Electromagnetic Methods in Geophysics: Applications in GeoRadar, FDEM, TDEM, and AEM [1 ed.] 111977098X, 9781119770985

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Electromagnetic Methods in Geophysics Applications in GeoRadar, FDEM, TDEM, and AEM Fabio Giannino

IDS GeoRadar s.r.l. Pisa, Italy

and Giovanni Leucci

Institute of Heritage Sciences (ISPC) National Research Council of Italy (CNR) Lecce, Italy

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This edition first published 2022 © 2022 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Fabio Giannino and Giovanni Leucci to be identified as the authors of this work has been asserted in accordance with law. Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging‐in‐Publication Data Names: Giannino, Fabio, author. | Leucci, Giovanni, author. Title: Electromagnetic methods in geophysics : applications in GeoRadar,   FDEM, TDEM, and AEM / Fabio Giannino and Giovanni Leucci. Description: Hoboken, NJ : Wiley, 2022. | Includes bibliographical   references and index. Identifiers: LCCN 2021027149 (print) | LCCN 2021027150 (ebook) | ISBN   9781119770985 (hardback) | ISBN 9781119770992 (adobe pdf) | ISBN   9781119771005 (epub) Subjects: LCSH: Magnetic prospecting. | Ground penetrating radar. Classification: LCC TN269 .G433 2022 (print) | LCC TN269 (ebook) | DDC  622/.153–dc23 LC record available at https://lccn.loc.gov/2021027149 LC ebook record available at https://lccn.loc.gov/2021027150 Cover Design: Wiley Cover Image: © Fabio Giannino Set in 10/12pt Times New Roman MT Std by Straive, Pondicherry, India

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CONTENTS Preface�������������������������������������������������������������������������������������������������������������������������������������������������������������������v Acknowledgements���������������������������������������������������������������������������������������������������������������������������������������������viii

Section I: Introduction to Electromagnetic Theory 1. Introduction....................................................................................................................................................3 2. Electromgnetic (EM) Theory: An Outline.........................................................................................................5 2.1. Ground Penetrating Radar (GPR): Operative Principles and Theory.........................................................5 2.2. ­Frequency Domain Electromagnetic (FDEM) Method: Operative Principle and Theory..........................14 2.3. ­Time Domain Electromagnetic (TDEM) Method: Operative Principle and Theory..................................21 2.4.  Airborne Electromagnetic (AEM) Method: Operative Principle and Theory............................................26

Section II: Hardware Architecture and Surveying 3. GPR Surveying..............................................................................................................................................33 3.1. ­GPR: Systems Architecture��������������������������������������������������������������������������������������������������������������������33 3.2. ­Survey Design���������������������������������������������������������������������������������������������������������������������������������������51 3.3. ­Data Acquisition....................................................................................................................................53 3.4. ­Data Analysis.........................................................................................................................................65 3.5. ­Data Interpretation.................................................................................................................................70 4. FDEM Surveying............................................................................................................................................83 4.1. ­FDEM Systems Architecture...................................................................................................................83 4.2. ­Survey Design........................................................................................................................................85 4.3. ­Data Acquisition....................................................................................................................................86 4.4. ­Data Analysis.........................................................................................................................................91 4.5. ­Data Interpretation.................................................................................................................................93 5. TDEM Surveying...........................................................................................................................................97 5.1. ­TDEM Systems Architecture...................................................................................................................97 5.2. ­Survey Design......................................................................................................................................100 5.3. ­Data Acquisition..................................................................................................................................101 5.4. ­Data Analysis.......................................................................................................................................102 5.5. ­Data Interpretation...............................................................................................................................103 6. AEM Surveying............................................................................................................................................107 6.1. ­AEM Systems Architecture....................................................................................................................107 6.2. ­Survey Design......................................................................................................................................111 6.3. ­Data Acquisition..................................................................................................................................111 6.4. ­Data Analysis.......................................................................................................................................113 6.5. ­Data Interpretation...............................................................................................................................118

Section III: Applications 7. Case Studies................................................................................................................................................123 7.1.  GPR: Multiple Geophysical Archaeological Surveys in Turkey.............................................................124 7.2.  ­GPR: Massive Array Archaeological Survey in Italy..............................................................................143 7.3.  ­GPR: Archaeological and Monumental Application at a Cathedral in Italy..........................................148 iii

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iv CONTENTS

7.4.    ­GPR: Archaeological Application in Peru..........................................................................................157 7.5.    ­GPR: Monumental Heritage Conservation at a Hypogeal Site in Italy................................................163 7.6.    ­GPR: Concrete Rebars Detection in South‐Europe.............................................................................169 7.7.    ­GPR: Concrete Rebars Detection and Water Content Estimate in Italy...............................................174 7.8.    ­GPR: Large Area Underground Utility Mapping in Italy.....................................................................191 7.9.    ­GPR: Utility Mapping and Fibre Optics Reconnaissance in Scandinavia...........................................198 7.10.  ­GPR: Utility and Cavity Mapping in Taiwan......................................................................................204 7.11.  ­GPR: Pipes Leakage Detection Experimental Test and Application in Italy.........................................210 7.12.  ­GPR: Pipes Leakage Detection in Italy..............................................................................................219 7.13.  ­GPR: Bridge Deck Study in Japan......................................................................................................225 7.14.  ­FDEM: UXO Search in Building Area in Italy.....................................................................................231 7.15.  ­FDEM: Search of Various Object in a Test Site in Italy.......................................................................235 7.16.  ­FDEM: Pollutants Search in Italy........................................................................................................245 7.17.  ­FDEM: Forensic Search in South Europe............................................................................................251 7.18.  TDEM: Geologic Modelling for a Reference Site in Italy....................................................................255 7.19.  AEM: Geophysical and Geological Modelling of Buried Valleys........................................................262 7.20.  AEM: Effect of Induced Polarization Over AEM Data.........................................................................274 8. General on Planning and Logistic................................................................................................................291 8.1. ­   Planning a Campaign and Mobilization Aspects................................................................................291 8.2. ­ Shipment and Clearance of Survey Equipment..................................................................................292 8.3. ­ Managing the Operative Aspects of the Field Activity........................................................................293 8.4. ­ De-­Mobilization...............................................................................................................................294 8.5.   Reporting.............................................................................................................................................294 Index������������������������������������������������������������������������������������������������������������������������������������������������������������������295

