneous distribution of hypocenters. For volcanic islands or volcanoes close to the coast, significant improvements can be obtained by integrating data from local earthquakes with those obtained by marine air-gun shots (Fig. 9). Examples of active studies undertaken include Deception Island in Antarctica (Zandomeneghi et al., 2009), Montserrat in Lesser Antilles (Paulatto et al., 2010), Mt. Etna (Dı´azMoreno et al., 2018) and Stromboli (Patane` et al., 2017) in Italy, Teide in Canary Islands (Garcı´a-Yeguas et al., 2012). All the above works account for body waves whose trajectory is predicted using ray theory. An alternative approach is to investigate the spatial distribution of attenuation and scattering properties of the materials using the late portions of the seismogram (coda waves), which propagate in a diffusive manner. Once combined with the definition of appropriate space-weighting functions (Del Pezzo et al., 2018), these methods allow us to derive attenuation images with improved lateral resolution with respect to ray-dependent techniques (e.g., De Siena et al., 2017, and references therein). The ability of any tomographic inversion to provide useful information about the subsurface structure of a volcano critically depends on several factors. The first, is the unavoidable uncertainty in the estimate of the wave properties (e.g., arrival times, amplitudes) used in the inversion. Since even small errors in the input data may result in large fluctuations of the result, specialized mathematical procedures are adopted to limit the variance of the final image. These methods, which are collected under the generic term of “regularization,” are usually implemented by requiring the final images to not have large gradients. The implication of this approach is that the tomographic results usually exhibit

50 Forecasting and planning for volcanic hazards, risks, and disasters

FIG. 9 (A) Source (dots) and receiver (triangles) geometry for the active tomographic study of Deception Island Volcano, Antarctic Peninsula. (B) Anomalies in P-wave velocity and attenuation (left and right column, respectively). The sharp, NE-SW trending boundary between the high- and low-velocity regions bordering the NW coast of the island has been interpreted by Zandomeneghi et al. (2009) as the major fault zone that marks the passage between the South Shetland crystalline basement and the northwestern limit of the Bransfield Basin. The low-Vp, high-attenuation body in the central part of the caldera has been interpreted by Prudencio et al. (2015) as either a shallow magmatic reservoir or a zone filled by geothermal fluids. (Figures courtesy of J.M. Ibanez, University of Granada, Spain.)

smooth boundaries, being any sharp gradient excluded a priori. The second element is resolution. In addition to the aforementioned ray coverage, resolution is primarily controlled by the frequency bandwidth of the seismic signals used in the inversion. In geometric ray theory, the seismic wavefield is only sensitive to those heterogeneities whose scale length is comparable to the dominant wavelength of the propagating wave. As an example, a 10-Hz (0.1 s period) wave traveling at 5 km/s has a wavelength of 0.5 km, which roughly corresponds to the size of the smallest detectable heterogeneity. A final issue regards the interpretation of the tomographic images, in which one attempts to relate the retrieved seismic attributes (e.g., velocity, attenuation) to the type and physical state of rock materials. For the specific case of volcanic environments, sometime a few simple rules apply: for instance, low velocity and high attenuation of S-waves are ascribed to regions of magma storage and/or fluid accumulation (see the pioneering work by Einarsson, 1978). On the other side, regions of high velocity, high Q (low attenuation) can be attributed to cooled magma bodies. In general, however, tomographic interpretations are inherently non-unique, since the reduction of a multitude of geological materials and processes to a few seismic parameters is unavoidably ambiguous. An exhaustive overview of the results and corresponding interpretations of seismic tomographies on magmatic systems is reported in Table 1 of Lees (2007).

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Shallow velocity structures from surface wave dispersion

Under the assumption that the seismic noise wavefield is stochastic and stationary, it has been demonstrated that the cross correlation of seismic noise recorded by two stations corresponds to the impulse response (Green’s function-GF) of the propagation medium in between the two sites (e.g., Lobkis and Weaver, 2001). In other words, the noise correlation function (NCF) is equivalent to the signal recorded by one of the two stations for an impulsive, virtual source acting at the other. Since seismometers and noise sources are usually placed at the Earth’s surface or very close to it, the noise-derived GFs are dominated by surface waves, whose dispersive properties bring information on the distribution of seismic velocities in the subsurface. The method of interstation correlation originated more than 50 years ago in a seminal paper by Aki (1957), with the formulation of the SPatial AutoCorrelation method (SPAC). SPAC involves the azimuthal averaging of inter-station noise correlations, and as such it requires arrays with semi-circular geometry. Most common SPAC applications in volcanic environments have used shortperiod (0.1–1 s) tremor recordings, permitting the determination of 1D velocity profiles down to depths of a few hundred meters (e.g., Ferrazzini et al., 1991; M
etaxian et al., 1997; Chouet et al., 1998; Saccorotti et al., 2001, 2003, 2004; Mora et al., 2006; Perrier et al., 2012). All the above results have been synthetized by Lesage et al. (2018) to derive a generic model describing the variations of P- and S-wave velocities in the shallowest 500 m of andesitic - basaltic volcanoes (Fig. 10).

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Ambient noise tomography

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Velocity (ms ) FIG. 10 Generic model for the shallow P- and S-wave velocity structure, obtained by Lesage et al. (2018) after averaging 44 seismic velocity models from 11 andesitic-basaltic volcanoes.

52 Forecasting and planning for volcanic hazards, risks, and disasters

consists of three steps: (i) calculation of the GFs of the medium in between two recording sites by averaging NCF estimates over a long period of time; the procedure is then iterated over all the available, independent station pairs; (ii) for each available GF, extraction of the surface-wave dispersion relationship, and calculation of the corresponding inter-station, frequency-dependent travel times; (iii) 2D tomographic inversion of these travel-times to obtain phase and/or group velocity maps at the distinct reference frequencies, with each frequency corresponding to a different penetration depth. Eventually, the phase/ group velocity data from individual nodes of the 2D maps are inverted to obtain a final, 3D model of the shear wave velocity structure. Applications at the local and regional scales take into account ambient noise in the oceanic microseism band (10s < T < 1 s) and station spacings up to several tens of kilometers, which permit resolving velocity structures at crustal level. The first ANT study in a volcanic environment is that by Brenguier et al. (2007), who retrieved a shear-wave velocity model down to depths of about 3 km below the Piton de la Fournaise volcano. A number of applications have followed, including Okmok in Aleutian Islands (Masterlark et al. 2010), Lake Toba in Sumatra (Stankiewicz et al., 2010; Jaxybulatov et al., 2014), Asama in Japan (Nagaoka et al. 2012), Eyjafjallaj€ okull (Benediktsdo´ttir et al., 2017), Torfaj€ okull (Martins et al., 2019) and Katla (Jeddi et al., 2017) in Iceland, Taupo in New Zealand (Behr et al., 2011), Deception in Antarctica (Luzo´n et al., 2011), Campi Flegrei in Italy (De Siena et al., 2018). Shortcomings of the noise-based approaches discussed in the present and previous sections are (i) The requirement for a homogeneous noise source distribution, i.e., the condition that seismic energy has to be equally-partitioned in time and space. Even when averaging the correlation estimates over long time intervals, there’s no guarantee that such condition is attained, and that may results in a biased reconstruction of the GFs. (ii) The need of long continuous records (months to years), or specialized array geometry. (iii) The limited spectral bandwidth of the retrieved GFs, determined by the frequency content of noise. (iv) The fact that the GFs are dominated by surface waves, which poses limits on the depth resolution of the method.

5.4 Temporal changes of medium properties A recent, yet well-established monitoring tool is represented by the measurement of temporal changes of seismic velocity beneath volcanoes. Among the different processes which may cause those changes, are stress variations induced by redistribution of magmatic/hydrothermal fluids at depth. Therefore, their measurement assumes paramount importance for the early detection of intrusive processes, as well as for identifying when an ongoing eruption begins waning. First attempts to estimate time-dependent velocity changes considered repeating velocity tomographic inversions using earthquake catalogs from

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subsequent time intervals (e.g., Patane` et al., 2006). However, that approach suffers from poor accuracy (on the order of percents in relative changes of seismic velocities) and coarse temporal sampling (at best months). In addition, the overall resolution of the method is markedly limited by the influence of uneven ray coverage on the results of the independent inversions conducted over subsequent time intervals (Julian and Foulger, 2010). A most precise approach to measure subtle velocity changes down to fractions of a percent is represented by the coda wave interferometry (CWI) technique. Using the propagation medium as an interferometer, CWI estimates velocity changes by measuring the time delays (or phase shifts) of multiplyscattered waves that sample the medium at different times. Hence, CWI requires a repeating source, which may be constituted by multiplets of similar earthquake (e.g., Ratdomopurbo and Poupinet, 1995; Pandolfi et al., 2006; Zaccarelli et al., 2009; Hotovec-Ellis et al., 2014, 2015), sustained LP events (Battaglia et al., 2012), repeating volcanic explosions (Haney et al., 2009) or air-gun shots (Wegler et al., 2006). The time occurrence of the above sources, however, is sparse and not-predictable, thus implying an irregular sampling of the possible velocity variations. An appealing alternative considers estimating coda-wave time shifts between the NCFs obtained over regularly-spaced time intervals, and a reference NCF usually derived from averaging the whole set of NCFs. This permits monitoring velocity changes with a uniform temporal sampling, whose resolution, however, is limited by the long recordings (days to months) which are required to obtain stable NCF estimates. The current, widespread utilization of the noise-based CWI has been triggered by the groundbreaking study of Brenguier et al. (2008), who identified clear seismic velocity decreases preceding five eruptive episodes at the Piton de la Fournaise volcano, La Reunion. Outcomes from the numerous, following applications are exhaustively synthesized in the review papers by Haney et al. (2014) and Brenguier et al. (2016). Processes controlling velocity changes may not be necessarily related to the internal dynamics of the volcano. For instance, a decrease of seismic velocity in shallow layers may be caused by medium damage in consequence of strong ground motion from large earthquakes (e.g., Battaglia et al., 2012). Other cases account for seasonal trends in the velocity variations, which are likely indicative of changes in snow loading (Hotovec-Ellis et al., 2014), pore pressure in hydrothermal systems modulated by rainfall (Martini et al., 2009), fluid saturation in aquifers (Sens-Sch€ onfelder and Wegler, 2006).

6

Conclusions and future opportunities

It is now well recognized that the interpretation of volcano-seismic signals is best performed within the context of other observations, such as gas geochemistry, ground deformation, gravity field, thermal imaging, infrasound emissions. Nonetheless, seismology still constitutes the pillar of most volcano

54 Forecasting and planning for volcanic hazards, risks, and disasters

monitoring programs, and reliable seismic observations are a necessary element for both tracking the ongoing processes and determining the internal structure of the volcano. While recognizing the tremendous progress in volcano seismology over the past few decades, its future evolution should consider several elements: (i) A technological effort toward a better knowledge of the actual capabilities and limitations of existing instruments, and the adoption of new ones. Therefore, strengthening the interaction with other communities (e.g., astrophysics, fundamental physics, precision metrology) could provide interesting perspectives toward the application of advanced sensing and imaging technologies; (ii) The incorporation of methods for the automated detection, location and classification of volcanic signals into real-time, operational systems; (iii) An improved assessment of the role played by the heterogeneous volcanic edifices in generating and modulating seismicity: this includes failure of the edifice itself, brittle-to-ductile transition zones, identification of volumes of seismic deficiency; (iv) The further development of a theoretical framework for the improved modeling and understanding of the physical processes at the source of the recorded signals. Beyond successful eruption forecasting, the achievements of any monitoring program should also be measured in terms of how and when the relative products are disseminated. Within this context, the earthquake seismology community already counts on well-established sharing policies; conversely, there are still numerous cases of data from volcano observatories or experiments that are not distributed systematically. As a final remark, we, therefore, encourage opening such wealth of information to a wider audience of scientific actors, in accordance with the data interoperability principles. Opening the possibility of applying a diversified set of processing methodologies and interpretative frameworks will hopefully help in improving our knowledge on the numerous, still unresolved issues in volcano seismology.

Acknowledgments The authors are very grateful to Freysteinn Sigmundsson, whose careful revision greatly contributed to improve the quality of the manuscript. Aoife Braiden is sincerely acknowledged for her numerous, thoughtful comments on the early version of the text. Luciano Zuccarello and Jesus M. Ibanez provided data and figures relative to array experiments and tomographic studies, respectively. Louis De Barros provided figure illustrating path effects on Etna volcano; the data were recorded by former Geophysics Group led by Chris Bean, University College Dublin, Ireland.

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72 Forecasting and planning for volcanic hazards, risks, and disasters Wassermann, J., 2012. Volcano seismology. In: Bormann, P. (Ed.), New Manual of Seismological Observatory Practice 2 (NMSOP-2). Deutsches GeoForschungsZentrum GFZ, Potsdam, pp. 1–77. https://doi.org/10.2312/GFZ.NMSOP-2_ch13. Wassermann, J., Ohrnberger, M., 2001. Automatic hypocenter determination of volcano induced seismic transients based on wavefield multichannel wavelet analysis of volcanic signals coherence—an application to the 1998 eruption of Mt. Merapi, Indonesia. J. Volc. Geotherm. Res 110, 57–77. Wegler, U., L€ uhr, B.G., Snieder, R., Ratdomopurbo, A., 2006. Increase of shear wave velocity before the 1998 eruption of Merapi volcano (Indonesia). Geophys. Res. Lett. 33, L09303. https://doi.org/10.1029/2006GL025928. West, M., Sa´nchez, J.J., McNutt, S.R., 2005. Periodically triggered seismicity at Mount Wrangell, Alaska, after the Sumatra earthquake. Science (New York, N.Y.) 308 (5725), 1144–1146. https://doi.org/10.1126/science.1112462. White, R., McCausland, W., 2016. Volcano-tectonic earthquakes: a new tool for estimating intrusive volumes and forecasting eruptions. J. Volcanol. Geotherm. Res 309, 139–155. https://doi.org/ 10.1016/J.JVOLGEORES.2015.10.020. Wielandt, E., Forbriger, T., 1999. Near-field seismic displacement and tilt associated with the explosive activity of Stromboli. Ann. Geophys 42, 407–416. https://doi.org/10.4401/ag-3723. Wiemer, S., McNutt, S.R., Wyss, M., 1998. Temporal and three-dimensional spatial analyses of the frequency-magnitude distribution near Long Valley Caldera, California. Geophys. J. Int 134 (2), 409–421. https://doi.org/10.1046/j.1365-246x.1998.00561.x. Wolfe, C.J., Brooks, B.A., Foster, J.H., Okubo, P.G., 2007. Microearthquake streaks and seismicity triggered by slow earthquakes on the mobile south flank of Kilauea volcano, Hawai’i. Geophys. Res. Lett. 34(23). https://doi.org/10.1029/2007GL031625. Woods, J., Donaldson, C., White, R.S., Caudron, C., Brandsdo´ttir, B., Hudson, T.S., ´ gu´stsdo´ttir, T., 2018. Long-period seismicity reveals magma pathways above a laterally propA agating dyke during the 2014–15 Ba´rðarbunga rifting event, Iceland. Earth Planet. Sci. Lett 490, 216–229. https://doi.org/10.1016/J.EPSL.2018.03.020. Woods, J., Winder, T., White, R.S., Brandsdo´ttir, B., 2019. Evolution of a lateral dike intrusion revealed by relatively-relocated dike-induced earthquakes: the 2014–15 Ba´rðarbunga–Holuhraun rifting event, Iceland. Earth Planet. Sci. Lett 506, 53–63. https://doi.org/10.1016/j.epsl. 2018.10.032. Wyss, M., Klein, F., Nagamine, K., Wiemer, S., 2001. Anomalously high b-values in the South Flank of Kilauea volcano, Hawaii: evidence for the distribution of magma below Kilauea’s East rift zone. J. Volcanol. Geotherm. Res 106 (1–2), 23–37. https://doi.org/10.1016/S0377-0273 (00)00263-8. Yamamoto, M., Kawakatsu, H., Kaneshima, S., Mori, T., Tsutsui, T., Sudo, Y., Morita, Y., 1999. Detection of a crack-like conduit beneath the active crater at Aso volcano Japan. Geophys. Res. Lett 26 (24), 3677–3680. https://doi.org/10.1029/1999GL005395. Yamasato, H., 1997. Quantitative analysis of pyroclastic flows using infrasonic and seismic data at Unzen volcano, Japan. J. Phys. Earth 45, 397–416. Yokoo, A., Ishii, K., Ohkura, T., Kim, K., 2019. Monochromatic infrasound waves observed during the 2014–2015 eruption of Aso volcano, Japan. Earth Planet Space 71 (1), 12. https://doi.org/ 10.1186/s40623-019-0993-y. Zaccarelli, L., Pandolfi, D., Bianco, F., Saccorotti, G., Bean, C.J., Del Pezzo, E., 2009. Temporal changes in seismic wave propagation towards the end of the 2002 Mt Etna eruption. Geophys. J. Int 178, 1779–1788. https://doi.org/10.1111/j.1365-246X.2009.04219.x.

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Zandomeneghi, D., Barclay, A., Almendros, J., Iba´n˜ez, J., Wilcock, W.S.D., 2009. Crustal structure of Deception Island volcano from P-wave seismic tomography: tectonic and volcanic implications. J. Geophys. Res 114, 1–16. https://doi.org/10.1029/2008JB006119. Zecevic, M., De Barros, L., Eyre, T.S., Lokmer, I., Bean, C.J., 2016. Relocation of long-period (LP) seismic events reveals en echelon fractures in the upper edifice of Turrialba volcano, Costa Rica. Geophys. Res. Lett 43 (19), 10,105–10,114. https://doi.org/10.1002/2016GL070427. Zhang, M., Wen, L., 2015. Earthquake characteristics before eruptions of Japan’s Ontake volcano in 2007 and 2014. Geophys. Res. Lett 42, 6982–6988. https://doi.org/10.1002/2015GL065165. Zobin, V.M., 2016. Introduction to Volcanic Seismology, 3rd ed. Vol. 6. Elsevier. Zuccarello, L., Burton, M.R., Saccorotti, G., Bean, C.J., Patane`, D., 2013. The coupling between very long period seismic events, volcanic tremor, and degassing rates at Mount Etna volcano. J. Geophys. Res. Solid Earth 118 (9), 4910–4921. https://doi.org/10.1002/jgrb.50363.

Chapter 3

Volcano geodesy: A critical tool for assessing the state of volcanoes and their potential for hazardous eruptive activity Michael P. Polanda and Elske de Zeeuw-van Dalfsenb a

U.S. Geological Survey, Cascades Volcano Observatory, Vancouver, WA, United States, bRoyal Netherlands Meteorological Institute (KNMI), R&D Department of Seismology and Acoustics, De Bilt, Netherlands

1

The ups and downs of volcanoes

Geodesy is the study of Earth’s size, shape, orientation, and gravitational field, and how those properties change over time. It is these changes in both shape (called deformation) and gravity that make geodesy such a powerful tool for monitoring volcanic activity and assessing volcanic hazards. That volcanoes deform in association with eruptive activity has been understood for centuries to millennia, as demonstrated emphatically by uplift and subsidence of the town of Pozzuoli within the Campi Flegrei caldera, Italy. Exceptional changes in ground elevation are recorded there at the Roman site Serapeum, which was constructed above sea level over 2000 years ago and is above sea level now, but must have been as much as 6–7 m below sea level at some point in time given the presence of mollusk borings in the site’s marble columns (Fig. 1) (Dvorak and Berrino 1991; Del Gaudio et al., 2010). Uplift of previously submerged coastal areas around Pozzuoli in the centuries prior to the 1538 eruption of Monte Nuovo (Di Vito et al., 2016) is indicated by royal edicts that were needed to address the ownership of this new land (Dvorak and Gasparini 1991). In his Principles of Geology, Charles Lyell (1830) connected the uplift and subsidence of volcanic areas, as seen in Pozzuoli, to magmatic activity at depth—uplift being caused by pressure increases, and subsidence a result of cooling subterranean magma. From these humble beginnings, volcano geodesy has grown into one of the major pillars of volcano surveillance and research, with tremendous utility for Forecasting and Planning for Volcanic Hazards, Risks, and Disasters https://doi.org/10.1016/B978-0-12-818082-2.00003-2 Copyright © 2021 Elsevier Inc. All rights reserved.

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FIG. 1 Marble columns at the Roman site Serapeum, in Pozzuoli, near the center of Campi Flegrei caldera in Italy. Mollusk borings are present several meters high on the columns, indicating that the site subsided well below sea level after its construction and then was uplifted above sea level. (Photo by Elske de Zeeuw-van Dalfsen, September 5, 2018.)

the assessment of volcanic hazards and understanding of subterranean processes. Surface deformation and gravity change (also called microgravity) provide insights into the location of subsurface magma reservoirs, their volumes, and their changes over time, which relate directly to the potential for future eruptive activity. Geodesy thus has a strong role to play in forecasting hazardous volcanic phenomena. In addition, geodetic data can be used in nontraditional ways to map volcanic hazards, such as lava flows and ash plumes.

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In this chapter, we briefly review past and modern methods for measuring geodetic change at volcanoes and discuss how geodetic data can be used to support volcanic hazards assessment through forecasting eruptions and tracking their evolution. We continue by addressing the challenges in the application of geodetic methods to active volcanism and describe novel uses of geodetic data with respect to volcanic hazards. We then demonstrate the power and pitfalls of volcano geodesy through case studies that include active and quiescent volcanoes, and we conclude with a discussion of the future of the field. Although we focus on geodesy, we acknowledge that volcanology—volcano monitoring in particular—is a multidisciplinary science. No one tool—seismology, geochemistry, geology, etc.—is better than all the others, and the best practice utilizes a multifaceted approach to better understand volcanic activity.

2

Measuring deformation and gravity change

Here, we give a brief summary of the history of deformation and gravity change measurements at volcanoes to provide a context for subsequent discussions of volcano geodesy and volcanic hazards. For a more thorough discussion of geodetic techniques used on active volcanoes, see Dzurisin (2007). A number of references also discuss the application of geodesy to specific volcanic regions, like Hawai‘i (Decker et al., 2008) and Iceland (Sturkell et al., 2006).

2.1

“Classic” volcano geodesy

It was not until the early 1900s that deformation of Earth’s surface was specifically used as a tool for assessing magmatic activity at active volcanoes. The first documented attempt to quantify volcano deformation was at Campi Flegrei, where a leveling line was established by the Instituto Geografico Militare in 1905 and remeasured sporadically thereafter, recording subsidence until about 1950 and several episodes of uplift since (Del Gaudio et al., 2010). At about the same time, Fusakichi Omori, who is perhaps better known for his contributions to seismology (Omori, 1894), was measuring surface deformation at Usu and Sakurajima, Japan. Omori (1913) utilized leveling data to determine that, in addition to the “new mountain” that formed as part of the 1910 eruption of Usu, areas away from the locus of activity experienced a mixture of co-eruptive uplift and subsidence—a deformation pattern that reversed itself in the year following the eruption. At Sakurajima, tide gauge data revealed uplift of the volcano prior to its 1914 eruption, and leveling and triangulation indicated subsidence of the volcano associated with the eruption. This deformation was probably a result of magma accumulation and subsequent evacuation (Omori, 1916). In Hawai‘i, Thomas Jaggar, founder of the Hawaiian Volcano Observatory in 1912, observed, starting in 1913, “the wanderings of the writing points, from the median line, of the two-component Bosch-Omori seismograph” at Kīlauea Volcano. He confirmed that these “wanderings” were

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due to ground tilt by comparing them to leveling data, and he related the tilt to seismicity and volcanic activity (Jaggar and Finch, 1929). This tilt record might represent the first continuous deformation monitoring of any volcano. Surface displacements over broader areas were recorded by leveling and triangulation, quantifying substantial caldera subsidence associated with a collapse event in 1924 (Wilson, 1935). The quality of the data from both Sakurajima’s 1914 eruption and Kīlauea’s 1924 collapse, and the spectacular nature of both episodes of activity, later led Mogi (1958) to use the two examples to demonstrate the application of his point-source model for subsurface pressure variations—the most widely applied analytical model in volcano geodesy. Land surveying techniques, like leveling (Fig. 2A) and triangulation, dominated volcano deformation monitoring for the first half of the 20th century. A major advance occurred in the mid-1960s with the advent of Electronic Distance Measurements (EDM; Fig. 2B), which is a form of trilateration (Decker et al., 1966). EDM measures the two-way travel time of a laser between an instrument and a reflector to infer the distance between the two points. When the reflector is permanently installed, the measurement can be completed by a single operator working at a relatively safe distance from the volcano’s summit. In terms of tilt, “seismometric” techniques (using the deviation of a pendulum seismometer) were common until the development of the water-tube tiltmeter in the mid-1950s (Fig. 2C; Eaton, 1959). Although not a continuous method, “wet” tilt (so-called to distinguish from “dry tilt” methods that were essentially small leveling arrays) overcame many of the drawbacks associated with seismometric tilt and was “portable.” This allowed for measurements at multiple locations, as well as the development of lengthy records (Fig. 2D) that aided in the interpretation of volcanic activity at such places as Kīlauea (e.g., Wright and Klein, 2014). Electronic tilt measurements became possible in the mid-1960s, gradually evolving from uniaxial surface-mounted devices to biaxial borehole instruments (Okamura et al., 1988; Decker et al., 2008). The first gravity measurements of volcanic unrest were completed in the early 1950s, when several hundred microgals of gravity change were measured at Izu-Oshima, Japan (Iida et al., 1952). Unfortunately, that and much subsequent work was complicated by a lack of elevation control, which prevented distinguishing subsurface mass change from vertical deformation (both of which result in gravity change; Carbone et al., 2017). When combined with deformation measurements, however, the great power of gravity data becomes clear, especially as a means of detecting activity that might otherwise be missed, for example, magma filling or evacuating void space (e.g., Dzurisin et al., 1980). Nevertheless, techniques for measuring gravity variations changed little during the second half of the 20th century, mostly relying on spring-based gravimeters (Carbone et al., 2017) used to conduct campaigns at measurement stations spread across a volcano (e.g., de Zeeuw-van Dalfsen et al., 2005).

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FIG. 2 Classical methods used in volcano geodesy. (A) Leveling by Cascades Volcano Observatory staff in the Three Sisters Wilderness of Oregon. (B) Recording an Electronic Distance Measurement reading with the laser pointed toward Mayon volcano, Philippines. (C) Water-tube tiltmeter located at the summit of Kīlauea Volcano. Water flows through the lower tube between the pots at either end. The upper tube carries air to equalize pressure at the pots. Changes in the relative water levels of the two pots give tilt over time. (D) Tilt radial to the summit of Kīlauea recorded by the water-tube tiltmeter pictured in part (C) spanning 1956–2018. Positive change is consistent with the inflation of the volcano and negative with deflation. ((A) Photo by Michael Poland, September 2002. (B) Photo by Ernesto Corpuz, January 29, 2000. (C) Photo by Michael Poland, December 23, 2011.)

80 Forecasting and planning for volcanic hazards, risks, and disasters

2.2 “Modern” volcano geodesy Volcano geodesy arguably entered the “modern” age with the advent of digital and space-based techniques. “Classic” techniques, like EDM and leveling, were replaced by Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) (Fig. 3). This transition began in the 1980s, when GNSS surveys were completed alongside EDM measurements, and took firm hold in the 1990s, as continuous GNSS networks were deployed at volcanoes around the world, and InSAR began to flourish thanks to the launch of multiple SAR satellites. Borehole tiltmeter and strainmeter deployments also expanded, which enabled the monitoring of subtle volcanic deformation due to the increased sensitivity of these instruments. These developments revealed new patterns of ground motion that had not been detected previously despite the excellent deformation networks that were in place at some volcanoes, like Kīlauea (Cervelli and Miklius, 2003). The technological innovations of the 1980s and 1990s led to a leap in volcano geodesy through (1) measurement of absolute three-dimensional displacements, (2) better spatial resolution, (3) better temporal resolution, (4) reduction in personnel needs for data collection, and (5) increased measurement accuracy. These developments brought about several fundamental changes in the field of volcano geodesy with direct relevance to volcanic hazards. First, the database of known deforming volcanoes expanded rapidly. InSAR, in particular, facilitated explorations of entire volcanic arcs, revealing numerous deforming volcanoes that might not otherwise be known (e.g., Pritchard and Simons, 2002, 2004;

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FIG. 3 Modern methods of volcano geodesy applied to Mauna Loa volcano, Hawaii. Both maps cover the same area and span the same time (August 22, 2014, to June 8, 2016). The summit caldera and smaller craters and fissures are marked by black lines. (A) Global Navigation Satellite Systems (GNSS) stations are moving away from the volcano’s summit caldera, which is consistent with the pressurization of the magma system. Uncertainty on the measurements is less than 1 cm. (B) The colored fringes in this interferogram indicate line-of-sight inflation. Their pattern suggests that the magmatic system includes a tabular magma body elongated along the length of the summit caldera and extending south of the caldera boundary.

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Chaussard and Amelung, 2012; Ebmeier et al., 2013; Lu and Dzurisin, 2014). As a result, the number of documented instances of volcano deformation worldwide grew from 44 in 1997 to over 220 in 2016 (Biggs and Pritchard, 2017), and it became possible to demonstrate the strong association between volcano deformation and eruption (Biggs et al., 2014). Second, with better data at more volcanoes, models of volcanic deformation expanded beyond simplistic representations (like that of Mogi, 1958) to models that account for complexities in topography, source characteristics, and rheology (e.g., Masterlark and Lu, 2004; Masterlark et al., 2012; Segall, 2016). Such models enable a better understanding of the plumbing systems at individual volcanoes—an important framework for any analysis of volcanic unrest. Third, geodetic data have become a critical tool for timely eruption forecasts by providing indications of changes in magma storage and transport conditions, sometimes over timescales of only minutes (Linde et al., 1993; Sturkell et al., 2006, 2013). Improvements in the temporal resolution and timeliness of geodetic data are particularly noteworthy, especially with regard to GNSS. While high-rate (i.e., sampling rates of 1–60 s) data from tiltmeters are more straightforward in terms of processing and analysis, the three-dimensional nature of GNSS data provides improved constraints on ground motion. Significant work has been dedicated to developing techniques for increasing the accuracy of high-rate (with sampling on the order of minutes to seconds), real-time GNSS for volcano monitoring (e.g., Larson et al., 2001, 2010). These approaches are not yet commonly used by volcano observatories because real-time data are often not available. Nevertheless, an increasing number of eruptions have demonstrated the importance of real-time high-rate GNSS as a monitoring tool—for example, at Mount St. Helens during 2004–8 (LaHusen et al., 2008) and Kīlauea in 2018 (Neal et al., 2019). Microgravity measurements evolved along a path similar to that of GNSS. Especially prior to the 2000s, the vast majority of microgravity studies were completed in campaign mode, with measurements conducted at fixed observation points and repeated as needed—often annually or every few years—to assess changes (e.g., de Zeeuw-van Dalfsen et al., 2005; Johnson et al., 2010). Since the start of the 21st century, however, technological innovation has facilitated continuous measurements at fixed locations. These data have imaged dynamic processes, like dike intrusions (Branca et al., 2003), and have also been used to uniquely determine physical properties, like the density of a lava lake (Carbone et al., 2013). Spring-based gravimeters experience sensor drift that prevents interpretation of continuous records that are longer than days to weeks (e.g., Poland and Carbone, 2018), but the development of semiconducting (Carbone et al., 2019) and absolute (Carbone et al., 2017) gravimeters addresses this issue. Lower costs and power consumption may be achieved by the future deployment of gravimeters based on microelectrical mechanical systems (MEMS), which can be used for both campaign and continuous measurements (Middlemiss et al., 2016, 2017).

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The “modern” volcano geodesy toolkit thus consists of an array of instruments and techniques that vary in cost and capability. Ground-based sensors include GNSS, tiltmeters, strainmeters, and gravimeters that can detect changes on the order of millimeters, microradians, nanostrains, and microgals, respectively, over seconds to years. Nearly a dozen SAR satellites have been launched since the early 1990s, with varying ground resolutions and wavelengths that enable imaging of deformation that spans spatial scales from meters to hundreds of kilometers and occurs in environments from desert to rainforest (Pinel et al., 2014). Continuously operating gravimeters can characterize variations in magmatic activity that are hidden from other methods and include such processes as gas segregation and the dynamics of dike emplacement (Carbone et al., 2017). These tools provide the backbone for models that can be used to forecast volcanic activity, and they are complementary in terms of spatial, temporal, and signal sensitivity. None of the techniques alone can image all spatiotemporal and amplitude scales of geodetic change at volcanoes. For example, borehole tilt is excellent for measuring small changes over periods of minutes, whereas InSAR is optimal for detecting less subtle changes that occur over days to years. It is, therefore, vital that geodetic monitoring of volcanoes makes use of a variety of techniques to ensure sensitivity to the wide range of signal strength, spatial extent, and temporal evolution that characterizes deformation and gravity change associated with active volcanism.