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PREFACE Geophysical techniques have many environmental, archaeological, forensic, geological, geotechnical, or engineering applications, as well as in the oil and gas, the mining industry, or for general academic research. Because of this, all the aspects connected with the logistics, designing, data collection, analysis, interpretation, and visualization, must be evaluated on a case-­by-­case basis. Regardless of the geophysical technique deployed for a specific subsoil exploration campaign, the final objective is always to search for the variations of a specific physical property of the system to be investigated, and to infer the possible anthropogenic or natural factor(s), which caused the variation itself. The properties which, more often, are the main subject of an applied geophysics measurements campaign are the electrical conductivity (electrical resistivity), the seismic waves velocity propagation (either in their vertical and/or horizontal component), the EM waves velocity propagation, the dielectric constant, the magnetic field, the gravity acceleration, and so on. Measuring of the abovementioned properties, in boreholes, on surface, in the sea, or airborne, is carried out with instrumentation specifically designed and manufactured, and the data gathered through these tools, are analyzed by means of specific software, whose proper use allows even very small variations of a given quantity to be highlighted. The survey and laboratory/office operations addressed to the acquisition and analysis of such specific data, result in those geophysical methodologies known as Geoelectrical techniques, EM induction methods, seismic methods (seismic refraction, seismic reflection, seismic tomography, MASW, Re.Mi.), ultrasound, Ground Penetrating Radar (GPR), Magnetic method, Micro-­gravimetry, and so on. The list outlined is obviously partial, but on the other hand may be useful to give an idea of the macro-­areas within which the most commonly used geophysical techniques can be allocated, with respect to the industry, the  academic research, or in the professional services activities. Tools deployed for the data acquisition, as well as the software to be used for the purposes of data processing and visualization, improved very much in the last three decades, and became rather complex and of a specific use. Moreover, the possibility to apply a specific technique for the purposes of a given project, cannot disregard the knowledge of the theoretical basis on which every geophysical technique is founded on. As a direct consequence

of this, a poor awareness on the hardware, the software, and the theoretical aspects, will most likely lead to a geophysical approach, whose possibility of technical success is low. Another very important factor in planning and performing a geophysical survey, is the collection of the greater possible amount of information relating to the nature, dimension, geometry, and burial depth of the “target” of the measurements campaign, and the (ge­ological) background where the “object” is imbedded. In this case, the word “target” means every buried feature determining a contrast in physical properties detectable by the technique. The knowledge of this, can ositively contribute to selecting the geophysical also p­ technique, which increases the probability to maximize the differences of a given physical property between the background and the target. Hence, the possibility to locate the latter with a higher degree of confidence and increasing the resolution of the final result. Many geophysical techniques are based on the propagation in the space of electromagnetic (EM) waves (wave field methods), and a unique classification of them can be difficult and, to a certain extent, of no use. However, in order to optimize the results deriving from their application to project-­related issues, it is of a paramount importance to have a clear idea about the existing EM techniques and what are the characteristics differentiating one from another. To do this, it may be used as a classification factor, at a first instance, the fact that a known amplitude and frequency transmitter may be used as a source of EM energy, as it happens for the TDEM methods, FDEM methods, as well as in the GPR techniques; on the other hand, also the interaction with the subsoil of natural sources of EM energy can be used as EM energy, as in the magnetotelluric methods (MT), the audio magnetotelluric (AMT), or in the audio frequency magnetotelluric (AFMAG). Under this point of view, EM techniques may be classified into active, the former, and passive, the latter. Another way to differentiate one EM technique or one group of EM techniques, is the fact that the EM signal generated from an AC current having a known amplitude and frequency content, is emitted continuously (FDEM methods), or that the transmitting coil spreads out a transient EM signal of known amplitude and frequency, and that a receiver coil measures the time decay of the emitted EM signal interacting with the subsoil, after that the transmitter is been switched off (TDEM methods). v

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vi Preface

EM methods can be further classified based on the relative position between transmitter and receiver coils: these can have a fixed or variable relative distance. On the basis of this aspect we may distinguish the Turam method (D.S. Parasnis,  1979), the Slingram, the ground conductivity meter (GCM), the Very Low Frequency (VLF). Furthermore, P.V. Sharma (1996), introduced an EM methods classification, depending on the fact that the primary EM field may be continuous, transient, or broadband; according to this we may have: ••EM continuous wave field methods (FDEM) ••EM transient field methods (TDEM) ••Magnetotelluric methods (MT) Regardless, all EM methods are deployed with the common aim of characterizing the subsoil on the base of its electrical conductivity and dielectrical constant. As is known, the purpose of these methods is to deduce the physical properties of the Earth and its internal constitution from the physical phenomena associated with it. On the other hand, the objective is to investigate, with a very high resolution and a relatively smaller scale, more superficial features present in the Earth’s crust. Typically, the investigation of these characteristics provides an important contribution to practical problems, such as oil exploration, the identification of water resources, mining exploration, pollutant research, bridge and road construction, and civil engineering. The presence of bodies or structures in the subsoil is highlighted by measuring at the surface variations of some physical parameters in the subsoil itself. In practice, some  measurements of a given physical field (i.e. electromagnetic) are carried out at the surface of a given area. If the subsoil were perfectly homogeneous, regardless of the position in which the measurement is carried out, the same value of the measured physical parameter would always be obtained. Assuming, instead, that in a certain position of the subsoil there is a body with different physical properties compared to the surrounding material, when the measuring instrument passes in correspondence with the body, the measured value tends to deviate from the unperturbed value, and the observed physical field assumes a value, defined as anomalous, i.e. a variation with respect to the reference value relative to an homogeneous situation (anomaly). Since each EM method is sensitive to the contrast of particular physical parameters (electrical conductivity, relative dielectric constant, etc.) of the object under investigation with respect to the surrounding environment, it is understandable that the greater or lesser effectiveness of the one with respect to the other depends on the extent of the contrast of the corresponding physical parameters. Therefore, the choice of the most suitable EM methods for a particular problem is strongly dependent on the

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objective and is essentially guided by the identification of the physical parameters of the object to be identified that present the greatest contrast with the host environment, and therefore they allow greater ease of detection, as well as considerations of an economic and logistical nature. EM methods are often used in combination. Thus, for example, the search for illegal landfill takes place at an early stage with the use of GPR and FDEM methods. The ambiguities resulting from the results of a single method can be removed by considering the results obtained by using a second method. For example, the reflections in a GPR survey due to the presence of a wall or a buried pipe could be similar (a hyperbole shaped reflection). By integrating the GPR survey with another EM survey, this ambiguity can be solved considering that relatively low conductivity values could be associated with the wall, while relatively high conductivity value could be associated to the buried pipe. It is important to stress that, although an interpretation of the results of the, here described, EM methods requires relatively advanced mathematical treatments, initial information, as will be shown in the book, can be obtained from the simple observation of the acquired data. More in general, the methodological characteristics of the EM techniques, leads to a number of advantages, as it follows: ••High degree of horizontal resolution in mapping apparent electrical conductivity: data acquisition and management software to be interfaced with electro-­ magneto meters, allowing the sampling frequency to vary. This allows for a very high number of data points to be selected acquisition collected while walking along pre-­ alignments. ••High degree of horizontal and vertical resolution in mapping EM reflection events. ••Reducing the data acquisition time (i.e. the field work): As the EM method is based on the EM induction principle, no contact between sensors and the soil to be investigated is required, as normally happens for geoelectrical methods, where steel rods must be embedded on the surface of the field to be investigated in order for the current to be injected. This occurrence allows for the EM data to be collected while walking, or driving, or flying along acquisition lines pre-­defined within the survey area. ••Survey cost reduction: due to the previous point, it follows that the data acquisition costs dramatically reduce, compared to other geophysical techniques to be deployed for the same purposes and over the same areas. As for any geophysical technique, also EM methods shows some limitations: ••Instruments calibration before each survey operation: measurements of the secondary EM fields due to the