3 Forecasting volcanic activity with geodesy In forecasting volcanic eruptions, geodesy has always been the little brother to seismology—earthquakes are typically thought of as the first and/or best indicator of a potential eruption. As Dzurisin (2003) pointed out, however, deformation might be expected to precede seismicity, since a material needs to bend (deformation) before it can break (earthquake). In fact, aseismic deformation has been recognized in several locations, lending credence to this perspective (e.g., Lu and Dzurisin, 2014), and global analysis of volcano deformation shows a strong correlation between deformation and eruption (Biggs et al., 2014). As one of the major legs of the volcano-monitoring stool (with seismology, geology, and geochemistry), geodesy provides a critical perspective in forecasting the onset and evolution of volcanic eruptions.

3.1 Eruption onset A fundamental goal in volcanology is predicting the onset and character of eruptions—that is, prior knowledge of eruption timing, location, and magnitude. The best example might be the series of successful predictions of dome-building eruptions at Mount St. Helens during 1980–86, in which deformation monitoring played a prominent role (Swanson et al., 1983). This success, however, was based entirely on the recognition of repeating patterns. Consistent predictions in the

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Distance change (mm)

absence of repeatable behavior remain elusive. Forecasts, especially probabilistic, are a more realistic goal (compared to predictions), and one that can certainly be informed by volcano geodesy (e.g., Segall, 2013). Before a volcano can erupt, magma must accumulate and pressure must build in the subsurface, usually causing surface deformation. There are numerous cases of pre-eruption deformation across a range of temporal and spatial scales. Persistent, low-level inflation, accompanied by seismicity, occurred in the months prior to eruptions of the Alaska volcanoes Augustine in 2006 (Cervelli et al., 2006) and Redoubt in 2009 (Grapenthin et al., 2013), although the latter signal was only recognized in retrospect (Fig. 4). Multiple episodes of inflation and seismicity also characterized the years prior to the 2010 eruption of Eyjafjallaj€ okull, Iceland, indicating discrete magmatic pulses to the volcano’s plumbing system (Albino and Sigmundsson, 2014; Hjaltado´ttir et al., 2015). At Hekla, Iceland, geodetic data reveal both persistent

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FIG. 4 GNSS data spanning pre- and co-eruption phases of activity at Augustine (top) and Redoubt (bottom) volcanoes in Alaska. Red lines indicate the approximate onset of phreatic eruptions, and red shading marks a time period of magmatic eruption. The Augustine data show the change in distance between two GNSS stations, AV02 and AV03, about 3 km south and 2.5 km north of the volcano’s summit, respectively. AV03 was destroyed by eruptive activity in late January. The increase in distance, starting in August 2005, is an indication of inflation, which Cervelli et al. (2006) modeled as due to a pressure source about 1.5 km beneath the volcano’s summit. Redoubt GNSS data show the north component of displacement from GNSS station AC17, about 28 km northeast of the volcano’s summit. The northward motion of the site in the months prior to the onset of the eruption in 2009 was interpreted as an indication of inflation due to pressure increase in a source about 14 km deep (Grapenthin et al., 2013). The northward transient of November-December 2008 is an environmental artifact and does not reflect ground deformation.

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long-term and transient short-term deformation that is precursory to eruptions. Steady inflation occurred prior to the 1991 eruption, between the 1991 and 2000 eruptions, and following the 2000 eruption (Sturkell et al., 2006), while rapid transient inflation, in addition to seismicity, was detected by strainmeters minutes before each of those eruptions (Linde et al., 1993; Sturkell et al., 2013). At many volcanoes, especially prior to the 2000s, deformation was discovered only after the onset of seismic unrest, since seismicity often stimulated the installation of deformation monitoring or the examination of existing deformation records. InSAR, however, proved to be a game-changer in this regard by facilitating low-cost reconnaissance of broad areas for signs of deformation. This development led directly to the recognition that some volcanoes inflate aseismically, like South Sister, Oregon (Wicks et al., 2002), and several volcanoes in Alaska, including Makushin, Westdahl, Okmok, Akutan, Seguam, and Peulik (Lu and Dzurisin, 2014). Early detection of magma accumulation before the onset of vigorous unrest provides valuable time to develop hazard mitigation strategies and to establish robust ground-based monitoring (e.g., Dzurisin et al., 2006). InSAR surveys of volcanic arcs have been successful at imaging numerous instances of volcano deformation that might not otherwise be known (e.g., Pritchard and Simons, 2002, 2004; Chaussard and Amelung, 2012; Ebmeier et al., 2013; Lu and Dzurisin, 2014), in some cases motivating additional deployment of ground-based sensors, as at Cordo´n Caulle, Chile (Pritchard et al., 2018). A strength of InSAR is also the ability to image deformation that is of very limited spatial extent—that is, hundreds of meters to a few kilometers across—that would probably not be detected by GNSS or other sensors (e.g., Richter et al., 2013) and that can help to identify potential locations of future small, but still hazardous, explosions (e.g., Salzer et al., 2014; Kobayashi, 2018; Kobayashi et al., 2018). Despite the great success of InSAR in identifying volcanoes that are deforming, ground-based monitoring remains essential for detecting the entire spectrum of volcano deformation—from slow, small, and broad displacements to rapid, large, and focused ground motion. Most volcanoes are covered by snow, ice, and/or vegetation and have highly variable atmospheric conditions, all of which can “hide” subtle signals from detection by InSAR. The slow monthslong inflation that was precursory to the 2009 eruption of Redoubt (Fig. 4), for example, was not seen by InSAR (Lu and Dzurisin, 2014). At the other extreme, some volcanic eruptions are preceded by only a few minutes of transient surface displacements that cannot be detected from space on a time scale that is useful for hazard mitigation. For instance, immediate preeruptive strain and seismic sequences at Hekla might only last about 30 min (Linde et al., 1993; Sturkell et al., 2013), and a change in tilt was (again retrospectively) seen 8 min prior to a small phreatic eruption which killed about 60 people at Ontake, Japan, in 2014 (Maeda et al., 2017). If data are available in real-time and hazardous signals are recognized, ground-based data could provide enough time to issue a warning to “take cover.”

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Serendipitous InSAR acquisitions may, however, capture some preeruptive magma migration events “in the act” (e.g., Bagnardi et al., 2013). As an example of such a situation, a SAR acquisition during the intrusion of a dike into the lower East Rift Zone at Kīlauea during late April and early May 2018, was downloaded, processed, and made into an interferogram 48 h before the onset of the May-August eruption (Neal et al., 2019). The interferogram (Fig. 5) revealed the extent of the intrusion, which was hinted at but not fully mapped from ground-based deformation measurements. The interferogram thereby provided valuable insight to the scientists and emergency managers tasked with hazards assessment and mitigation. Typically, however, downlink, processing, and interpretation require more time than is available during a rapidly evolving crisis (Pritchard et al., 2018). For this reason, ground-based instrumentation, particularly GNSS and tiltmeter stations, will always be critical, especially for real-time hazards assessment. Geodetic tools that can aid with eruption forecasting are not only limited to deformation observations but also include gravity records. The great power of gravity measurements is that the technique is sensitive to subsurface processes that might not result in surface deformation. A growing body of research has identified gravity signals that are spatially and/or temporally independent of deformation (see Carbone et al., 2017, for a summary), some of which have April 19 - May 1 (16:30 local time), 2018 Pāhoa Eruption site

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FIG. 5 Sentinel-1 interferogram of Kīlauea Volcano, Hawai‘i, spanning April 19–May 1, 2018, with acquisitions at 16:30 Hawaiian Standard Time on both days. Displacements in the interferogram include a line-of-sight subsidence in the south part of Kīlauea caldera and of the middle East ¯ ‘o eruptive vent due to magma drainage, and line-of-sight uplift in Rift Zone centered on the Pu‘u ‘O the lower East Rift Zone due to magma intrusion. The interferogram, combined with GNSS and seismic data, revealed the spatial extent of deformation associated with a major dike intrusion and was available more than a day before the onset of the 2018 lower East Rift Zone eruption (which started at the location of the red star at about 17:15 Hawaiian Standard Time on May 3).

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implications for forecasting. For example, at Etna, anticorrelation between seismic tremor amplitude and continuous gravity (with tremor increasing as gravity decreases) has been interpreted as reflecting the sudden accumulation of volatiles at shallow levels in the volcanic plumbing system, and on some occasions was associated with changes in eruptive style (Carbone et al., 2006, 2008). These processes are not easily detectable by other monitoring techniques, making the case for both campaign and continuous gravity measurements as a means of assessing long- and short-term (respectively) variations in shallow magma storage characteristics (Carbone et al., 2017).

3.2 Eruption evolution In addition to their utility in forecasting eruption onset, geodetic data offer important insights into how an ongoing eruption might vary over time and space—critical for understanding potential changes in hazards that might impact populations and/or infrastructure. An excellent example of this capability is provided by Kīlauea, which erupted lava almost continuously from 1983 until a major summit collapse and flank effusion in 2018. Over the course of the 35-year-long eruption, changes in the location of the eruptive vent were often presaged by inflation at the eruption site and/or the summit, providing a means of anticipating the onset of hazardous activity (e.g., Poland et al., 2008, 2016; Lundgren et al., 2013; Orr et al., 2015; Patrick et al., 2015). When lava threatened the village of Pahoa during 2014–5, subtle deflation and inflation of the summit measured by tiltmeters correlated, after a lag of hours, with respective decreases and increases in lava effusion and advance rates. Summit deformation, therefore, indicated potential future changes in lava flow hazards (Poland et al., 2016). Geodetic data were also vital for tracking the evolution of Kīlauea’s 2018 lower East Rift Zone eruption and summit collapse. As with previous episodes of new vent formation, the first indication that a change in the long-term erup¯ ‘o, which began in midtion might be forthcoming was inflation of Pu‘u ‘O ¯ ‘o March 2018 (Fig. 6, top, red dots). Continued pressurization at Pu‘u ‘O prompted the Hawaiian Volcano Observatory to issue a public notice on April 17 that a change in the character of the eruption was expected and might involve an intrusion and the formation of a new eruptive vent. A second public notice was issued on April 24 in response to summit inflation (Fig. 6, top, blue dots) and overflow of the lava lake at that location (Neal et al., 2019). While the magnitude of the ensuing change in the long-term eruptive activity was not necessarily seen as the most likely outcome, certainly the formation of a new eruptive vent was deemed a probable development, and deformation data were vital to that assessment. Once the new vent formed on Kīlauea’s lower East Rift Zone, both InSAR and GNSS were instrumental in recognizing that the dike was not pressurizing after mid-May and was unlikely to propagate further downrift (Fig. 6, bottom, black dots). At Kīlauea’s summit, real-time, high-rate GNSS was vital for tracking caldera collapse (Neal et al. 2019), which evolved from

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FIG. 6 GNSS-measured displacements before (top) and during/after (right and bottom) the 2018 summit collapse and flank effusion at Kīlauea Volcano, Hawai‘i. Flank lava flow is indicated by a red polygon on the map. Blue points show distance changes between summit GNSS stations UWEV and AHUP (blue circles on map), with distance increases interpreted as inflation and decreases as ¯ ‘o GNSS stations PUOC and JCUZ deflation. Red points show distance changes between Pu‘u ‘O (red circles on map). Black dots are north displacement at GNSS station NANT (black circle on map), with northward motion interpreted as due to dike opening in the lower East Rift Zone. Green line is the vertical displacement at GNSS station CALS (green circle on map) for July 4 12:00 to July 8 12:00 local time, demonstrating the sudden offsets that were typical of near-daily downdropping events during June to early August. The dashed line on the map gives an approximate trace of the volcano’s East Rift Zone. Dotted lines in top plot give the times that the Hawaiian Volcano Obser¯ ‘o (April 17) and summit (April 24) inflation vatory issued information statements regarding Pu‘u ‘O and that a change in the eruption was likely within days to weeks (Neal et al., 2019).

mostly steady subsidence in May to a series of near-daily downdropping events focused on the subsiding caldera block itself in June to early August (Fig. 6, bottom, blue dots), interspersed with subsidence that increased in rate in the hours before each downdropping event (Fig. 6, right, green line).

3.3

Forecasting challenges

For the many successes in the use of geodesy for forecasting eruptions and tracking eruption evolution, there remain considerable challenges. First and

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foremost, there are numerous cases where the “expected” deformation, which would reflect magma accumulation, is not seen prior to an eruption. Mount St. Helens provides a well-known example. Deep-seated inflation was not measured prior to the 1980 eruption, but this might be explained by the limited number of monitoring stations and their poor geometry prior to the onset of seismic unrest 2 months before the climactic eruption; during the 2 months of seismicity, the only deformation was localized bulging of the volcano’s north flank (Dzurisin, 2000, 2018). Deformation monitoring of the volcano improved, both in terms of spatiotemporal coverage and sensitivity, in the decades that followed, yet once again there was no obvious indication of inflation prior to the onset of a dome-building eruption during 2004–8. The only significant ground motion associated with that eruption was broad deflation that began coincident with the onset of seismicity about 1 week before the first phreatic explosion, and highly localized uplift in the vent area about 2 weeks before the first appearance of lava at the surface (Dzurisin, 2018). Why was there no obvious inflation of the volcano prior to the onset of eruptions in 1980 and 2004? Was there no magma accumulation at depth? Was any pressurization too small to be detected by the available instrumentation? Or is there something fundamental that we do not understand about how Mount St. Helens works? Another poignant example is provided by the 2015 volcano explosivity index (VEI) 4 eruption of Calbuco, Chile. Limited ground-based deformation monitoring was in place prior to the activity, and there is a long history of InSAR observations of the volcano, yet no deformation was detected in the decades prior to the eruption (Delgado et al., 2017) despite evidence that an influx of hotter magma may have triggered the activity (Morgado et al., 2019). It may be that some volcanoes just don’t produce measurable deformation in the weeks to years prior to significant eruptions (like at Calbuco), or that the deformation only occurs when the magma has already risen to very shallow depths (like at Mount St. Helens). Deformation may also be muted or “hidden” due to the physical properties of the magmatic system and host rock. Material heterogeneities can alter the magnitude of deformation that is expected for a given source strength (e.g., Masterlark, 2007), and the inherent compressibility of a volatile-rich magma can result in much less deformation than is expected for a given change in volume (e.g., Rivalta and Segall, 2008). These scenarios should be in kept mind when considering geodetic data in eruption forecasting. The antithesis of the problem of “no detected deformation” is that there is measurable deformation, but no one is aware of it until the volcano has entered a period of enhanced unrest. With the proliferation of GNSS networks and the now-ubiquitous availability of InSAR, there is a risk that a flood of data can overwhelm analysts, and so deformation may go unnoticed. Automated detection algorithms could be implemented to scan deformation data for anomalies but may result in numerous false positives owing to, for instance, atmospheric artifacts, or, of more concern, false negatives if surface displacements are not recognized as such.

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An open question is whether geodetic data—or any monitoring data, for that matter—can be used to forecast the size and style of a future eruption. Outside of well-known cases of pattern recognition—like dome-building eruptions of Mount St. Helens in the 1980s (Swanson et al., 1983)—eruption sizes have, perhaps arguably, never been forecast from monitoring data alone, but instead are inferred from global databases of eruptions and their manifestations (e.g., Ogburn et al., 2015; Cassidy et al., 2018) or from the geologic record (e.g., Punongbayan et al., 1996). Gravity and deformation data can, however, provide unique information concerning the mass and volume of magma accumulating beneath a volcano. Even so, there is no compelling evidence that these data provide a strong indication of the style or size of any future eruption. Here again, Kīlauea in 2018 is an excellent example—why did the inflation at ¯ ‘ Pu‘u ‘O o in 2018 culminate in the largest eruption in the past 200 years, rather than a more typical (based on past patterns) new eruptive vent within a few kilo¯ ‘ meters of Pu‘u ‘O o (Neal et al., 2019)? These questions are not unique to geodesy but are rather more general problems for volcanology (Papale and Marzocchi, 2019). Like any monitoring method, geodesy is not a “magic bullet” for eruption forecasting. Used in combination with other techniques like seismology and gas geochemistry, however, geodesy is a powerful means of assessing future and ongoing eruptive activity.

4

Limitations of geodetic data

Although geodesy has considerable advantages for volcano surveillance, one must be cautious not to overlook pitfalls that can lead to incorrect assessments of volcanic activity. Users of geodetic data must be cognizant of these limitations, which affect both data collection and interpretation, especially as they relate to volcanic hazards assessments.

4.1

Data collection

The challenges related to the collection of geodetic data are threefold: (1) Are we looking in the right place? (2) Are we looking at the right time? (3) Are we using the right tools? In terms of the “right place,” it has generally been assumed that magmatic sources are located beneath volcanic summits; thus, ground-based monitoring has traditionally focused on volcanic edifices. With its better spatial coverage, InSAR has revealed that deformation due to magma accumulation or withdrawal is often off-center from the volcanic edifice, for example, at South Sister, Oregon (Wicks et al., 2002), and Nevado del Ruiz, Columbia (Lundgren et al., 2015). In fact, an analysis of global volcano deformation found that nearly a quarter of all displacements attributed to magmatic sources were centered more than 5 km from the nearest volcanic vent (Ebmeier et al., 2018). In some cases, it may even be difficult to attribute a deformation source to a

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specific volcano. Inflation detected by InSAR near Hualca Hualca, Peru, for instance, may actually be associated with Sabancaya volcano, which is 7 km distant (Pritchard and Simons, 2002). Timing is equally important. If not done at the right time with respect to a deformation event, geodetic surveys might “miss” important displacements. Ebmeier et al. (2018) found that transient deformation events lasting less than 1 month were underrepresented in volcano deformation databases—perhaps not surprising given the paucity of continuous deformation monitoring of volcanoes worldwide and the weeks-to-months repeat intervals of most SAR satellites until the 2010s. At Kīlauea, it took the installation of continuous GNSS stations to realize that slow-slip events were occurring on the volcano’s south flank; campaign GNSS in the same region failed to identify the displacements (Cervelli et al., 2002). The importance of such events cannot be overstated— recognition of slow slip at Kīlauea was critical to better understanding the feedback between volcanic and tectonic activity at the volcano (Montgomery-Brown et al., 2010, 2015). The third challenge involves the issue of the “right tools.” Some surface displacements may occur at rates that are not detectable by certain measurement types—this may explain why Ebmeier et al. (2018) also found that longduration (more than 5 years) deformation episodes are poorly represented in global databases. Displacements and gravity changes that occur slowly require long time series, which can be expensive to maintain and difficult to justify if there are no obvious changes on few-year time scales. Steady gravity decreases at Askja volcano, Iceland, for example, might not have been recognized with only a few years of data, but measurements spanning multiple decades were able to distinguish that subsidence of the volcano was associated with a decrease in gravity, indicating source processes that included both magma withdrawal and cooling/contraction (de Zeeuw-van Dalfsen et al., 2005). Some ground displacements may also be too small to detect without specialized instrumentation. Cyclic deflation-inflation tilt events at Kīlauea’s summit began to be recorded shortly after the installation of borehole tiltmeters in 1999, and the events were clearly associated with surges and lulls in ¯ ‘ eruptive activity from the Pu‘u ‘O o eruptive vent (Cervelli and Miklius, 2003; Anderson et al., 2015). Just because geodetic change has not been detected on a given volcano does not necessarily mean that there is no magmatic deformation or gravity variation to be measured.

4.2 Data interpretation The primary challenges that stand in the way of accurate interpretation of deformation and gravity change include identifying volcanic signals in the presence of environmental noise, separating volcanic from nonvolcanic processes, and extracting useful information from models of geodetic change. The question of whether measured geodetic signals reflect actual ground motion or gravity change, or are rather a result of some artifact, has persisted

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since geodesy was first applied to active volcanoes. For example, Wilson (1935) highlighted the challenge with respect to leveling data from Kīlauea, pointing out that uplift of the summit area relative to the lower flank over the course of 1912–21 was probably a result of a scale error in the leveling rods used in 1912. EDM surveys likewise require careful assessment of atmospheric parameters like humidity, temperature, and pressure—not just at the endpoints, but ideally along the path of the EDM line—to fully account for sources of error (Iwatsubo and Swanson, 1992). The same types of limitations are present in “modern” geodetic techniques. Electronic tiltmeters and spring-based gravimeters are characterized by drift that is impossible to distinguish from steady geodetic changes, meaning that continuous data of those types are only useful over short (minutes-days) time scales (Jahr et al., 2006; Poland and Carbone, 2016, 2018). GNSS data are subject to a variety of artifacts, the most important of which are multipath and the presence of atmospheric water vapor, that have motivated a range of mitigation factors, including antenna design, signal processing strategies, and models of atmospheric properties (e.g., Larson et al., 2010). Even with these adjustments, there can still be ambiguity in whether cm-level displacements recorded by GNSS stations are real ground motion or artifacts. Atmospheric path delays are particularly problematic in InSAR data, where water vapor tends to correlate with topography and can thus result in volcano-centric signals in interferograms that are similar to those expected from subvolcanic sources of pressure change (e.g., Beaudecel et al., 2000) and that are difficult to remove (e.g., Foster et al., 2013). While these challenges in the interpretation of geodetic data can be significant, there are means of mitigating their impacts. For example, efforts merging weather models and GNSS-derived tropospheric delay estimates (Yu et al., 2018) show promise in minimizing artifacts in interferograms. Regardless, geodetic data are like any other measurement—they have associated uncertainties and are subject to conditions that influence their accuracy. These sources of error should be acknowledged before any interpretations are made. Interpretation of geodetic signals at active volcanoes is complicated by the difficulty in distinguishing magmatic, tectonic, and hydrothermal processes. Over timescales of months to years, tectonic deformation may mask subtle, long-term inflation due to magma accumulation, especially in GNSS datasets. For instance, to separate tectonic and magmatic effects, Lisowski et al. (2008) used a large set of GNSS data from the Pacific Northwest United States to account for regional strain accumulation and rigid-body rotation of the Cascadia forearc, which was necessary to isolate cm-scale magmatic deformation of Mount St. Helens before, during, and after the volcano’s 2004–8 eruption. Short-term tectonic activity can also “contaminate” volcanic signals in GNSS time series, as exemplified by the displacements associated with episodic tremor and slip events from the Cascadia subduction zone recorded at GNSS sites on and around Mount St. Helens (Fig. 7; Dzurisin et al., 2015).

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Like deformation data, gravity measurements at volcanoes can also be “contaminated” by nonvolcanic sources. The most common source of non-magmatic gravity change is variation in groundwater levels over time—a factor that can be assessed via data from water wells (e.g., Battaglia et al., 1999) or models of groundwater recharge due to precipitation (e.g., Battaglia et al., 2006). A thorough accounting of “nuisance” sources is needed before any gravity change can be interpreted as due to subsurface magmatic activity (e.g., Poland and de Zeeuw-van Dalfsen, 2019). Of equal concern to the proper interpretation of data is the interpretation of the models derived from those data. Most models of deformation and gravity change utilize simple analytical solutions and elastic half-spaces, like the well-known Mogi (1958) and Okada (1985) sources. While numerical models and sophisticated analytical formulations offer the ability to approximate more complex source geometries, nonelastic rheologies, and factors like crustal

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FIG. 7 North, east, and vertical components of deformation from GNSS site JRO1, which is located 8 km north of Mount St. Helens. Time series has been corrected for seasonal fluctuations and steady tectonic motion due to forearc rotation and subduction zone strain. The period of the 2004–8 eruption is shaded red and included a motion to the south, east, and down, suggesting deflation of the volcano. After the end of the eruption, a few-year period of motion to the north and up is indicative of inflation. Blue bars denote episodic tremor and slip events on the nearby Cascadia subduction zone. Those events are strongly manifested in the east component of JRO1, complicating interpretation of the time series. Fortunately, volcanic deformation at this station is most strongly manifested in the north and up components. (Modified from Dzurisin, D., Moran, S.C., Lisowski, M., Schilling, S.P., Anderson, K.R., Werner, C., 2015. The 2004–2008 dome-building eruption at Mount St. Helens, Washington: epilogue. Bull. Volcanol. 77(10), 89, https://doi.org/10.1007/s00445-0150973-4.)

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heterogeneity, the simplest models remain the most frequently applied—they are easy to implement, and often there are not enough data to justify more advanced models. The most common source parameters from these simple models are location and strength. When using the popular point-source, or Mogi, model (Mogi, 1958), source strength is usually expressed as volume change because such models have an inherent ambiguity between the source volume and pressure change. In situations where a priori information on source size is available, or when using models with finite source volume, like a finite sphere (McTigue, 1987), modeled pressure change can be determined but is often unrealistically high (i.e., greater than host-rock strength), perhaps due to assumptions of elastic rheology (Newman et al., 2001). The map-view location of a source is usually well constrained, but there is a significant tradeoff between depth and strength, so it can be difficult to distinguish shallow sources with low strength from slightly deeper sources with higher strength. Modeled depth is sensitive to the physical properties of the host rock; therefore, supplementary information on source depth—for instance, from seismic data—can be of great value (e.g., Masterlark et al., 2012). Modeled volume changes should also not be taken as actual volumes of magma, given that simple analytical models usually treat magma as an incompressible fluid—clearly an oversimplification, given the presence of dissolved and exsolved gases in the melt. The actual volume of magma accumulation or withdrawal from subsurface sources might be many times larger than modeled values (e.g., Rivalta and Segall, 2008). This may be one reason why modeled volume changes usually do not match erupted or intruded volumes—not because volumes of magma withdrawal are much less than erupted volumes, but rather because compressibility can bias models of volume change (e.g., Mastin et al., 2009; Anderson and Segall, 2011, 2013). If interpreted literally, volume changes may result in misleading conclusions—for example, that a magma chamber is being recharged during an eruption, which has very different hazard implications than no recharge. The 2004–8 eruption of Mount St. Helens provides a poignant example of this problem. During that eruption, the volume of lava extrusion greatly exceeded the volume of deflation, suggesting the possibility that the magma reservoir was being recharged during the eruption (e.g., Dzurisin et al., 2008). Modeling studies, however, suggested that compressibility effects with no recharge could explain the volume discrepancy (Mastin et al., 2009; Anderson and Segall, 2013; Segall, 2013). Similar ambiguity surroundsposteruptive inflation at Mount St. Helens (Fig.7),which could indicate magma recharge (Dzurisin et al., 2015) or might reflect some other process, like the relaxation of a viscoelastic shell around the magma body that fed the eruption (Segall, 2016).

5

Beyond the subsurface: Novel uses of geodetic data

When discussing volcano geodesy, the focus is usually on how deformation and gravity data can shed light on subsurface processes—generally magma storage and transport. This narrow view, however, misses the richness of geodetic data,

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and how they contribute to a better understanding of volcanic hazards through novel applications. In fact, geodetic data provide a number of underappreciated capabilities, including means of tracking surface change due to the emplacement of volcanic deposits, detecting ash plumes, and constraining the physical properties of magma.

5.1 Surface change SAR amplitude data are, in general, underutilized, despite their great sensitivity to changes in the characteristics of the surface and ability to “see” through the dense cloud cover that often characterizes volcanic summits. High-resolution SAR data (a few meters per pixel or better) have been used to map the development of structures during volcanic crises, for example, dike emplacement and lava effusion at Ba´rðarbunga, Iceland (Dumont et al., 2018), pyroclastic flow emplacement and lava dome collapse at Soufrie`re Hills Volcano, Montserrat (Wadge et al., 2011), emplacement of successive lava flows at El Reventador, Ecuador (Arnold et al., 2019), and lava dome growth at Merapi, Indonesia (Pallister et al., 2013), and Mount Cleveland, Alaska (Wang et al., 2015). These data have proven to be of vital importance to civil defense officials, and in some cases have contributed to warnings that are credited with saving thousands of lives (Pallister et al., 2013). Changes in SAR coherence through time can also be used to track surface change. Variations in the geometry of scatterers at the surface between the times of SAR image acquisitions can lead to a loss of correlation between SAR images. Most commonly, this is caused by heavy vegetation and changes in snow and ice cover. The emplacement of volcanic deposits can also cause incoherence, for example, as land is resurfaced by lava, pyroclastic density currents, or mudflows. It is thus possible to map, for example, the evolution of lava flow areas over time with SAR coherence, as long as the flows are covering areas that would otherwise be coherent, like barren older lava flows (Zebker et al., 1997; Rowland et al., 2003; Dietterich et al., 2012). SAR data also provide a means of measuring surface topography. Multiplepass interferometry, which involves data acquisition at different points in time, can be used to construct a digital elevation model (DEM) of the surface, but only if the ground remains coherent between satellite acquisitions (which is not usually the case on vegetated or snow/ice-covered volcanoes). Alternatively, single-pass, or bistatic, interferometry, which involves two SAR images acquired at the same time, is coherent everywhere, since there is no time between acquisitions. Repeat acquisition of bistatic data is especially useful for measuring topographic change over time, allowing for calculations of not just lava flow volumes, but effusion rates—critical information that might not be possible from ground-based measurements (e.g., Poland, 2014; Albino et al., 2015; Arnold et al., 2017, 2019; Kubanek et al., 2017).

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Detection and characterization of volcanic plumes

GNSS data have demonstrated a surprising amount of flexibility in applications that are not related to monitoring surface displacements. For example, GNSS results have been used to estimate snow depth, lake levels, and soil moisture through the characteristics of the signal-to-noise ratio (SNR, which is related to multipath) computed by the receiver for individual satellites (Larson et al., 2008, 2009, 2013; Larson, 2019). SNR is also affected when GNSS signals pass through ash plumes (Larson, 2013), and position offsets occur due to the phase delay in the GNSS signal caused by ash plumes located in the signal path (Grapenthin et al., 2013). The combination of SNR and phase data can not only detect ash plumes but also distinguish plumes that are dominated by ash versus water or ice (Grapenthin et al., 2018)—a capability with obvious applications to volcanic hazards, given the threat to aviation posed by ash plumes (e.g., Prata, 2009). GNSS data could therefore potentially be used as an all-weather plume detection system, especially at volcanoes with dense GNSS monitoring networks and near-real-time data transmission. Volcanic plumes have also been detected in InSAR data. Given that there can be a phase delay in GNSS signals that pass through a plume; it is not surprising that radar interferograms, which also measure phase differences between a satellite and the ground, might show the presence of gas and ash in the atmosphere. There are relatively few examples in the literature of plumes as transient phase anomalies in interferograms, probably because it can be difficult to distinguish phase differences caused by plumes from those due to ground deformation or other atmospheric phenomena. For instance, an interferogram spanning the eruption of Fogo, Cape Verde, in 2014 contains a residual, once a deformation model is subtracted, which is consistent with the presence of a volcanic plume that was also imaged by other satellite systems (Gonza´lez et al., 2015). Similar phase residuals were occasionally present in interferograms spanning summit eruptive activity at Kīlauea (Fig. 8) and may reflect the presence of a gas-rich plume—a persistent feature of that eruption during 2008–18 (e.g., Patrick et al., 2011). GNSS and other ground-based data further offer some promise for estimating plume height, albeit via a model accounting for magma pressure instead of from direct measurement. Using high-rate (1 Hz) GNSS observations supplemented by tilt data from the 2011 eruption of Grı´msv€ otn Volcano, Iceland, Hreinsdo´ttir et al. (2014) demonstrated that plume height was correlated with the rate of pressure change (and therefore magma discharge) in the source magma reservoir. Nearreal-time GNSS and other data might, therefore, be useful not only for detecting plumes but also for characterizing the intensity of ash emissions.

5.3

Properties of magma and magmatic systems

The physical properties of magma—including such parameters as density and gas content—must generally be prescribed when it comes to models of

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FIG. 8 COSMO-SkyMed interferogram spanning February 2–9, 2016, showing the summit caldera of Kīlauea Volcano, Hawai’i. The small bullseye-like fringe pattern that occurs just east of the summit eruptive vent may be related to the presence of a plume of gas and ash causing an artifact in the pattern of the phase change (the signal is unlike any deformation pattern ever seen at Kīlauea). The photo shows an example of Kīlauea’s summit plume, with the Hawaiian Volcano Observatory facility in the foreground. (Photo by Michael Poland, September 3, 2008.)

magma dynamics. Making the wrong assumption in terms of these parameters can lead to biased interpretations of monitoring data. For example, failure to account for the compressibility of magma may result in modeled subsurface volume changes that are several times different than actual volumes of erupted/intruded magma (Rivalta and Segall, 2008). Geodetic data offer some ability to measure the physical properties of magma, especially when merged with other non-geodetic datasets. The combination of gravity and deformation data can provide constraints on the density of subsurface sources causing geodetic unrest, given that gravity measures mass change, and deformation provides an indication of volume change. Battaglia et al. (1999) used this principle to infer that magma, as opposed to water or gas, was driving unrest at Long Valley caldera in eastern California. Similar applications of gravity and deformation measurements have suggested that hydrothermal fluids are a source of unrest at Campi Flegrei (Battaglia et al., 2006; Gottsmann et al., 2006), while a combination of hydrothermal and magmatic sources may be active at Yellowstone, USA (Tizzani et al., 2015). Gravity has also been used in combination with data from visible and thermal cameras to determine that the density of the lava lake at Kīlauea during 2011–5 was approximately 1000 kg/m3 (Carbone et al., 2013; Poland and Carbone, 2016, 2018). This information could not be determined by any other means, providing important constraints on models of lava lake convection and a better understanding of the hazards associated with rockfall-induced small explosive events (Carbone et al., 2013).