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Preface  vii

interaction between the Primary EM field generated by the transmitter coil and the subsoil, is performed through a ratio with respect to a reference signal. For this reason, a test EM measure over an area where no EM anomalies should be located, has to be done prior to the commencement of the actual survey. ••Vertical resolution is limited in FDEM and TDEM: the electrical conductivity datum to be collected, refers to a volume of subsoil located at the medium point between the transmitter and the receiver, and it must be considered as an “apparent” conductivity datum. ••Dynamic Range is reduced: as highlighted in McNeill (1980) for induced EM methods, when the subsoil shows very low electrical conductivity values (i.e. very resistive subsoil) it is rather difficult to induce electric current in the subsoil by the use of a electro-­magneto meter, capable to generate, in turn, eddy current large enough for a secondary magnetic field (induced) to be measured by a receiver coil with a dynamic range between 1 and 100 mS/m. Some of the fields of applications allowing for the intrinsic properties of the EM to be enhanced along with their expected final results, are: ••Mapping of saline water intrusion ••Buried metallic utilities mapping ••UXO mapping ••Cavity search (under given subsoil conditions) ••Utility mapping ••Mapping of pollutants plumes ••Mapping of un-­authorized landfill areas ••Forensic geophysics ••Archaeological geophysics ••Buried metal search, in general ••Mineral resource research. Obviously, this is only a partial list of the potential application of the EM methods and may not be considerate complete; each geophysical measurement campaign should be designed and planned to take into account criteria strictly project specific and target oriented.

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Throughout the book, an in-­depth view into the theory and application of four Electromagnetic geophysical techniques known as Ground Penetrating Radar (GPR), Frequency Domain Electromagnetic (FDEM), Time Domain Electromagnetic (TDEM) and Airbone Electromagnetic (AEM) shall be given. Also, each technique shall be considered in its general aspects related to economical, planning, and logistic aspects that are an integral part of the deploying activity on site. As a further aspect that we attempt highlighting in this book, is that the output of each technique should/could be considered also in terms of its potential integration with the output of other source of information, collected either below the ground and above the ground, in a further effort of digitizing the global information describing the whole surrounding, in a common point cloud containing much information of a different nature, and for potentially different applications and use. This general concept takes place in what is nowadays known as Smart-­Cities, where many sources of information are collected by many sensors, analyzed together, and made available to stakeholders for the optimization, maintenance, and use of assets being part of a urban or industrial context. More than 25 years of professional experience, collected in over 40 Countries world-­wide, for academic, research, professional, and industrial purposes, results in this manuscript that rather then enter into the deep details, aims at describing the optimal use of a limited number of geophysical techniques and its implementation to several application, demonstrating their flexibility. ­REFERENCES Parasnis, D.S. (1979). Principles of Applied Geophysics. Third edition, Chapman and Hall. Sharma, P.V. (1997). Environmental and engineering geophysics. Cambridge University Press.

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ACKNOWLEDGEMENTS The authors are grateful and wish to offer thanks for their support, comments, suggestion, review, to: Lara De Giorgi, Ivan Ferrari and Francesco Giuri, Institute of Heritage Science (National Research Council of Italy). Alberto Bicci, President of IDS GeoRadar s.r.l. Part of Hexagon.

Vincenzo Sapia, Istituto Nazionale di Geofisica e Vulcanologia (RU Applied Geophysical Measurements Laboratory, Italy). Andrea Viezzoli and Antonio Menghini, Aarhus Geofisica s.r.l. Prof. Enzo Rizzo, University of Ferrara (Italy).

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Section I Introduction to Electromagnetic Theory

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1 Introduction

the available assets, and the need to collect high quality data. Eventually, still within the context of the description of an EM survey, all the most relevant aspects connected to the data acquisition, analysis, visualization, and interpretation, shall be discussed. The third section is dedicated to the applications, and several case histories shall be illustrated. These will be proposed with the aim of highlighting the technical and practical aspects that may be of interest for the geophysicist approaching these techniques. Cases illustrated in this ­section were selected with the aim of covering a wide geographical context but, at the same time, the largest possible number of different applications including archaeological and monumental heritage study, utility mapping, rebars detection, water leakage mapping, bridge deck study, mineral exploration, geological and hydrogeological mapping. In the same sections all those aspects are illustrated related to non-­technical parts involving the logistics and the handling of a survey in terms of its organization and implementation, even when the shipping of material overseas is part of the campaign.

The framework of the following pages is structured into three sections. In the first section, the theoretical basis on which the GPR, FDEM, TDEM, and AEM techniques are founded, shall be illustrated without entering into the very deep physical and mathematical aspects, which are beyond the purposes of this text. However, the theoretical aspects shall be treated with a detail allowing the Reader to have a sufficient familiarity with those features that makes the methods themselves particularly suitable for specific applications. This will also allow the reader to comprehend how the EM instruments are built by the manufacturer, worldwide. This specific aspect is treated in the second section, where the system’s hardware architecture is illustrated, as well as showing how the instrumentation is designed and manufactured with the aim of maximizing the capability to detect the variation of physical properties of the subsoil, down to a given depth. Also in the same second sections, all the aspects connected with the design of a survey campaign related to the EM methods will be analyzed, in order to reach the best achievable compromise between the client’s requirements and technical specifications, the survey area’s logistics,

Electromagnetic Methods in Geophysics: Applications in GeoRadar, FDEM, TDEM, and AEM, First Edition. Fabio Giannino and Giovanni Leucci. © 2022 John Wiley & Sons, Inc. Published 2022 by John Wiley & Sons, Inc. 3

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2 Electromgnetic (EM) Theory: An Outline

2.1. ­GROUND PENETRATING RADAR (GPR): OPERATIVE PRINCIPLES AND THEORY

(as those devoted to the biennial International Conference on GPR held since 1986), and numerous research papers. Although in earlier times GPR data were generally used and interpreted as they were collected (the so-­called raw data), they are now routinely subjected to digital data-­processing, interpretation, and display techniques aiming to further enhance the visibility of meaningful signals in the raw data, and to help in understanding their three-­dimensional relationships. Due to the close kinematic similarity with seismic reflection methods, most of the processing and visualization techniques currently available in GPR processing software are a direct adaptation of the seismic ones. The physical bases and mathematical foundations underlying these techniques are therefore available from seismic literature (Yilmaz, 1987) and most recently Persico, 2014. Nevertheless, although without presuming to furnish a deep examination and an exhaustive treatment of the theoretical and practical aspects of the GPR method, the main basic principles underlying the acquisition and processing of GPR data, needed for the comprehension of the tasks faced in the next chapters, are concisely exposed in the following pages.