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Models of magmatic systems using geodetic data have traditionally focused on deformation and gravity change measurements to constrain the source location and changes in volume or mass. These limited kinematic inversions are gradually being supplanted by more realistic models, including physics-based approximations. Geodetic data are a primary input to such models. When combined in these models with a few additional data types, like extrusion rate and constraints on volatile source composition, geodetic measurements can be used to probabilistically estimate such parameters as magma compressibility and gas content (e.g., Anderson and Segall, 2013), and even magma supply (Anderson and Segall, 2011; Anderson and Poland, 2016). Physics-based models will only grow in their application in the future, especially given their potential for probabilistic forecasting (Segall, 2013). The need for the powerful information provided by deformation and gravity change observations will grow in concert.

6

Case studies

To highlight the application of geodesy to volcanic hazards assessment, we offer two case studies. The first, of Agung, Indonesia, demonstrates the unique perspective, novel applications, and pitfalls of deformation data for constraining magma storage, unrest, and eruptive activity of a volcano that has a history of large eruptions with significant regional, and even global, impact. The second explores the motivation behind, and implementation of, geodetic networks on quiescent, but potentially hazardous, volcanoes—specifically those located on the islands of Saba and St. Eustatius in the Caribbean Netherlands.

6.1 Agung, Indonesia: Geodetic insights into pre- and co-eruptive volcanic activity and hazards Agung, a 3142 m-high stratovolcano in Bali, Indonesia, has a geologic history that is marked by repeated large explosive events (Self and Rampino, 2012; Fontijn et al., 2015). Its VEI ¼ 5 eruption in 1963 was one of the largest of the 20th century, resulting in over 1000 deaths and impacting the global climate (Self and Rampino, 2012). Following over 50 years of quiescence, the volcano reawakened in 2017, with a slow buildup in seismicity over months that peaked in September-October 2017 and preceded the eruption of lava within the volcano’s summit crater that November. Weak eruptive activity persisted through 2018 and into 2019, including a second cycle of lava effusion in July 2018, but there was no major explosion, and lava activity remained confined to the summit crater. Concern over a repeat of 1963 prompted the evacuation of well over 100,000 people from the surrounding areas in mid-late 2017, and political, social, and economic pressures to forecast the outcome of the activity were particularly high (Syahbana et al., 2019). Deformation data offered an important window into the 2017–9 activity at Agung, providing information about subsurface processes that would not otherwise be known.

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A survey of Indonesian volcanoes with InSAR suggested inflation of Agung at a rate of a few cm/year during 2007–09 (Chaussard and Amelung, 2012). This discovery motivated the installation of additional ground-based monitoring instruments, including a network of five continuous GNSS stations (Syahbana et al., 2019). Unfortunately, there are large data gaps prior to 2017, and data were not downloaded and analyzed routinely, so surface displacements were not recognized until after the onset of seismicity in 2017. Once analyzed, the GNSS data revealed complex deformation in the months prior to the September seismic crisis. The time series of displacements at station RNDG (Fig. 9A), located about 12 km southwest of the volcano’s summit, shows two periods of inflationary deformation that were corroborated by results from other stations in February-March 2017 and August-September 2017, with the former unaccompanied by seismicity, and the latter coincident with slowly increasing earthquake activity. The sense of displacement changed from moving away from the volcano to motion toward the volcano with the onset of intense seismicity in mid-September. These patterns suggest inflation of a source beneath the volcano prior to the most intense part of the seismic swarm, and then deflation of that source during the swarm (Syahbana et al., 2019). The mid-September seismic swarm, which occurred between Agung and Batur (a volcano located about 18 km to the northwest of Agung), was initially thought to represent distal volcano-tectonic activity, perhaps as nearby faults were activated by pressure changes beneath the volcano (Syahbana et al., 2019). InSAR data spanning the seismicity, however, revealed that the earthquakes were associated with the intrusion of a dike between Agung and Batur (Albino et al., 2019; Fig. 9A)—a conclusion that would not have been possible without the broader coverage provided by InSAR given the lack of GNSS sites in the vicinity. From high-resolution imagery, InSAR also detected uplift within Agung’s summit crater (Fig. 9B). This deformation may reflect the perturbation of the shallow hydrothermal system, as suggested by fumaroles, thermal anomalies, and expulsion of water within the crater (Syahbana et al., 2019). No further surface displacements were detected by InSAR or GNSS prior to, during, or following the lava effusion of late November, nor throughout 2018 (including associated with the lava effusion in July of that year). High-resolution SAR did provide important information on the lava effusion itself, given the persistent cloud cover that prevented regular visual observations (from the air or space). Amplitude images revealed the extrusion of a low-viscosity (based on its smoothness) lava flow and its subsequent degradation as small explosions left craters pockmarking the surface (Fig. 9C). Interferograms made from these data also showed posteruptive subsidence of the flow over time periods of a few days (Fig. 9D), presumably due to cooling and contraction (e.g., Chaussard, 2016). The example of Agung demonstrates many of the advantages and challenges of geodesy with respect to volcanic hazards. Deformation data, from InSAR, provided the first indication of potential volcano inflation, motivating the

FIG. 9 GNSS and synthetic aperture radar data from Agung volcano, Bali, Indonesia, during 2017–8. (A) Sentinel-1 interferogram spanning September 21–October 27, 2017, showing a pattern of fringes along the coast north of Agung that is consistent with a dike intrusion (modeled location marked by dashed white line) between Agung and Batur (Albino et al., 2019). The fringes high on Agung’s north flank are an atmospheric artifact. The area of the interferogram is denoted by a red box in the map inset. Time-series inset shows the north displacement of GNSS station RNDG (white circle on interferogram), about 12 km SW of Agung. The motion of RNDG to the south (downward in the plot) is interpreted as inflation and motion to the north as deflation, both due to a source of pressure change beneath the summit of the volcano (Syahbana et al., 2019). (B) High-resolution COSMO-SkyMed interferograms of the summit crater of Agung. Interferograms that span September-October are characterized by about 10 cm of line-of-sight uplift within the crater of the volcano, but time periods before and after show no signs of intracrater deformation. (C) High-resolution COSMO-SkyMed radar amplitude images of Agung’s summit crater showing the time period before (top), during (middle), and following (bottom) lava extrusion in late November 2017. The lava flow surface was initially uniform (middle) but gradually degraded (bottom) as it cooled, showing pockmarks from small explosions. (D) High-resolution COSMO-SkyMed interferogram of Agung’s summit crater spanning February 17–20, 2018. The perpendicular baseline for the interferogram is 74 m, which gives an altitude of the ambiguity of about 100 m. This means that new topography—in this case, the lava flow—that is not in the digital elevation model used to account for the topographic phase would produce a single interferometric fringe per 100 m. The new lava flow is a maximum of about 120 m thick (Syahbana et al., 2019), so the numerous fringes apparent on the new lava flow in the interferogram are not topographic in nature, but instead, reflect rapid subsidence—tens of cm in just 3 days—presumably due to flow cooling and contraction.

100 Forecasting and planning for volcanic hazards, risks, and disasters

installation of enhanced ground-based monitoring. Continuous GNSS stations detected additional uplift (including aseismic inflation) but were not downloaded and analyzed in a timely manner, so the deformation, which might have provided additional warning of the impending eruption, was not recognized until late in the crisis. Deformation data were also key to understanding the mechanism of the intense September-October seismicity—dike intrusion— and also documented significant changes to Agung’s crater, where the eruption would eventually occur in late November, although misinterpretations of atmospheric artifacts in some interferograms were propagated in social media posts during the September-October 2017 seismic crisis. Finally, in a demonstration of the use of geodetic data for nongeodetic purposes, amplitude images from satellite radar provided synoptic views of lava flow effusion and evolution that were not available from visual observations owing to the geometry of the crater and frequent cloud cover. Emphasizing the challenges in volcano geodesy, surface deformation indicating magma ascent beneath the summit was not detected, nor was it possible to forecast the size and style of the eventual eruption from geodetic or other monitoring data. One wonders, however, if a continuous gravimeter operating near the summit crater—an installation that would have been a considerable logistical and financial challenge—might have identified magma rising to shallow levels.

6.2 Saba and St. Eustatius, Dutch Antilles: Preemptive geodetic response at historically dormant volcanic islands Most of the examples discussed earlier involve volcanoes where geodetic networks and satellite data have been available for years to decades. But what strategies should be employed at volcanoes where there is no geodetic monitoring (yet)? How can geodetic methods be implemented to provide advance warning of potential future volcanic activity? The most northern volcanoes of the Lesser Antilles volcanic arc in the Caribbean (Fig. 10) offer an example. The volcanic arc of the Lesser Antilles is home to 17 active volcanoes located on 11 main islands. The deposits of past eruptions and historical records provide evidence for the violence with which these volcanoes can erupt and destroy lives and livelihood—in 1902, the fifth-deadliest eruption in recorded history, from Montagne Pelee on Martinique, killed about 29,000 people (Tanguy, 1994). Even though the volcanoes of this chain seem relatively quiet, they can reawaken after prolonged periods of inactivity. Soufrie`re Hills volcano, on the island of Montserrat, became active in 1995 after 350 years of dormancy (Sparks and Young, 2002). Monitoring instruments were installed during the opening phase of that eruption, but the lack of baseline data prior to the onset of volcanic activity complicated interpretation of co-eruption changes (Jackson et al., 1998). The northernmost islands of the chain, Saba and St. Eustatius, are collectively home to over 5000 people. Both islands host a stratovolcano (Fig. 10):

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FIG. 10 The Royal Netherlands Meteorological Institute (KNMI) volcano monitoring networks on Saba (upper right) and St. Eustatius (lower right), in the Lesser Antilles volcanic arc (left, with active volcanoes indicated by red triangles and island names in italics; the island of Sint Maarten is located off the map, 50 km to the north). Blue circles show locations of collocated seismometer and GNSS, red circles are seismometers only, green circle is GNSS only, and yellow circle depicts a monitored hot spring. Two additional GNSS-only installations are planned, ideally on the northwest part of Saba and the southeast part of St. Eustatius. (Modified from de Zeeuw-van Dalfsen, E., Sleeman, R., 2018. A permanent, real-time monitoring network for the volcanoes Mount Scenery and The Quill in the Caribbean Netherlands. Geosciences 8, 320, https://doi.org/10.3390/ geosciences8090320.)

The Quill (600 m elevation) on St. Eustatius and Mt. Scenery (887 m elevation) on Saba, the latter island also hosting multiple lava domes. Even though there are no historical accounts of eruptions at these volcanoes, the presence of heated groundwater on St. Eustatius and hot-springs on Saba suggest that these volcanoes are active (de Zeeuw-van Dalfsen and Sleeman, 2018). Taking into account the hazard and exposure factors, these volcanoes classify as high and very high threat, respectively, according to the scheme of Ewert et al. (2005), thus justifying the importance of comprehensive monitoring. Lessons from other Caribbean islands, such as Montserrat, 120 km SE of St. Eustatius, demonstrate that it is crucial to establish a monitoring network before the activity commences.

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Since 2006, the Royal Netherlands Meteorological Institute (KNMI) has operated a seismic network (designated seismic network code “NA”) in the Caribbean Netherlands (de Zeeuw-van Dalfsen and Sleeman, 2018), starting with one broadband seismometer on each of the islands of Sint Maarten, Saba, and St. Eustatius. In 2014–5, five additional seismometers were installed, three on Saba and two on St. Eustatius. The network is designed to monitor regional seismicity as well as seismic signals that may precede or accompany the volcanic activity. Because not all volcanic eruptions are presaged by seismic indicators (e.g., Van Eaton et al., 2016; Fee et al., 2017), it is preferable to use multiple techniques to assess the state of volcanic activity—a stool with more than one leg makes a more comfortable seat! KNMI is thus developing a multiparameter monitoring system on Saba and St. Eustatius, with geodesy playing a central role (Fig. 10). But how should a ground-based geodetic monitoring network be set up in the absence of guidance from current or historically observed eruptions? In these circumstances, knowledge of the geologic history of the volcano is of utmost importance. Unfortunately, the eruptive patterns of Mt. Scenery and The Quill are poorly known—the dates of the most recent eruptions are uncertain and the eruption histories are complicated. This affects both the assessment of hazards and the choice of locations for monitoring instruments. On Saba, for example, knowledge of the eruption history is critical for ensuring that the most recently active vent areas are monitored, but the island’s structure of overlapping domes makes identifying the last eruptive vent a challenge. In an ideal world, a monitoring network would consist of multiple sensors at varying distances from the target volcano, with station density highest close to the likely eruptive vent. This geometry would enhance the potential for detecting a geodetic signal and facilitate modeling efforts. At least one instrument should also be located outside the zone where deformation is expected to serve as a reference and to define the background deformation, but this is difficult to achieve on a small island. And what techniques would be most useful for monitoring deformation on such small islands? InSAR is hampered by shadowing effects due to steep topography and by coherence loss because of heavy vegetation. The installation of borehole tiltmeters would be a considerable logistical challenge, and campaign GNSS observations require time and energy that are disproportionate to the amount of data that would be collected. Continuous GNSS is therefore the most suitable geodetic observation method for Saba and St. Eustatius. In addition to station geometry, points of concern regarding continuous GNSS for volcano monitoring include: (1) the location with respect to the volcano and other monitoring equipment; (2) the availability of bedrock and stability of the site; (3) access to power and data transmission facilities; (4) an unobstructed sky view; (5) a suitable path through vegetation and topography for power and data cables; (6) protection against lightning strikes; and (7) the security of the installations. Finding sites on Saba and St. Eustatius that fulfill all

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these requirements is impossible given the lack of infrastructure, dense tropical rainforest, and ground that is composed of loosely consolidated pyroclastic flow deposits. To construct a geodetic monitoring network, compromises, such as a partly obstructed sky view, are therefore inevitable. Engineering solutions can overcome other challenges, like creating a shallow-braced concrete foundation where bedrock is absent. As of 2019, two GNSS stations have been installed on each island (Fig. 10). Plans for additional stations in the coming years include one on the northwest part of Saba and another on the southeast part of St. Eustatius, which would strengthen the network geometry on both islands by establishing sites on all sectors of the volcanoes. These installations will be challenging, however, especially on Saba, where the northwest corner of the island is covered in tropical rainforest, composed of loose pyroclastic debris without solid bedrock, and has no road access. Overcoming the challenging environmental conditions and installing continuous GNSS sites on Saba and St. Eustatius is a major step forward in establishing comprehensive volcano monitoring networks that can provide early warning of future volcanic hazards on those islands, especially given the record of geodetic change as an indicator of magma accumulation and ascent. Establishing these networks, however, is only the start. To detect changes in geophysical parameters with enough time to provide a warning to authorities, a monitoring network must be maintained and continuously operational, even, and perhaps especially, when volcanic activity appears to be at background levels. This requires both financial and scientific support from the government and the populace. Sustaining a multiparameter monitoring network in tropical conditions is a constant challenge, but is the only means of ensuring that potential future volcanic activity is likely to be detected in advance.

7

The future of geodesy applied to volcanic hazards

As in other Earth science disciplines, the past few decades have seen tremendous growth of geodetic techniques and know-how applied to volcanic hazards. We anticipate similarly rapid development in the coming decades owing to new satellite missions, technological improvements that increase the accuracy and decrease the cost of geodetic sensors, and new ways of analyzing data and modeling volcano behavior. These advances will improve the ability of geodetic data to forecast not only new volcanic activity but also changes in ongoing eruptions, thereby serving the societal need for improved mitigation of volcanic hazards. With respect to SAR satellites, there is already near-daily coverage of many places on Earth by sensors that span X-, C-, and L-band wavelengths and that have imaging resolutions of centimeters (over a swath width of a few kilometers) to tens of meters (over a swath width of hundreds of kilometers). Some specialized missions, like TanDEM-X, are capable of providing data that can

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be used for mapping topographic change (e.g., Poland, 2014) and updating maps of volcanic hazards (e.g., Richter et al., 2016). Planned and proposed missions will enhance these capabilities, supplying, for example, frequent L-band repeat passes, which are especially valuable for heavily vegetated volcanoes, from the US-India NISAR mission (Rosen et al., 2017), and bistatic high-resolution radar, which yields surface topography and how that changes over time, from the candidate Harmony mission (Lo´pez-Dekker et al., 2019). A growing trend includes open access to satellite SAR images, especially for hazard applications, and low latency between acquisition and delivery. This capability offers perhaps the best hope for detecting volcano deformation in advance of eruptive activity thanks to the growth of artificial intelligence algorithms that utilize, for instance, independent component analysis (Gaddes et al., 2018) and machine learning techniques (Anantrasirichai et al., 2018) to detect anomalous signals. When properly tuned to accurately recognize volcanic deformation, these developments offer hope that operational monitoring of global volcanoes with InSAR may be achieved within the next several years, providing a means of alerting scientists and emergency managers of any anomalous displacements at a volcano—especially at remote or typically dormant volcanoes that might not be subject to the same scrutiny as more active centers. Ground-based technology will similarly advance. New generations of GNSS receivers will no doubt take advantage of the increasing number of available satellites and systems, and lower-cost instruments will result in the monitoring of more volcanoes worldwide with high-rate, real-time data streams. Additional ground-based monitoring will aid overall understanding of the spatial and temporal patterns of deformation that occur before, during, and after eruptions of various types—information that can feed probabilistic-based forecasts of volcanic hazards (e.g., Newhall and Pallister, 2015). Artificial intelligence approaches might also be adopted for examining data from networks of ground-based geodetic stations, facilitating the recognition of magmatic transients in data that might also be characterized by tectonic deformation, hydrothermal activity, and atmospheric artifacts. We expect the most radical technological innovations to occur in the field of gravity. For several decades, the fundamental approaches to measuring gravity have not evolved, generally relying on expensive spring-based gravimeters or even more expensive superconducting and free-fall instruments, few of which are designed for use in the harsh conditions found at the summits and flanks of most volcanoes (Carbone et al., 2017). These technological constraints are already beginning to change. Continuous measurements with microgal accuracy by a portable instrument is a reality thanks to quantum gravimetry (e.g., Menoret et al., 2018), even if the instrumentation is still too expensive for most volcano observatories. Although only capable of measuring relative gravity changes, MEMS gravimeters are approaching accuracy that is sufficient to detect tens-of-microgal changes at a small fraction of the cost of current spring-based instruments (Middlemiss et al., 2016, 2017). An array of

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continuously operating MEMS gravimeters would be not only affordable but also increase the signal to noise ratio of volcano gravity measurements. The European H2020 project NEWTON-g aims to develop new tools for terrestrial gravimetry, namely a field-compatible gravity imager, which includes an array of low-cost MEMS-based relative gravimeters anchored by an absolute quantum gravimeter. This system will provide imaging of gravity changes with unparalleled spatiotemporal resolution. We anticipate that this technology will result in the exploration of gravity change at numerous volcanoes worldwide within a decade. Finally, improvements in the analysis and modeling of geodetic data will continue to follow their current steep trajectory. For decades, volcano geodesy was a relatively data-poor field, with just a few ground-based measurements at discrete times on a given volcano; continuous deformation monitoring was rare until the 1990s. Models of deformation were therefore necessarily simple (e.g., Mogi, 1958; Okada, 1985), since complex models could not be justified given the dearth of data. Now, two decades into the 21st century and with the explosion of space-geodetic techniques, volcano geodesy is no longer limited by data availability. The resulting challenges are twofold: analyzing the flood of results, and maximizing the return from those results. Collaboration between volcano geodesists and computer scientists is beginning to address the former issue, taking advantage of artificial intelligence to automatically evaluate large datasets with minimal human intervention (e.g., Anantrasirichai et al., 2018; Gaddes et al., 2018). Models are similarly advancing, abandoning simple analytical solutions that ignore complications like topography, rheology, and geometry in favor of incorporating actual physics to solve for not just the size and shape of a magma reservoir, but also its volatile content and magma budget in a probabilistic framework (e.g., Anderson and Segall, 2011, 2013; Anderson and Poland, 2016, 2017). These advanced models have some predictive capabilities that will continue to be developed in the years to come (e.g., Segall, 2013). It is not unreasonable to anticipate that forecasts of volcanic hazards will eventually share similarities with weather forecasts, providing probabilistic outcomes that are based on both monitoring data and physiochemical laws. Volcano geodesy, which offers direct constraints on subsurface volume and mass changes, will provide vital input to these models.

Acknowledgments We are grateful to Paolo Papale for inviting this contribution. Reinoud Sleeman and Dan Dzurisin provided comments and suggestions that greatly improved the manuscript, and Masato Iguchi and Emily Montgomery-Brown offered insightful reviews. Mike Lisowski provided the data needed to develop Fig. 7. We would also like to acknowledge the inspiring scientists—our colleagues, mentors, and friends—with whom we have worked over the years, and who provided many of the examples discussed in this chapter.

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114 Forecasting and planning for volcanic hazards, risks, and disasters Poland, M.P., de Zeeuw-van Dalfsen, E., 2019. Assessing seasonal changes in microgravity at Yellowstone caldera. J. Geophys. Res. https://doi.org/10.1029/2018JB017061. Prata, A.J., 2009. Satellite detection of hazardous volcanic clouds and the risk to global air traffic. Nat. Hazards 51 (2), 303–324. https://doi.org/10.1007/s11069-008-9273-z. Pritchard, M.E., Simons, M., 2002. A satellite geodetic survey of large-scale deformation of volcanic centres in the central Andes. Nature 418, 167–171. https://doi.org/10.1038/nature00872. Pritchard, M.E., Simons, M., 2004. An InSAR-based survey of volcanic deformation in the Southern Andes. Geophys. Res. Lett. 31(15). https://doi.org/10.1029/2004GL020545. Pritchard, M.E., Biggs, J., Wauthier, C., Sansosti, E., Arnold, D.W.D., Delgado, F., Ebmeier, S.K., Henderson, S.T., Stephens, K., Cooper, C., Wnuk, K., Amelung, F., Aguilar, V., Mothes, P., Macedo, O., Lara, L.E., Poland, M.P., Zoffoli, S., 2018. Towards coordinated regional multi-satellite InSAR volcano observations: results from the Latin America pilot project. J. Appl. Volcanol. 7(1). https://doi.org/10.1186/s13617-018-0074-0. Punongbayan, R.S., Newhall, C.G., Bautista, M.L.P., Garcia, D., Harlow, D.H., Hoblitt, R.P., Sabit, J.P., Solidum, R.U., 1996. Eruption hazard assessments and warnings. In: Newhall, C.G., Punongbayan, R.S. (Eds.), Fire and Mud: Eruptions and Lahars of Mount Pinatubo, Philippines. University of Washington Press, Seattle, WA, pp. 67–85. Richter, N., Poland, M.P., Lundgren, P.R., 2013. TerraSAR-X interferometry reveals small-scale deformation associated with the summit eruption of Kīlauea Volcano, Hawai‘i. Geophys. Res. Lett. 40 (7), 1279–1283. https://doi.org/10.1002/grl.50286. Richter, N., Favalli, M., de Zeeuw-van Dalfsen, E., Fornaciai, A., Fernandes, R.M.D.S., Perez, N.M., Levy, J., Victo´ria, S.S., Walter, T.R., 2016. Lava flow hazard at Fogo Volcano, Cabo Verde, before and after the 2014–2015 eruption. Nat. Hazards Earth Syst. Sci. 16 (8), 1925–1951. https://doi.org/10.5194/nhess-16-1925-2016. Rivalta, E., Segall, P., 2008. Magma compressibility and the missing source for some dike intrusions. Geophys. Res. Lett. 34(4). https://doi.org/10.1029/2007GL032521. Rosen, P.A., Kim, Y., Kumar, R., Misra, T., Bhan, R., Sagi, V.R., 2017. Global persistent SAR sampling with the NASA-ISRO SAR (NISAR) mission. In: 2017 IEEE Radar Conference (RadarConf), 8–12 May, Seattle. https://doi.org/10.1109/RADAR.2017.7944237. Rowland, S.K., Harris, A.J.L., Wooster, M.J., Amelung, F., Garbeil, H., Wilson, L., MouginisMark, P.J., 2003. Volumetric characteristics of lava flows from interferometric radar and multispectral satellite data: the 1995 Fernandina and 1998 Cerro Azul eruptions in the western Gala´pagos. Bull. Volcanol. 65 (5), 311–330. https://doi.org/10.1007/s00445-002-0262-x. Salzer, J.T., Nikkhoo, M., Walter, T.R., Sudhaus, H., Reyes-Da´vila, G., Breto´n, M., Arambula, R., 2014. Satellite radar data reveal short-term pre-explosive displacements and a complex conduit system at Volca´n de Colima, Mexico. Front. Earth Sci. 2, 12. https://doi.org/10.3389/ feart.2014.00012. Segall, P., 2013. Volcano deformation and eruption forecasting. In: Pyle, D.M., Mather, T.A., Biggs, J. (Eds.), Remote Sensing of Volcanoes and Volcanic Processes: Integrating Observation and Modelling. 380. Geological Society Special Publications, pp. 85–106. https://doi.org/ 10.1144/SP380.4. Segall, P., 2016. Repressurization following eruption from a magma chamber with a viscoelastic aureole. J. Geophys. Res. 121 (12), 8501–8522. https://doi.org/10.1002/2016JB013597. Self, S., Rampino, M.R., 2012. The 1963–1964 eruption of Agung volcano (Bali, Indonesia). Bull. Volcanol. 74 (6), 1521–1536. https://doi.org/10.1007/s00445-012-0615-z. Sparks, R.S.J., Young, S.R., 2002. The eruption of Soufrie`re Hills Volcano, Montserrat (1995– 1999): overview of scientific results. In: Druitt, T.H., Kokelaar, B.P. (Eds.), The Eruption of Soufrie`re Hills Volcano, Montserrat, From 1995 to 1999, pp. 45–69. https://doi.org/10.1144/ GSL.MEM.2002.021.01.03 Geological Society of London Memoir, 21.

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Chapter 4

Geochemical monitoring of volcanoes and the mitigation of volcanic gas hazards Marie Edmonds Earth Sciences Department, University of Cambridge, Cambridge, United Kingdom

1

Introduction

Volcanic eruptions are among the most dynamic and powerful forces in the natural world, with the capacity to destroy life and livelihoods and render environments inhospitable. Yet, hundreds of millions of people worldwide live within the reach of Holocene volcanoes, making volcano monitoring essential for understanding unrest and forecasting eruptions. Surveillance of active and dormant volcanoes takes a range of forms (Auker et al., 2013). While the signals of magma movement and intrusion, in the form of seismicity and ground displacements, are powerful, there is increased discriminatory and forecasting power when using a combination of geophysical and geochemical approaches on a range of timescales, particularly when long data baselines are available prior to and between eruptions. Volcanic gases, as measured at the surface, are the only direct chemical probe of magma at depth and may, by their composition and/or flux, indicate movement of magma toward the surface, changes in the permeability of the shallow conduit system, or pressurization of the magma column beneath a lava dome or plug (Aiuppa, 2015; Aiuppa et al., 2010b; Edmonds et al., 2003b; Moussallam et al., 2017; Shinohara et al., 2008). The ways in which volcanic or fumarolic gases are used for volcano monitoring and eruption forecasting are discussed in Section 2. As well as being an invaluable tool for volcano monitoring, volcanic gases are a significant hazard. Volcanic gases are often implicated in fatal volcanic incidents occurring close to vents or calderas, where gases (chiefly carbon dioxide) may accumulate in topographic depressions (Williams-Jones and Rymer, 2015). Sulfate aerosol in volcanic plumes is linked with acid rain and respiratory diseases. Indirect fatalities due to volcanic gases account for millions of deaths due to famine over the past millennium: sulfur dioxide injected into Forecasting and Planning for Volcanic Hazards, Risks, and Disasters https://doi.org/10.1016/B978-0-12-818082-2.00004-4 Copyright © 2021 Elsevier Inc. All rights reserved.

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the stratosphere during large explosive eruptions, after conversion to sulfate aerosol, absorbs ultraviolet and visible radiation, thus warming the stratosphere and cooling the troposphere. This effect can persist for several years and lower global temperatures by up to a few degrees for the largest eruptions (Robock, 2000). The nature of volcanic gas hazards, the associated risks, and how they may be mitigated, are discussed in Section 3.

1.1 Magmatic degassing and the tenet of geochemical monitoring It is well known that volcanoes outgas prodigious quantities of volatiles, in a range of styles and forms (Allard, 1997; Allard et al., 2016; Gerlach, 1991; Gerlach and Graeber, 1985; Werner et al., 2019). As well as the overt emission of large clouds of gas and ash during explosive (which may reach the stratosphere and impact global climate) (Carn et al., 2013; Dutton and Christy, 1992; Wallace and Gerlach, 1994) and effusive eruptions, many “active” volcanoes exhibit permanent or semipermanent “passive degassing,” that is an emission of gases without eruption of lava (Allard, 1997; Shinohara, 2008). These volcanoes may be basaltic/basaltic andesite open vent volcanoes such as Popocatepetl (Mexico) (Roberge et al., 2009), Masaya (Nicaragua) (Williams-Jones et al., 2003) and Nyiragongo (Democratic Republic of Congo) (Carn and Bluth, 2003) or more silicic volcanoes such as Bagana (Papua New Guinea) (McCormick-Kilbride et al., 2018) and Soufrie`re Hills (Montserrat) (Christopher et al., 2015). It is well understood that these fluxes of volcanic gases sourced from passive degassing are far larger (typically by an order of magnitude) than eruptive degassing (Bluth et al., 1993; Carn et al., 2017). Volcanoes in a state of unrest or dormancy, in contrast, do not exhibit hightemperature vent emissions but instead, persistent soil gas and low-temperature fumarolic emissions; these kinds of volcanoes include restless calderas (e.g., Campi Flegrei, Italy and Yellowstone, USA) (Cardellini et al., 2017; Werner and Brantley, 2003) and other quiescent volcanoes in a range of tectonic settings (e.g., Teide, Canary islands; Vulcano, Italy; Mount Hood, Oregon, among many others). Volatiles in these settings may be sequestered into hydrothermal systems at depth and “scrubbed” (Symonds et al., 2001) and/or discharged via cold and hot springs (Bergfeld et al., 2004; Taran et al., 2008). Volcanic gases may be scrubbed where crater lakes exist; this has been demonstrated to influence carbon/sulfur ratios and SO2 fluxes at Poas, Costa Rica (de Moor et al., 2019; Rouwet et al., 2017), at Kawah Ijen (Gunawan et al., 2017). In general, these studies show that scrubbing may not proceed to completion and is reversible; gas composition may depend on lake chemistry and on gas flux. Geochemical monitoring, as defined here, is the surveillance of gaseous or aqueous emissions from volcanoes in the form of plumes (during eruptions or noneruptive periods), fumaroles (during periods of unrest or during eruptions), through soil over diffuse areas (between and during eruptions) and via cold or hot springs (typically during periods of dormancy or unrest) (Fig. 1). Over the

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Active volcanoes High temperature gas plume Diffuse CO2 degassing

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SO2

Fumarole Hot springs

Diffuse degassing H2S CO2 Hot springs

Caldera faults Meteoric water

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SO4 SO2

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Hydrothermal HCO3 system CO2

Second boiling CO2

FIG. 1 Schematic diagram to show the origin of geochemical signals at active and dormant/restless volcanoes. Active volcanoes outgas high-temperature gases exsolved during decompressional degassing during magma ascent up the conduit. High-temperature fumaroles on the volcano’s flanks may also degas magmatic gases, mixed to varying degrees with meteoric fluids. Hot springs may emit boiling waters, with dissolved magmatic components. Deep-derived CO2-rich fluids may seep out of faults and fractures around the volcano. Dormant or restless volcanoes may be associated with calderas (depressions over the magma chamber). A magma body at depth sources magmatic volatiles, which interact with a hydrothermal system, which may “scrub” SO2 and CO2 as sulfate and bicarbonate. Some CO2 and perhaps H2S, derived from deep magma bodies and the hydrothermal system, may reach the surface along faults. Hot springs may emit boiling fluids with both magmatic and meteoric components.