2.1.1. General The Ground Penetrating Radar (GPR), also known as Georadar is one of the most widely accepted and used geophysical methods for the exploration of the shallow subsurface, especially but not limited to civil engineering, geological studies, utility mapping, environmental, or archaeological applications. Its ability to provide, easily and quickly, high-­resolution and continuous information on the uppermost few meters (up to tens of meters) of the natural or man-­made surface, heavily contributed to the increasing popularity of this method and to its expanding role among the shallow geophysical techniques in the last two decades. Nevertheless, the same reasons could make this method highly subjected to misuse. For a successful application of the GPR technique, as well as any other methods for underground mapping, it is necessary not only to understand its fundamental principles, but also its general characteristics and limitations in relation to the practical application for which it is required its deployment on site; this information, addresses the user to develop suitable field and post-­acquisition procedures for the specific problem at hand. Dissertation on the theoretical basis, practical guidelines, as well as numerous case histories on GPR studies in various fields of applications, can be found in recent literature, such as books (Leucci,  2019; Conyers,  2004; Conyers,  2013), geophysical handbooks (Campana and Piro,  2008; Reynolds,  2011; Persico et  al.,  2018), Proceedings and Special Issues of geophysical journals

2.1.2. Principles of the Method The GPR technique is similar, in principle, to the seismic reflection technique but, instead of mechanical waves, it uses high frequency (10–2500  MHz) electromagnetic pulses to explore the underground. A radar wave, emitted by a transmitting antenna (a transmitter antenna, or transmitter, is generally indicated with “Tx”) placed directly above the ground surface,

Electromagnetic Methods in Geophysics: Applications in GeoRadar, FDEM, TDEM, and AEM, First Edition. Fabio Giannino and Giovanni Leucci. © 2022 John Wiley & Sons, Inc. Published 2022 by John Wiley & Sons, Inc. 5

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6  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM

propagates in the ground and it is partially reflected by any change in the electrical properties of the subsoil. The reflected energy is then detected by the receiving antenna (a receiving antenna, or receiver, is generally indicated with “Rx”). This basic concept is schematized in the simple sketch of Figure 2.1.1, below. Georadar antennas have a relatively large frequency band, whose width is approximately equal to the center-­ frequency, that is the frequency around which most of the pulse energy is concentrated. For example, if the center-­ frequency of emission of the transmitter dipole is 600 MHz, the frequency band is approximately between 300 MHz and 900 MHz. However, the intrinsic characteristics of emission, primarily depends upon the manufacturers technical specifications and technology. Most GPR equipment uses dipole antennas (identified by their center-­frequency or by the pulse width, approximately corresponding to the reciprocal of the center-­ frequency) arranged either in monostatic or in bistatic configurations. In the first case (monostatic mode) the same antenna is used for transmission and reception and the Tx and Rx dipole are contained in the same antenna case and a fixed distance from each other. In the second case (the bistatic mode) there is a constant, small offset between the two antennas, that can be placed either in separated cases (as for the low-­frequency antennas) or inside the same box (as for the higher-­frequency ones). Generally, the offset is sufficiently small that it can be practically neglected, and the last arrangement could be considered nearly monostatic. For both arrangements the Computer

Control unit

Transmitter - receiver seletcor Rx-Tx antenna

Reflected wave

Transmitted wave

Target

usual data acquisition is the reflection mode, performed either as continuous profiling (moving the antennas along the profile at a slow, near constant towing speed) or as stationary point collection (shifting them stepwise). GPR data, properly amplified, are then recorded and displayed as a two-­dimensional section with the antenna positions (or midpoint positions in case of bistatic systems) in the horizontal axis (Figure 2.1.2 a) and the two-­way travel time in the vertical axis (Figure 2.1.2 b and c). This section can be considered a normal-­incidence time section (corresponding to the zero-­offset section of the seismic reflection), where the two-­way time is plotted vertically below the midpoint position, between the Tx and the Rx, even if the actual ray path is slanted, as for the reflection from dipping interfaces or from small-­size targets (diffraction). Typically, the vast majority of commercial GPR, are built according to a monostatic architecture. However, GPR data can be acquired using other modes depending on the relative geometry of transmitter(s) and receiver(s). These acquisition modes are known as: The Common Mid-­Point (CMP) or Common Depth Point (CDP), the Wide-­Angle Reflection and Refraction (WARR), and the transillumination (Figure 2.1.3) The first two are mainly used for the electromagnetic (EM) wave velocity determination whereas the last is used in tomographic studies. In general, any GPR is built to measures EM waves reflection events at a given time. This means that, once the EM signal is emitted by the Tx, it travels in the ground and when the wave encounters a reflector it is scattered back and recorded by the receiver. The time spent by the EM wave to travel from the Tx to the reflector and back to the Rx is known as two-­way travel time. Hence, the electromagnetic wave propagation velocity plays an important role in the GPR data analysis, because it allows the conversion of the two-­way travel time window into depth. The EM wave, propagates at a different velocity in different mediums, depending on their physical (dielectric) properties. Beside CMP and WARR methods to estimate EM waves velocity, other methods can be used. They are (i) the location of objects at known depth, and (ii) the reflection from a source point. In the first method, two-­way travel time is the time that an electromagnetic wave takes to travel through the ground, from the transmitting antenna to the object and back to the receiving antenna (Figure 2.1.4). Denoting the depth of the known object with a zknown and the velocity of the electromagnetic wave with v, the two-­way travel time for a monostatic configuration of the antenna is given by:

Figure 2.1.1  Sketch of the basic components of a GPR system and principle of operation.

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t

2 zknown v

(2.1.1)

Since the depth of the object is known, it can be taken the double travel time from a radar section and express

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Electromgnetic (EM) Theory: An Outline  7 (a)

0

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1. 20.

0. 0.001

Simulation parameters Antenna = 600 MHz Scan/m = 2.5 Samples = 512 Range(ns)=80 Gain = 2 Lin.gain = 1 exp.gain = 1 Rays/deg = 1 Aperture(m) = .7

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Figure 2.1.2  Schematic illustration of data acquisition in the reflection profiling mode (a), corresponding radar time section (b) and the waves characterization (c).

the velocity of the electromagnetic wave using Eq. 2.1.1 (Figure 2.1.4 a). The second method is based on the phenomenon that a small object, for example, the cross section of a pipe, reflects radar waves in almost all directions (Figure 2.1.4 b). Denoting the depth of the object with z and the lateral distance of the monostatic antenna from the object with x, the length w of the wave path can be simply expressed by:

W2

4 x2

z2

(2.1.2)

and therefore, the function of the two-­way travel time with:



t x

w v

2 x 2 z2 v

(2.1.3)

Denoting with t0 the two-­way travel time, on the vertical to the object, one has:

0005147532.INDD 7

t0



2z v

(2.1.4)

Therefore:



t x

4x 2 v2

t 20



(2.1.5)

which is the formula for the so-­called “diffraction hyperbola” method. Many commercially available GPR data processing software, allows for the computation of the EM velocity propagation, automatically, based on this method. Since from the radar section for each x position the corresponding two-­way travel time t (x) is known, the velocity can be calculated by inverting Eq.  (2.1.5). The shape of the hyperbola is governed by the velocity of the wave through the ground and by the geometry of the buried object (Fruhwirth et al., 1996) (Figure 2.1.5).