past century, the methods and approaches used to monitor and quantify the volatile emissions from volcanoes have grown enormously in their sophistication and their prevalence. Gas and water-based emissions from volcanoes may be analyzed in the laboratory for a range of elements and isotopes, and spectroscopic techniques are now viable in situ, for a range of elements. Platforms for sensors of various kinds now include remotely operated unmanned aerial systems (UAS). Active gas plumes in both the stratosphere and troposphere are visible and quantifiable from space. The fluxes and compositions of volatiles emitted from volcanoes in all states of eruption, unrest, and dormancy, yield important clues about magma storage and transport, and the potential for changes in eruptive state. Geochemical monitoring has thus evolved as one of the principal monitoring methods, particularly given the enormous development in technology that allows cheaper, safer, automated, and highresolution monitoring. The fundamental tenet behind geochemical monitoring of volcanoes is that magma bodies in the crust outgas volatile species during either second boiling or during magma decompression and rise; this exsolved volatile phase then migrates to the surface and can be detected using a range of instrumentation on a range of temporal scales. Second boiling occurs during magma cooling and crystallization (Fig. 2A); the exsolved volatile phase produced may migrate upward through faults and fractures, or become entrained in geothermal cells,

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Carbon dioxide dissolved in melt (ppm)

Volatiles in silicate melt during second boiling

(A)

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FIG. 2 Volatile exsolution from magmas during slow cooling and crystallization in magma bodies in the crust (second boiling, A) and during decompression, magma ascent, and eruption (first boiling, B). During prolonged crystallization, volatile-undersaturated magmas may undergo substantial crystallization before reaching saturation with respect to an exsolved volatile phase. Subsequent crystallization will be in the presence of an exsolved volatile phase, which will grow in mass as crystallization continues.

which bring the magmatic components to the surface through soil, fumaroles or springs (Fig. 1). As magma moves toward the surface, degassing occurs in response to decompression (Fig. 2B), with volatiles exsolving from melts and either rising ahead of the magma or being advected by the magma to the surface, where they are outgassed to the atmosphere in volcanic plumes (Fig. 1), perhaps as part of a convective flow, where degassed magma then sinks

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back down the conduit (Kazahaya et al., 1994; Shinohara, 2008). Volatile species exsolve at a range of depths, depending on the magma composition, temperature, and oxidation state (Fig. 2B) (Blank and Brooker, 1994; Carroll and Rutherford, 1985; Gerlach and Graeber, 1985; Moore, 2008). During intereruptive periods, when hydrothermal systems become reestablished, SO2 may undergo disproportionation reactions, reacting with water to form sulfate (which may be precipitated) and reduced sulfur species such as native sulfur and hydrogen sulfide gas (Delmelle and Bernard, 2015; Delmelle et al., 2000; Rouwet and Morrissey, 2015; Taran et al., 1996). As well as the primary volatile species H2O, CO2, and S-bearing gases, the exsolved volatile phase associated with magma also contains minor species (halogens HF and HCl, H2, noble gases Ar and He, carbon monoxide, CO) and trace species (volatile metals and semimetals such as Cu, Zn and As, carbonyl sulfide and other trace C-O-H species) (Aiuppa et al., 2005; Mather et al., 2012; Mori and Notsu, 1997; Moussallam et al., 2014; Symonds et al., 1987). The composition of the exsolved volatile phase as measured at the surface in high-temperature gases depends on the magma composition (which is often linked to tectonic setting), as well as the pressure, temperature, and oxygen fugacity conditions at which the gas phase segregated from the magma (Burgisser et al., 2015; Gerlach, 1993; Gerlach and Nordlie, 1975; Giggenbach, 1975, 1996). As well as elemental abundances, the isotopic composition of the gases (e.g., carbon, hydrogen, or sulfur isotopes) also contain information about volatile sources (e.g., mantle, crust) and the processes which affected them (e.g., crustal assimilation, partial degassing, precipitation of a volatile-bearing liquid or mineral). During periods of unrest, when magmas may be intruded into the crust but not erupted, volatiles sourced from the magma body may ascend toward the surface. Carbon dioxide, owing to its low solubility in magmas (Fig. 2), may segregate and rise through permeable country rocks or faults, to give rise to “diffuse degassing” over large volcanic regions, or locally, on a volcano’s flanks. Diffuse emissions of hydrogen sulfide (H2S) may also occur (Fig. 2). Volatiles may be intercepted by the hydrothermal system, where SO2 and CO2 may be “scrubbed” to varying degrees (Symonds et al., 2001), through conversion to sulfate and bicarbonate. Monitoring the geochemistry of gases emitted from fumaroles and springs in restless caldera systems may reveal complex interactions between a deep, degassing magma body and an overlying brittle hydrothermal system (Chiodini et al., 2011; Tamburello et al., 2019). Geochemical monitoring of volcanoes seeks to quantify the variations in fluxes and compositions of magmatic components of gaseous and aqueous emissions, to understand the state of unrest, to forecast eruptive activity and in some cases to evaluate whether an eruption may have ceased. Geochemical monitoring may be used in tandem with geophysical monitoring methods, such as geodesy, seismicity, gravity, and strain, in order to constrain more precisely the presence of juvenile magma, magma ascent, and/or depth of storage. In this

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chapter, the methods commonly used to monitor volcanoes and volcanic regions geochemically are reviewed, along with the principal uncertainties. Key case studies are highlighted which exemplify the state-of-the-art in geochemical monitoring. Future directions and opportunities are discussed, including methods to broaden the reach of geochemical monitoring of volcanoes to remote and poorly studied locations; and to enlarge the geochemical diversity of elemental and isotopic species measured.

2 Measurements of fumarolic/vent emissions, volcanic plumes, soil gases, and springs The techniques employed to monitor volatile emissions from volcanoes range from direct collection of samples followed by laboratory analysis, to remotely acquired spectroscopic and other sensor data that may be transmitted in real time, to satellite-based observations of gas clouds. If collected regularly, samples of fumarolic gases or spring discharges at quiescent volcanoes may reveal important baseline data, which allows perturbations to the system to be readily identified when unrest begins. Sensors (electrochemical or spectroscopic) may be deployed in campaign mode, or in permanent mode, installed on the crater rim, with power and data logging/telemetry. Strategies for the interpretation of geochemical monitoring data are generally to understand the data in the context of “end members,” which may be magmatic, meteoric, crustal, or biogenic. If the composition of these end members is well known, it may be possible to deconvolve the sample into its components. For high-temperature gases, well-constrained thermodynamic models (Burgisser et al., 2015; Gerlach and Nordlie, 1975; Giggenbach, 1987; Newman and Lowenstern, 2002; Witham et al., 2012) may be used to understand the conditions of formation of volcanic gases, using key gas ratios as sensitive indicators of, for example, pressure, oxidation state. In this section the methods and data interpretation are discussed, with key examples, for each of the main settings where geochemical monitoring of volcanoes takes place.

2.1 High-temperature gases from fumaroles and active vents Fluids acquired from high-temperature vents are representative of quenched high-temperature magma-gas equilibria (Gerlach, 1993; Giggenbach, 1996; Giggenbach and Goguel, 1988; Symonds et al., 1994). The easiest, but perhaps most hazardous way to collect a gas sample is directly from a vent or fumarole, which makes it possible to acquire relatively undiluted gas samples, and analyze them down to parts-per-billion levels. The evacuated-bottle method (Giggenbach, 1975; Giggenbach and Goguel, 1988) utilizes a borosilicate glass bottle with a high-vacuum stopcock and sample port (Sutton et al., 1992) (Fig. 3A). The bottle is partially filled with concentrated aqueous sodium hydroxide, carefully weighed, and evacuated with a vacuum pump. A

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FIG. 3 Geochemical monitoring methods. (A) Direct sampling, showing (1) evacuated bottle method and (2) flow-through bottle method (Sutton et al., 1992); (B) the instrument configuration and optics for open path Fourier Transform Infra-Red spectroscopy (OP-FTIR) (McGonigle and Oppenheimer, 2003); (C) the DOAS method to acquire SO2 fluxes (Galle et al., 2003) using stationary scanning (Edmonds et al., 2003a); and (D) the principal components of the accumulation chamber method, to quantify soil CO2 flux over regions of diffuse degassing (Cardellini et al., 2017; Fischer and Chiodini, 2015).

chemically inert and heat resistant tube (typically made of titanium, alumina or silica) is inserted into a high-temperature fumarole or vent (Sutton et al., 1992). Once the tube has thermally equilibrated, the evacuated-sample bottle or flowthrough sample bottle is connected to the collection tube and the gases are collected for analysis. The gases from a fumarole bubble through the alkaline solution. Acid gases, such as CO2, H2S, SO2, HCl, and HF dissolve into the liquid. The remaining gases, such as N2, O2, H2, CO, and He, are collected in the headspace of the bottle (Sutton et al., 1992). The headspace gases are analyzed by gas chromatography while the gases in solution are analyzed by ion chromatography or wet-chemical methods. In the faster flow-through bottle method (Fig. 3A) a glass bottle with a stopcock at each end and a hand-operated pump are attached to the sampling tube. The pump flushes out air and draws the hightemperature gases into the bottle. This type of sample must be analyzed within a few hours of collection, as the high-temperature gas species inside the bottle are

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not chemically stable for longer periods. Hydrogen and helium may diffuse rapidly out of the container if not analyzed within a short timeframe. A number of techniques have been developed which allow volcanic gas compositions to be measured remotely, without the close approach necessary for direct sampling. One of the first methods to become established was open path Fourier Transform Infra-Red (OP-FTIR) spectroscopy (Fig. 3B), which may be used in passive mode, using the sun as a source of IR; or in active mode, using a lamp or a glowing vent as an IR source (Francis et al., 1998; McGonigle and Oppenheimer, 2003; Oppenheimer et al., 1998, 2006). OP-FTIR has been used to retrieve concentration ratios, using the principles of the Beer-Lambert Law, for SO2, HCl, HF, CO and for short, fixed paths in active mode, H2O and CO2. When combined with SO2 fluxes (see later), this method may be used to derive fluxes of these species. An advantage of OP-FTIR is a high degree of precision over short pathlengths for volcanic gases not present in the background atmosphere, owing to their strong absorption signature and a high degree of temporal resolution that allows short timescale variability to be recorded (Allard et al., 2005; Edmonds and Gerlach, 2007; Oppenheimer et al., 2006). Disadvantages are the expensive, large, and bulky spectrometer and telescope and the high-power requirements, which together preclude automated measurements. There are now a range of multispecies electrochemical and spectroscopic sensors that may be deployed near vents and fumaroles to measure key ratios in gases such as carbon/sulfur. The multi-GAS (multicomponent gas analyzer system) technique (Fig. 4) was first applied to volcanoes in the mid-2000s (Aiuppa et al., 2006, 2009; Shinohara et al., 2008b). The multi-GAS is based on assembling commercially available infrared and electrochemical gas sensors into a single sensor system. The system is low cost, lightweight (1 were linked to periods

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of lava dome growth at Soufrie`re Hills Volcano (when HCl was produced by decompressional degassing) and 25 (Kurz et al., 1982). Volcanoes on thick crust in subduction zones and on the continents (Lowenstern et al., 2014) may show signs of a strong component of crustal fluids, and exhibit lower R/RA, down to 60% of the 896 houses are located within a significant CO2 outgassing anomaly (Ferreira et al., 2005). In 1998, four families were evacuated from their residences because CO2 concentrations inside houses reached values of 8 mol% (Gaspar et al., 1998). Strategies to mitigate risk associated with volcanic gas hazards are in place at a number of locations and in progress at others. No formal early warning or alert system exists in the Azores, but there are soil gas flux spectrometers and soil temperature sensors located in Furnas village that telemeter data back to the Azores Monitoring Centre for Volcanology and Geothermal Energy in real time (Viveiros et al., 2010). At Mammoth Mountain, USA, risk mitigation measures include the posting of signs in prominent areas warning of the hazards associated with gas accumulations in topographic lows. For this lower level of hazard, this communication method is effective and has resulted in a largely safe enjoyment of the area by a largely educated public, despite the gas emissions. At Dieng, Indonesia, an improved network of telemetered arrays of sensors, webcams, and linked siren warning systems for the surrounding villages was approved for USAID/USGS funding in 2013. For future events, it is widely assumed that phreatic eruptions will be preceded by significant seismicity (Le Guern et al., 1980). Evacuations of far larger areas will be necessary to protect the population from the gas hazard and Early Warning Systems are needed to communicate encroaching hazards. Prolonged and sustained low-level emissions of sulfur dioxide from volcanoes present a significant hazard owing to their effect on the environment through acid rain and dry deposition of acidic volcanic gases, as well as for people, through respiratory diseases. At Kīlauea Volcano, during the long-lived 1983–2018 eruption, volcanic SO2 plumes were sourced from the summit and from eruption sites on the east rift zone (Longo et al., 2010). These emissions were dispersed by the trade winds, thereby impacting populations on the west coast, by which time the plume had transformed to volcanic fog (“vog”), a haze comprised largely of volcanic sulfate aerosol of a sufficiently small size to

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breathe in. Indoor SO2 concentrations on Hawaii Island during the eruption regularly exceeded the World Health Organization guidelines and during periods of enhanced volcanic outgassing, there were contemporaneous occurrences of acute respiratory conditions on the island (Longo et al., 2010). In response to the clear need for a system of monitoring and early warning, SO2 concentration sensor data from around the island were combined with SO2 emission rates and a model for plume dispersion to produce a vog model that forecasts air quality for the Hawaiian Islands. These warnings mitigated risks due to vog (Reikard, 2012). Advice to residents to minimize their exposure to vog once a forecast or warning for high aerosol concentrations was issued during particularly severe episodes, include closing windows and doors, limiting outdoor activities and exertion, and having medications on hand. Communication of vog warnings took place via the web, radio, field units, and road signs. At Masaya Volcano, Nicaragua, a persistent low-level SO2 plume has persisted for at least 25 years and probably centuries (Williams-Jones et al., 2003). The plume typically fumigates the higher ridges to the west of the volcano and has severely impacted plant communities there, causing leaf and fruit injury and dramatic decreases in diversity and growth rates (Delmelle et al., 2002). The impact on the local community is not well documented, but it is likely that the persistent fumigation of downwind villages represents a significant health hazard. Work is ongoing to develop an operational monitoring system for volcanic air pollution which will provide forecasts, real-time data, and public advisories and integrate with the preexisting Nicaraguan systems of disaster risk prevention and mitigation.

4 Limits to knowledge and future developments Geochemical monitoring has developed greatly over the past 2 decades, evolving from an infrequent, labor-intensive exercise in direct sampling, to a generation and installation of networks of automated sensors. These developments have elevated geochemical monitoring, allowing data to be compared on even terms with geophysical data such as seismic, tilt (Conde et al., 2014; Patrick et al., 2011), which has led to development of better constrained models to understand eruptive activity. Geochemical monitoring has emerged as one of the principal methods to observe volcanoes and many observatories around the world have installed DOAS networks or multi-GAS sensors generating continuous and real-time data. As the quantity of data produced at volcanoes globally increases however, it may be argued that the sophistication of the models we have at our disposal to understand them is more limited. Ultimately, the linkages between the monitoring streams must be cast in terms of physical and chemical processes and used to develop integrated models within which these data may be interpreted. Examples of these linkages include the presence of deep-seated exsolved volatile phases in magma reservoirs, which may be rich in CO2 and SO2 and linked

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to the shallow system to drive persistent degassing may also impose a much greater compressibility of the magma (Huppert and Woods, 2002). This heightened compressibility fundamentally changes the way in which the volume of reservoirs changes in response to injections of fresh magma from depth, and to batches of magma being removed by eruption, which is manifest at the surface as ground displacements, measured by GPS, tiltmeters, and satellite-based InSAR. Models which combine the thermodynamical evolution of the exsolved volatile phase with physical processes of recharge, eruption, and crystallization (Anderson and Segall, 2011; Degruyter and Huber, 2014; McCormick Kilbride et al., 2016) are required in order to utilize the full potential of the disparate monitoring data streams. Sensors are becoming ever-smaller, cheaper, and precise. Disposable sensors that are deployed as a cloud until they cease working or are destroyed, may be on the horizon, rather than costly installations which require servicing and replacement. Sensors that may be deployed in tandem with other geophysical sensors, in the same physical location, may reveal additional coherence and complexity in volcanic signals. Robotics and UAS systems are clearly an enormous opportunity for monitoring volcanoes, allowing approach where no human dare tread and the possibility of acquiring higher concentration, hotter volcanic gas samples close to source that have not been affected by in-plume processing and mixing. As well as elemental sensors of the principal species, measurements of trace species and the isotopes sulfur, carbon and other species may soon be possible routinely using spectroscopy. At the same time, the precision and capacity of satellite-borne sensors is rapidly improving, allowing better detection of tropospheric volcanic SO2 and raising the possibility of monitoring passively degassing volcanoes from space, which will be of incalculable value to remote and poorly resourced volcanic regions. As our technological and modeling capacity improves and becomes more complex and computer-hungry, it will be ever more important to ensure that knowledge, resources, and facilities are shared efficiently with countries hosting hazardous volcanoes around the world. It is clear that the effects of volcanic emissions on local communities are not well quantified in many parts of the world, particularly in less developed countries where such studies are limited by resource. It is the responsibility of global science and government bodies to tackle these problems for the good of all. Volcanic emissions contain not only particulates of sulfate aerosol but also heavy metals and other species; the impacts of these on human health are not well understood. Hazards exist not only through air but also when volcanic gases become dissolved in groundwater and find their way into aquifers and drinking water. A global challenge is to protect communities that live around volcanoes from hazards due to volcanic gases and develop low cost and reliable monitoring systems that can provide early warning of potential disaster. Such systems must be coupled with education on hazards which engenders safe behaviors and minimization of risks in volcanic areas.