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8  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM

the conduction current density. The EM field relates to these quantities by means of empirical relationships known as constitutive equations (Keller, 1987; Ward and Hohmann, 1987):

(a) Tx

Tx

Tx

Tx

Rx

Rx

Rx

Rx

(b)

Rx

Rx

Rx

Rx

Rx

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Rx

Rx

Rx

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Rx

Figure 2.1.3  Schematic illustration of data acquisition in the a) CMP, b) transillumination, and c) WARR, (Tx: transmitter, Rx: receiver).

As already mentioned above, GPR method is based on the propagation of Electromagnetic (EM) waves in the ground. And the Maxwell’s equations provide the starting point to understand how electromagnetic fields can be used in Georadar exploration to obtain information about the electric and magnetic properties of the soil which is an electrically neutral medium (ρ = 0  where ρ indicates the charge density). This is a set of the four Maxwell’s equations:

E

0 (2.1.6)



H

0 (2.1.7)





B t

H

J

D t

H t E

(2.1.8) E t

(2.1.9)

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0005147532.INDD 8

H J

E (2.1.10)



E

E0 e

z i

e

t

z

(2.1.11)



H

H0 e

z i

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z

(2.1.12)

e

where

2.1.3. Electromagnetic wave propagation

E

E B

where σ, ε, and μ are respectively the electrical conductivity (Siemens/m), the electrical permittivity (Farad/m), and the magnetic permeability (Henry/m). These relations allow the description of the behavior of EM waves in a medium by means of three constitutive parameters, that in general are tensor quantities, but under the assumption of isotropy and homogeneity can be considered scalars: the electric permittivity, ε, the electric conductivity, σ, and the magnetic permeability, μ. A useful approximation, in the case of a homogeneous isotropic medium, is represented by the damped plane wave solution of the scalar wave equation. In this case each component of the electric (E) and magnetic (H) field at a distance z and time t is related to the corresponding fields at z=0 and t=0 (E0 and H0) by the expressions:

Tx

Tx

D

1



1 2

2

1

2

(2.1.13)

1 1



1 2

2

1

2

(2.1.14)

1

α is called absorption constant and β is called the phase constant. The constitutive parameters ε and σ are, in general, complex numbers and have in-­phase (d.c.) components, namely ε’ and σ’, and out of phase (high frequency) components, namely ε” and σ” (Turner and Siggins,  1994) relating with each other by the following:

i (2.1.15)



i

(2.1.16)

At most radar frequencies the out of phase component of the electric conductivity (σ”) is generally negligible, while the out-­of phase component of the electric permittivity (ε”) is not.

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Electromgnetic (EM) Theory: An Outline  9 (a)

0

Depth (m)

0.6

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1.2 1.8 2.4 3.0 3.6 4.2 4.8 5.4 6.0 0

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(b) Two way time window

2.4

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Time (ns)

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8.4

Distance (m) Figure 2.1.4  Electromagnetic-­wave velocity measurements: (a) the known object depth; (b) the two-­way travel time related to reflection event by known object (e.g. a pipe).

Moreover, most geological materials, which are best suited for GPR investigations, are low loss (tanδ 35 Ωm) are instead thought to be representative of sand and gravel. The non‐ unique relationship between lithology and resistivity and the smooth inversion model make direct model segmentation difficult. This fact is illustrated in Figure  7.210 by the result of a simple automatic segmentation into a model consisting of bedrock aquiclude, till aquitard, and sand/gravel aquifer using the nonoverlapping classes of 30 Ωm.

10000

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50000

7.19.3.1. AeroTEM modeling As opposed to direct segmentation based solely on resistivity, as shown in Figure  7.210, cognitive voxel‐ based modeling provides a method for segmenting the electric resistivity model with subtle, nonunique and spatially dependent, or sequence‐dependent relationships, between electric properties and lithology. In addition to this it can be included bounding constraint surfaces that add a process‐based element of erosional unconformities to the model. To interpret the mentioned incised valleys, it has been used control points to define the erosional surface of the resistive valley feature within the conductive bedrock as shown in Figure 7.211 for model cross section M2 along the western incised valley. Here, it is observed resistivity values larger than 30 Ωm, which can be due to sand and gravel valley fill. In contrast,

4

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Resistivity (Ωm)

Figure 7.209  Histogram of layer resistivities for all 1D 29‐layer smooth inverse models of the AeroTEM survey (no correction for variable layer thickness) (from Sapia, et  al., 2015). With permission of Vincenzo Sapia.

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the eastern incised valley is less resistive (more conductive). The differences between the western and eastern incised valleys seems to be confirmed by ground‐based TEM surveys, which suggest a more conductive eastern valley fill (Figure 7.212). The difference in resistivity can be due to a transition in valley fill from sand and gravel to sandy/silty till or till with smaller amounts of sand and gravel as indicated by cored borehole results along seismic line S1 (Crow et al., 2012). Tunnel valleys may be interpreted in a similar way done for the incised valleys, although they are smaller, and the resistivity contrasts are weaker and more variable. Some of the tunnel valleys appear as clear resistive features (>40 Ωm). Conversely, others exhibit a resistivity structure as shown in Figure 7.213 where the resistivity contrast is reduced, but the valley is still visible with the resistivity values of the fill material ranging from approximately 12 to 25 Ωm. To summarize it can be said that buried valleys showing resistivity values on the low end of this range are interpreted as mud‐rich till whereas buried valleys that are observed to have resistivity on the high end of this range are interpreted to be filled with sandy\silty till materials. Finally, the model construction includes the definition of the bedrock surface that defines the regional aquiclude. Here, the bedrock surface is first defined using bottom‐up region grow with a resistivity limit of 15 Ωm. The control points defining the bottom erosional surfaces of all valley features are interpolated across the model region as shown in Figure  7.214 and the resulting bedrock topography is illustrated in Figure 7.215. After the geometry reconstruction of the valley structures and the bedrock topography, the following step is the classification of valley fill and surrounding material. It is observed that the dominant resistivity outside the interpreted buried valleys is approximately 14–23 Ωm, which are consider to be representative of the regional till package that extends to approximately 70 m thick: for modeling purposes, it has been assigned the entire sedimentary sequence above bedrock and outside of the interpreted valleys as a general “till.” Examples of the results for the 3D modeling approach are shown in Figure 7.216. The buried valleys are observed in the seismic data as a low‐ to high‐amplitude discontinuous reflection facies, and the interpreted bedrock surface is derived from the transition to low‐amplitude reflections with limited penetration (Pugin et al., 2014). A gravel surface is interpreted in the seismic data from multiple high‐amplitude reflections, whereas the overlying till package manifests as high‐ amplitude, continuous reflections (Pugin et al., 2011). The bedrock is readily apparent in the AEM resistivity model as a conductive basement overlain by more ­resistive

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Case Studies  267 (a) Elevation (m asl)

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Figure 7.210 Automatic segmentation of lithology for model cross section M1 based on resistivity thresholds. (a) Bedrock: 5 < ρ < 15 Ωm, (b) aquitard: 15 < ρ < 30 Ωm, and (c) aquifer: ρ < 30 Ωm. (from Sapia, et al., 2015). With permission of Vincenzo Sapia.