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Chapter 5

A review of the physical and mechanical properties of volcanic rocks and magmas in the brittle and ductile regimes Yan Lavall
ee and Jackie E. Kendrick Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom

1

Introduction

Volcanic systems consist of igneous, metamorphic, and sedimentary rocks, as well as magma, and are among the most active settings on Earth. Their intense locus of magmatic, volcanic, structural, tectonic, and hydrothermal activity challenges the materials hosted in the system, leading to deformation as they relax the stress or build toward failure. The response and stability of volcanic materials is central to multiple processes: for magma this includes during melting, melt extraction, transport, emplacement, replenishment, mingling/mixing, crystallization, and eruption (including eruption style and flow emplacement of any type); for rocks, this includes wholesale elastic deformation and localized faulting events taking place during magma evolution and reaction, dyking and emplacement, structural resurgence, caldera subsidence, sector collapse, hydrothermal activity, and anthropogenic activities, such as bridge, dam, or nuclear storage constructions, and geothermal exploitation of volcanic systems, etc. Understanding the physical and mechanical properties of volcanic materials is thus central to the resolution of magmatic and eruptive processes, in order to model monitored signal via data inversion, to develop accurate volcano and fluid flow models, and to safely engineer infrastructures and applications (e.g., bridge construction, borehole drilling, geothermal exploitation, etc.). Laboratory testing and experiments, although costly and time-consuming, are crucial solutions to test hypotheses, expand our description of materials at all scales, validate geomechanical models, and achieve our primary goal in volcanology, namely, to increase the accuracy of volcano simulations to improve volcano hazard assessment and risk mitigation strategies (Mader et al., 2004). Forecasting and Planning for Volcanic Hazards, Risks, and Disasters https://doi.org/10.1016/B978-0-12-818082-2.00005-6 Copyright © 2021 Elsevier Inc. All rights reserved.

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Volcanic rocks and magmas are highly heterogeneous materials subjected to a wide spectrum of temperatures, stresses, strain and strain rates; as a result, their behavior may vary immensely. In the last two decades, volcanic materials have been increasingly targeted in volcanology, magma rheology, and rock physics studies. This is in part due to their poorly constrained heterogeneous nature—in contrast to extensive work on, for instance, sandstone, granite, limestone, which are commonly homogeneous in nature, a factor which improves experimental reproducibility—and in part owing to extensive technological developments, which allow us to study rheological and mechanical behavior at conditions relevant for magmatic and volcanic settings. Of particular relevance when attempting to reconcile the mechanics of volcanic rocks and their molten counterparts is the consideration of deformation modes. Rock deformation is commonly defined in the brittle and ductile field. Rutter (1986) reviewed these concepts, highlighting that deformation modes represent macroscopic failure of rock masses, which should not be confused with deformation mechanisms (e.g., viscous flow, crystal plasticity, cataclastic flow or fracture): brittle refers to the rupture of material along a localized fracture, commonly resulting in dilation; in contrast, ductile refers to pervasive deformation, commonly resulting in compaction. (Note that in its application to crustal deformation, the brittle-ductile transition should not be misinterpreted as the brittle-plastic transition which also takes place.) Magma deformation is perhaps a little less clearly defined. Experiments have defined flow and rupture of silicate melts (and some magmatic suspensions) in the viscous and brittle fields, respectively; yet, ductile deformation is commonly used to refer to viscous deformation, omitting other important deformation mechanisms which may contribute to magmas’ deformation, and as such the literature makes common misguided inferences about the ductile-brittle transition of erupting magma. We hope that this review will help clarify these distinctions, discussing properties in both the brittle and ductile fields. Here, we review the physical and mechanical properties of volcanic rocks and magmas in the brittle and ductile fields. In Section 1 we introduce volcanic materials, as well as their petrological and structural properties. In Section 2 we review our knowledge of the physical properties of volcanic materials, including porosity, permeability, and seismic velocity. In Section 3 we present the thermo-mechanical properties of volcanic rocks, including the compressive strength, the tensile strength, Young’s modulus, the thermal properties (expansivity, thermal stressing and cracking, and mineral breakdown), as a function of deformation rate, temperature and pressure (when such variables have been constrained). In Section 4 we introduce the rheology of silicate melts (viscosity, relaxation, and failure criterion) and magmas (effective viscosity, crystal plasticity, and failure criteria), as a function of deformation rate, temperature, and pressure (when such variables have been constrained). We then integrate our knowledge of magma rheology with that of rock deformation to present the first deformation mechanism map (DMM) portraying magma’s rheological regimes,

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including a re-conciliation with the fragmentation threshold. In Section 5 we discuss material rupture, including an appraisal of damage architecture, monitoring, seismic signatures, and failure forecast). Finally, in Section 6 we review the tribological properties (friction coefficient and comminution) of materials following rupture and slip. Altogether, this is a broad pallet of properties and we remind the reader that the scope of each subject tackled here is vast and as such the depth of the review varies and some factors may not be covered. There is an extensive body of work on igneous rocks, which is challenging to summarize comprehensively. We disclose that this review is biased toward volcanic rocks and magmas, in order to review the recent body of works done primarily in the last two decades.

2

Physical properties of volcanic materials

Volcanoes comprise complex successions of physically, mineralogically, and structurally heterogeneous rocks, which may exhibit variable states of physical coherence, alteration, and saturation with fluid (Karlstrom et al., 2018; Lavall
ee et al., 2019; White and Houghton, 2006). Structurally volcanic rocks display a wide range of coherence, as they exhibit damage at different scales, from the presence of kilometer-long fractures and faults (Einarsson, 1991; Goldfinger et al., 1997; Gutmanis, 1989; Stern, 2004), down to metric mesofractures in certain units (Cashman and Cashman, 2006; Pallister et al., 2013) and microfractures common to the majority of volcanic rocks (except for some obsidian blocks which may occasionally be devoid of fractures). Mineralogically, volcanic rocks can vary greatly, owing to the wide-ranging chemical compositions and pressure-temperature-strain history of magmas from which they originate (Lesher and Spera, 2015). As a result, volcanic rocks commonly reveal heterogeneous textures with varying crystallinities, mineralogical assemblages, and fabrics, surrounded by interstitial glass (i.e., a metastable, amorphous phase abundant in magmatic scenarios where magma has been forced to cool relatively quickly), which impact their petrophysical properties. Magmas and lavas are equally diverse materials, as revealed by the spectrum of eruption styles. The rock record exposes a broad range of igneous structures, suggesting that magmatic bodies vary widely in size, shape, and physicochemical character through time and space (Edmonds et al., 2019; Hildreth, 1981; Marsh, 1988, 1996). Magmatic bodies may likely reach up to several tens of kilometers in size as constrained by plutons (Marsh, 1996; McCaffrey and Petford, 1997), caldera collapse geometries (Lipman, 1997), and the volumes of erupted materials (Bachmann et al., 2007; Bachmann and Bergantz, 2008; Bryan et al., 2010). The flow of eruptive products (lava and pyroclastic density currents) is gravity driven, as such they tend to pond in depressions (i.e., caldera structures, valleys, etc.) and their extent is often laterally constrained. The majority of intrusive magmatic bodies may be rather small, and their longevity (in the molten state) likely increases with size, especially if an underlying heat

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source is sustained (Annen et al., 2006; Taylor and McLennan, 1995). Their presence in a volcanic system, inferred from the occurrence of volcanic activity or serendipitous encounters during geothermal drilling (Elders et al., 2014; Mortensen et al., 2014), is currently difficult to resolve using geophysical data inversion (Greenfield and White, 2015; Lin et al., 2014; Schuler et al., 2016) as we have few examples of ground-truthing our models. An example that may form a strong basis to develop such geophysical models is the known presence of a magma body at 2.1 km depth below the surface at Krafla volcano, Iceland (Mortensen et al., 2014), which shows a faint seismic velocity anomaly and seismic clustering (Schuler et al., 2015, 2016). Different models have been suggested to illustrate the geometry of magmatic systems, with isolated magmatic reservoirs and complex architecture of vertically extensive and variably connected sills, dikes, and laccoliths (Cashman et al., 2017). Whereas at depth, we may envisage magma as coherent bodies, the examination of shallow dikes suggest that magma may be fractured and brecciated in upper volcanic conduits. Drilling at Unzen volcano, Japan, has shown that dikes in the subsurface conduit area of the volcano may be as narrow as some tens of centimeters to a few meters following an eruption (Ikeda et al., 2008; Noguchi et al., 2008; Sakuma et al., 2008); even if, in this case, a 20-m wide spine of high-viscosity lava was extruded (Coats et al., 2018; Cordonnier et al., 2009; Goto, 1999). This indicates that the size of magmatic conduits varies upon stress changes during and at the end of an eruption. Finally, the crystallinity of magma varies widely in magmatic systems (up to 100%). Whereas in a magma reservoir prolonged timescales at a narrow range of P-T-X conditions promote the growth of crystals to similar sizes, magma transported in dikes/sills, and erupting at the Earth’s surface generally exhibits more heterogeneous porphyritic textures, with variable fractions of melt (0%–100%).

2.1 The porosity of volcanic rocks and magmas The porosity (and density) of rocks is commonly determined via Archimedes principles and via pycnometry. The Archimedean method (developed and employed during the Hellenistic period, c.250 BCE; Heath, 1879) has long been used in volcanology as it simply relies on weight changes associated with the buoyant force exerted on a body immersed in a fluid. This method has been used in the laboratory and field. In the field, it is increasingly used to measure rock density and porosity at low cost, allowing large data gathering (Farquharson et al., 2015; Kueppers et al., 2005; Lavall
ee et al., 2012c, 2019), which has enabled a reconciliation of volcanic activity with eruptive products as displayed in Fig. 1 (Mueller et al., 2011). Pycnometry relies on estimates of a volume of a body inaccessible to gas, commonly using helium; thus, the method is generally employed in the laboratory. Previous studies have shown that helium (i.e., the smallest of the single-atom gases with a diameter of 62 pm), is able to access a more complete fraction of the porous network of rocks than water, as such

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FIG. 1 Relationship between the volcanic explosivity index (VEI) and the mean porosity of associated eruptive products. The explosivity and porosity share a nonlinear positive correlation (blue curves); deviations from this relationship have been noted towards lower level of explosivity for basaltic magmas (e.g., Hawaiian and strombolian activity) and toward a higher level of activity for cryptodome eruptions (such as at Bezymianny and Mount St. Helens). The abbreviations are: ARK, Anak Krakatau 1999; AUG, Augustine 1986; BEZ; Bezymianny 1956; CI, Campanian Ignimbrite 37,000 BP; CH, Crater Hill (unknown); COL, Colima 1998–2005; KAR, Karymskoye lake (unknown); KEL, Kelut 1990; KIL, Kilauea Iki 1959; KRK, Krakatau 1883; MRP, Merapi 1998; MSH, Mount St. Helens 1980; NOV, Novarupta 1912; ROT, Rotokawau (unknown); STR, Stromboli 2002; TAU, Taupo 1800 BP; UNZ, Unzen 1990–1995. (Data from Mueller, S., Scheu, B., Kueppers, U., Spieler, O., Richard, D., Dingwell, D.B., 2011. The porosity of pyroclasts as an indicator of volcanic explosivity. J. Volcanol. Geotherm. Res. 203, 168–174.)

pycnometry using gases of small atomic or molecular size, such as helium or nitrogen, generally provides a more accurate quantification of rock porosity (e.g., Heap et al., 2014). More recently, tomographic imaging of laboratory specimens has also started providing us with relatively sharp reconstructions of the porous network, but this method generally fails to quantify pore space much smaller than one micron and if it is performed, it is at the expense of time and depends upon the total rock volume imaged. The porosity of volcanic rocks ranges from 0% to 97%. Equally, the density of volcanic rocks can vary widely (from 10%). For instance, c.44 wt% CO2 is released during complete calcination of pure limestone (e.g., calcite), which creates
40% porosity and decreases in

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strength by 60%–80% (Heap et al., 2013), thus its presence in veins and pore space of volcanic materials should not be overlooked. Similarly, xeolite may be common in ignimbrites, for example the Neapolitan Yellow Tuff has been shown to lose 16.5 wt% during dehydration, which may weaken the rocks by
80% (Heap et al., 2012). Presumably, the effects of mineral precipitation are opposite and of similar magnitudes, although they are infrequently constrained. Yet the work continuously carried out on such important processes suggest that alteration and mineral breakdown may play central roles on rock stability in volcanic environments subjected to temperature fluctuations (Day, 1996).

3.5 Mechanical properties of volcanic rocks at elevated temperature Volcanic rocks commonly experience elevated temperature in volcanic systems; yet, few studies have investigated the influence of temperature on the mechanical properties of volcanic rocks. Recent studies have shown that the compressive strength of volcanic rocks (at elevated temperature) holds a similar inverse relationship with porosity, however the strength may increase moderately with temperature (Fig. 8; Coats et al., 2018; Heap et al., 2018a; Lavall
ee et al., 2019; Schaefer et al., 2015), provided that the rock-forming minerals do not break down at high temperature (Heap et al., 2012), and provided that the strain rate remains sufficient to ensure purely brittle behavior. Similarly the tensile strength of volcanic rocks can increase with temperature (Hornby et al., 2019) or may be inconclusive (Benson et al., 2012; Lamur et al., 2018). These relationships have been observed for rocks tested in the brittle regime (i.e., at temperatures below the solidus or the glass transition temperature; see Section 4). In particular, the brittle-ductile transition of hot glassy volcanic rocks (above the glass transition) can shift to lower pressure conditions (than at ambient temperature), owing to increased diffusion that enhances viscous relaxation, and which may favor compaction over dilatant rupture (Violay et al., 2012); yet, such a transition is rate dependent (Coats et al., 2018; Dingwell and Webb, 1989, 1990; Lavall
ee et al., 2007, 2008, 2019), thus thermo-kinetic considerations are required to assess the yield envelope and deformation modes of volcanic rocks (see Section 4). Of note, the Young’s modulus typically decreases with increasing temperature for both glass-bearing (Coats et al., 2018; Smith et al., 2009) and glass-free lavas (Benson et al., 2012; Lamur et al., 2018; Rocchi et al., 2004; Schaefer et al., 2015), with some exceptions attributed to closure of fractures during thermally induced expansion (Heap et al., 2018a, 2019b). The findings of recent studies conducted at high temperature highlight that caution must be exerted when modeling large-scale behavior using mechanical data obtained via laboratory tests at ambient conditions; in hot environments, modeling efforts must ensure careful thermo-kinetic considerations.

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FIG. 8 Temperature-dependence of strength for porous volcanic rocks. (A) Compressive strength of dacite (Coats et al., 2018) and basalt (Schaefer et al., 2015) at 20°C versus 900°C, and deformed at a rapid strain rate of 103 s1 (to prevent viscous flow and ensure a fully brittle response). The data shows a mild strengthening of volcanic rocks with temperature, especially in the case of the dacite. (B) Temperature-dependence of the compressive strength of dense basalts (porosity