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Figure 7.211  AeroTEM resistivity along model cross section M2. Solid black line is the surface topography. Red dots mark the control points interpreted from the resistivity model to define the bottom erosional surface of the western inset valley. Black dots mark the control points interpreted to define the erosional surface that forms the upper limit of the buried valley. (from Sapia, et al., 2015). With permission of Vincenzo Sapia.

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268  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM (a)

(b)

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rms = 4.7%

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Figure 7.212  Ground‐based TEM data and inversion results forc80 × 80 m central loop soundings (Oldenborger and Brewer, 2014). Data have been inverted using EM1DTM (Farquharson et al., 1993). (a) Western incised valley at the north end of the survey area. (b) Eastern incised valley along S2. The red line indicates model uncertainty as per the depth of investigation index (departure of the red line from the models indicates higher uncertainty). (from Sapia, et al. 2015).

materials. In general, the AEM result shows good agreement with the seismic information in terms of depth to bedrock, although, due to the smoothing constraints of the spatially constrained inversion, the AeroTEM resistivity model lacks a 500 m wide buried valley as depicted in the seismic 2D profile (Figure 7.216a, left).

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Figure  7.217 shows the final result of the modeling procedure, where the different type of valleys, the bedrock and the general till package are illustrated. The valleys are generally between 30 and 60 m deep and most reach the bedrock in their deeper part. They appear to be U‐shaped, and they are 1–2 km wide. All of the valleys are covered by the general till; thus, they do not reach the surface.

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Case Studies  269

Elevation (m asl)

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Figure 7.213  AeroTEM resistivity along model cross section M3. The solid black line is the surface topography. Red dots mark the control points interpreted from the resistivity model to define the bottom erosional surface of this tunnel valley, and black dots mark the control points interpreted to define the erosional surface that forms the upper limit of the valley. (from Sapia, et al., 2015).

N E

W S

1

5 10 20 40 60 100 Resistivity (Ωm)

2 km

Figure 7.214  A 3D depiction of the bottom erosional surfaces of all buried valleys interpreted over the entire model volume from the AeroTEM resistivity model (red box in Figures 7.206 and 7.207). The surfaces are interpolated from control points using kriging with a search radius of 100 m. (from Sapia, et al., 2015). With permission of Vincenzo Sapia.

7.19.3.2. VTEM modeling The same procedure applied and illustrated for the AeroTEM survey was implemented to the VTEM resistivity model and to improve a better match near the surface with respect to available ancillary information available (i.e. electrical resistivity tomography), the

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VTEM data went through a calibration procedure (Sapia et  al., 2014a). To do this, first the dominant resistivity contrasts are used to interpret valley surfaces that are interpolated across the model domain. Secondly, those surfaces are used to constrain voxel‐based segmentation in conjunction with definition of the bedrock topography.

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270  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM

N W

E

2 km

S

350

390 430 Elevation (m asl)

470

Figure 7.215  Bedrock topography of the 3D geologic model obtained via interpretation of the AeroTEM resistivity model. (from Sapia, et al., 2015). With permission of Vincenzo Sapia.

The resulting voxel‐based lithological property model is shown in Figure 7.218. The VTEM resistivity model provides additional details to the subsoil model, especially aiding at better defining at the near surface level and with respect the till package. In the VTEM model it is visible a conductive layer (10 Ωm) at approximately 10 m depth within the till package (Figure 7.218a). Here, two surfaces are constructed to constrain the top and the bottom of this interpreted mud‐rich inter‐till unit (Figure 7.218b). This unit is located stratigraphically on top of the two incised valleys (Figure 7.218c). It should be observed that if one side the VTEM model provides a better understanding of near surface, on the other it does not clearly resolve the small tunnel valley that occupies the eastern sector as instead was possible with the AeroTEM model. Also, the VTEM model, does not show a resistivity variability between the eastern and the western incised valleys as it does in the AeroTEM model, and the two main incised valleys appear to have nearly the same resistivity values. This may be due to the 2D nature of the VTEM survey data and the resulting inability to represent the regional geologic signature. Moreover, the eastern incised valley exhibits moderate resistivity and based on the previous AeroTEM modeling and the borehole records, this is attributed to be the response of silty till valley fill. Conversely, the western valley is characterized by a slightly higher resistivity, which can be due attribute to gravel and sand. The 3D representation of the complete geologic model is illustrated in Figure 7.219 with the till package

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stripped away to reveal the interpreted buried valley network. 7.19.4. Conclusion Regional‐ and local‐scale AEM surveys have been used for 3D interpretation and modeling of a portion of the Spiritwood Buried Valley Aquifer system, in Canada. In the case study presented, pseudo‐3D resistivity models obtained via inversion of the AEM data reveal significant geologic structures that have been segmented and classified into distinct buried valleys systems with different morphologies and valley fills. A modeling workflow has been adopted based on a so‐ called cognitive voxel modeling approach, incorporating seismic reflection data, borehole geophysical logs, and other supporting knowledge when constructing the model based on AEM data. All the resistive features are interpreted to be buried valley structures filled with coarse‐grained material (sand and gravel) that represent high groundwater resource potential, for which connectivity and volume can be estimated. And this 3D voxel‐based model helps in visualizing the complex 3D geology. On the other hand, the presented cognitive approach allows for construction of a model with sharp contacts and distinct model units from a data set that suffers from smoothness, uncertainty, and a nonunique relationship with lithology. The full details of this case are deeply discussed in Sapia et al., 2015.

08-12-2021 14:31:00

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10 20 30 Resistivity (Ωm)

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466500 E

Shale

Gravel and sand

469000 E

Mud-rich Till

Sandy\Silty Till

Till

Figure 7.216  (a) Seismic reflection profiles S2 (left) and S6 (right). Red lines indicate the interpreted bedrock surface, and green indicates the inter‐ till reflection surface. (b) Corresponding AeroTEM resistivity models. (c) Interpreted models. (from Sapia, et  al., 2015). With permission of Vincenzo Sapia.

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(a) N W

E S

a′

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Shale

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Gravel and sand

Mud-rich Till

Sandy\Silty Till

Till

a

a′

400

300 1000

6000

18000

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Figure 7.217  (a) Three‐dimensional voxel‐based lithological model of a selected portion of the Spiritwood survey area. (b) East– west cross section along M1. The solid black line is the surface topography. (from Sapia, et al., 2015). With permission of Vincenzo Sapia. (a)

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350

(c)

N E

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Figure 7.218  (a) VTEM resistivity model along seismic line S1. (b) A 3D depiction of interpreted surfaces of buried valleys, inter‐till boundaries and bedrock topography. (c) Voxel‐based geologic model. (from Sapia, et al., 2015). With permission of Vincenzo Sapia.

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Case Studies  273

N W

E S 2 km

Shale

Gravel and sand

Mud-rich Till

Sandy\Silty Till

Figure 7.219 The 3D visualization of the voxel‐based geologic model of the buried valleys and the bedrock interpreted at the Spiritwood survey area. (from Sapia, et al., 2015). With permission of Vincenzo Sapia.

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274  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM

7.20. ­AEM: EFFECT OF INDUCED POLARIZATION OVER AEM DATA OBJECT: Large area geological mapping TECHNIQUE: AEM GEOGRAPHIC AREA: Australia

7.20.1. Introduction Traditionally, AEM data has been rendered in terms of electrical resistivity and/or electrical conductivity maps. However, the industry has now widely accepted that the output of AEM processed data can be frequently affected by induced polarization (IP) effects than previously acknowledged. A clear understanding of how much, where, and when IP is present is not completely clear, to date. In the following pages, a case study from South Australia illustrates the results relative to geological and demonstrates the relationships between chargeability and the, often unpredictable, consequences for the resistivity inversions. Here, a novel method for assessing the presence of IP effects in AEM data, which is named the “AIP scanner,” is based on the joint analysis of the entire dataspace together with a selected portion of the model space. This is based on a combination of extensive data space and limited model‐space analysis and the basic assumption is that failing to model IP, when present, increases AEM inversion misfits.

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The resulting “AIP scanner” map indicates areas of definite AIP effects, areas possibly affected, and areas probably unaffected by AIP. Such maps are extremely useful tools for the exploration industry wishing to leverage AEM data information content. 7.20.2. Methods and Results Many recent studies proved the IP effect to affect the output of AEM data (Viezzoli and Manca (2020), Kaminsky and Viezzoli (2017), Oldenburg and Kang (2015), and Macnae (2016). Also, they prove that failing to model IP, usually increases data misfit. In an attempt of summarizing, it can be stated that the IP effects in AEM data can: 1. Produce negatives, 2. Increase the decay rate of transients, and 3. If not modelled, result in large data misfits associated with inverted resistivity models. The “AIP scanner” hereinafter illustrated (the thorough details are in Viezzoli et al., 2020), are based on the

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Case Studies  275

above, and the implementation of its main steps include the following: 1. Resample the dataset to approximately 30 m spacing along lines in order to speed up the procedure, without materially affecting the lateral resolution of the AEM system. 2. Inspect the voltage data, assess the noise level on the absolute values of voltages, and delete readings below noise. 3. Calculate the synthetic responses of the specific AEM system used in the survey to a suite of layered earth scenarios without any chargeability. Fit different portions (e.g. early, mid, late times) of each of these synthetic responses with an exponential decay. Note the upper limit of the range of these decays. They represent the fastest decays and smallest time constants that can be expected without IP effects (IP can increase the rate of decay substantially). 4. Run “IP metrics” over the prepared data from point 2. They include metrics both on negative readings and on rates of decay of the transients (cf Viezzoli and Manca, 2020). 5. Choose portions of the dataset representing different EM behavior and invert with layered earth and without IP modelling (i.e. deleting negatives but keeping fast decays). Inspect the resulting misfit. 6. Run an optimization routine that uses the IP metrics at point 4 to predict the misfits of point 5. 7. Use the output of point 6, (i.e. the coefficients associated with the metrics that best predict the misfit in non‐ IP mode) applied over the entire dataset, obtaining a map: the “AIP scanner.” 8. Compare the “AIP scanner” performance against chargeability recovered from selected lines fully modelled for AIP. The AIP scanner map defines areas with no measurable AIP effects, areas with definite AIP effects, and areas with possible AIP effects. No information about the magnitude of chargeability in the ground, nor about the depth or characteristics of the source is to be obtained. This information can only be derived through the modelling of the IP effects in the AEM data with an inversion (e.g. using a Cole–Cole model). As areas with AIP effects yield erroneous resistivity models unless IP is modelled, this scanner will indicate where a conventional inversion may wrongly represent conductivity structure, producing either false resistors or conductors. 7.20.3. Data Acqusition and Results The area of interest for this case study is located in South Australia and relates to a recent XciteTM helicopter TDEM survey (Combrinck and Wright, 2016). In

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particular, the area sits in the Adelaide Rift Complex, a thousand‐kilometer long belt of sedimentary rocks, with some minor volcanic, deposited between 870 and 500 Ma. The local geology, shown in Figure 7.220, is largely siltstones, sandstones, and dolomite, where carbonaceous siltstones and shales are the most likely candidates for stratigraphic conductors. The Xcite system main characteristics are: ••25 Hz base frequency transmitter. ••trapezoidal current waveform. ••4 turns of a loop of approximately 300 m2. ••The receiver, in central loop, provides streamed voltage data, later typically binned into 45 gates. In Figure 7.221 it is shown an extract of the Xcite data along one line, where AIP effects as negative voltages indicated in red. The raw HTEM data collected at the survey area were processed by applying the workflow described above. In particular: 1 and 2) Dataset resampling and noise level assessment. The noise model applied to eliminate data follows the increasing gate widths of the transients and it has a value of 3×10‐3 pV/Am4 at 1 ms, decreasing with later time gates according to ~ t‐1/2. Figure 7.222 displays, the sum of log10(abs(negatives)). The most intense negatives fall within a unit of grey, calcareous shale, and are not particularly aligned with individual stratigraphic units. Un‐intuitively, while one might assume these represent the strongest IP signals in the dataset, they do not necessarily produce the most material changes between resistivity sections inverted with and without AIP. As Viezzoli and Manca (2020) show, the strength of a negative response depends on several factors other than the magnitude of chargeability, such as host resistivity or presence of resistive bedrock below the chargeable material. Also, it should be noted the lack of negative transients over the conductive shales and recent cover, which both present high amplitude signals that IP effects do not overpower. Table  7.2 contains calculated EM transient time constants over five groups of time intervals (A–E) representative of the survey area. A map of one of these time constant τ (time interval C) is shown in Figure  7.223. With respect to Table 7.2, a time constant less than 0.35 (light blue on Figure  7.223) is explained because the presence of IP effects in the corresponding area. It should be mentioned that, while there is a correspondence with the map of sum of negatives (Figure 7.222), there are also areas of small τ not associated with negative decays. 3 and 4) Calculating syntethic response and run IP metrics Because the AIP scanner metrics has to match with fully modelled AIP effects, it guides the choice of representative survey lines, meant to represent areas affected

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276  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM

Figure 7.220  EM survey lines over government geological map. For comparison with AEM images below, dark brown carbonaceous siltstones are the most conductive stratigraphic units, along with light yellow recent cover in the north and west. Medium brown units are dolomite, orange is siltstone and grey/green shales, and pale pink is younger dolomitic siltstone and dolomite. (Viezzoli et al., 2020) With permission of Andrea Viezzoli.

Figure 7.221 Example of transients affected by IP (red bars represent negative voltages), with late time noise culled and left in light grey. Note how some transients start negative (right plot). (Viezzoli et al., 2020)

by AIP, and to be processed and inverted without AIP modelling and this involves eliminating noisy gates and negatives. The output misfits of inversion without AIP with respect to individual metrics previously calculated, is evaluated through scatter plots (Figure  7.224). Linear

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regressions are calculated on a subset of all misfits (the log10 of the misfits) and corresponding time constants. This process leads to a selection of time constant ­intervals that best correlate with the misfits, to be used later in the AIP scanner optimization routine. Figure 7.224 shows an example of one of these scatter plots with the corresponding linear regression. High misfit and low τ are linked with areas of potential IP. 5 and 6) Inversion of portion of the dataset without modeling for IP and running optimization with IP metrics After the mentioned regression analyses, it is optimized the best combination of coefficients with individual metrics for predicting the misfit obtained without IP. For this case study, the best prediction seems to be given by the combination of sum of negatives and decay rates from interval B and C. In Figures  7.225 to 7.228, it is shown the sensitivity maps of the AIP scanner to individual metrics and they illustrate how the metrics complement each other in the prediction over the whole area. 7) Obtaining the AIP scanner map Figure 7.228 shows the final “AIP scanner” map. This map overlain by high misfit > 1 from inversion without

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290000.000

10

300000.000

310000.000

30

Figure 7.222  Distribution of sum of log10(abs(Σ(negatives)), in pV/Am4 (above noise, see text for details). Blank areas contain no negative values in the transients. These areas are largely over the very conductive shales (brown) and recent cover (pale yellow). (Viezzoli et al., 2020) With permission of Andrea Viezzoli.

Table 7.2  Gate intervals (starting from first gate useable for modelling) and corresponding decay rate categories. (Viezzoli et al., 2020). With permission of Andrea Viezzoli. Time interval Time gates

Minimum expected time constant τ (unitless)

A

B

C

D

E

1‐7 0.68

8‐15 0.41

16‐21 0.35

22‐28 0.31

29‐35 0.22

IP, and it is plotted along the lines involved in the prediction. There is good spatial correlation between the IP scanner and the high misfits. According to the AIP scanner approximately 50% of the dataset is somehow affected by AIP and by comparing with Figure  7.220, the geologic map, it shows a sort of association with specific geological units and domains. While the effect of IP on the resulting conductivity inversions may not always be large or even material

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to interpretation, Figure 7.228 underlines the widespread occurrence of AIP effects that one may expect in any AEM dataset in the region. 8) Comparing the AIP scanner with chargeability sections over selected lines Lastly is to carry out AIP modelling over the same acquisition lines used earlier to invert without AIP. Then data are inverted using the dispersive Cole‐Cole model, like in Pelton et  al.’s notation (1978). The chargeability sections processed and inverted in this way are compared against the AIP scanner map values (Figure 7.229). The spatial correlation between the inverted chargeability (the faded back ground in Figure 7.229) and the scanner (the foreground in Figure 7.229), is illustrated in Figure 7.229. It confirms the efficiency of the AIP scanner in predicting the presence and location of measurable AIP effects, and therefore also where chargeability can be recovered (through layered earth inversion) for this specific AEM dataset. However, the AIP scanner ­indicates the degree of confidence with respect to the presence or

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6630000.000

6640000.000

6650000.000

6660000.000

278  ELECTROMAGNETIC METHODS IN GEOPHYSICS: APPLICATIONS IN GEORADAR, FDEM, TDEM, AND AEM

250000.000

260000.000

270000.000

280000.000

290000.000

300000.000

310000.000

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95

1.0

Figure 7.223  Distribution of time constant τ over interval C (cf Table  7.2). Blank areas occur where negative decays prevent the calculation of τ. (Viezzoli et al., 2020) With permission of Andrea Viezzoli.

0.7

not of IP effects in the AEM data, but does not reflect the magnitude or depth of its effect. The AIP scanning procedure is relatively fast and cheap compared to full AIP modelling over the entire area and it is a useful precursor to guide the choice of more involved modelling.

R-squared: 0.50478

0.6

Tau C

0.5 0.4

7.20.4. Discussion and Conclusion

0.3 0.2 0.1 –1

–0.5

0 0.5 Log10(Misfit)

1

1.5

Figure 7.224  Scatter plot and corresponding linear regression showing correlation between time constant for interval C (refer to Table 2 for detail) and misfit obtained without AIP modelling. (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

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The results obtained as an output of the AIP scanner procedure, with and without AIP modelling, are illustrated and discussed for two acquisition lines and illustrated in Figure 7.230 and 7.231. Both figures show, on top (red line) the positioning of the reference acquisition lines. Then in the lower part of each of the image, there are the inverted section AIP‐modeled and AIP‐Modeled. With respect to Figure 7.230 two main considerations can be drawn from the comparison and interpretation of the resistivity section with (upper resistivity section) and without (lower resistivity section) AIP modelling:

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6670000.000 6660000.000 6650000.000 6640000.000 6630000.000

250000.000

260000.000

0.01

270000.000

280000.000

0.05

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300000.000

310000.000

0.2

1.0

6630000.000

6640000.000

6650000.000

6660000.000

6670000.000

Figure 7.225 Sensitivity of IP scanner to the metric of decay rate interval B. (Viezzoli et  al., 2020). With permission of Andrea Viezzoli.

250000.000

0.01

260000.000

270000.000

0.05

280000.000

290000.000

0.2

300000.000

310000.000

1.0

Figure 7.226  Sensitivity of IP scanner to the metric of decay rate interval C, illustrating some different zones of high sensitivity than for interval B in Figure 7.225. (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

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6670000.000 6660000.000 6650000.000 6640000.000 6630000.000

250000.000

260000.000

0.01

270000.000

280000.000

0.05

290000.000

300000.000

310000.000

0.2

1.0

6630000.000

6640000.000

6650000.000

6660000.000

6670000.000

Figure 7.227  Sensitivity of IP scanner to the metric of the sum of negatives. This sensitivity map is spatially uncorrelated to the decay rate maps of Figures 7.225 and 7.226 (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

250000.000

0.01

260000.000

270000.000

0.05

280000.000

290000.000

0.2

300000.000

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1

Figure 7.228  “AIP scanner” map, with dark blue = no AIP, yellow‐orange‐red = definite AIP, and light blue to green = possible AIP. The misfit from lines inverted without AIP modelling is superimposed as black dots for any misfit > 1, matching well with the scanner results. (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

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1. A large conductor at ‐350 below ground level is only visible in the inversion with AIP and this can be assigned to the shales of the Tapley Hill Formation (Pft). This conductor is not visible when inverting without AIP.

2. The inversion without AIP shows a thin, well‐defined conductor, directly overlying very resistive crystalline basement. When accounting for AIP, the near‐surface conductor is more diffuse and no longer overlies anything

500 450 400 Elevation [m]

350 300 0

250 200 150 50 0

AIP scanner grid

mV/V

100 10

0

50

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1400

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1900

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Distance [m]

Figure 7.229  Inverted chargeability section compared against profile of AIP scanner values for one of the comparison lines. The depth of investigation is indicated via the faded portions of the section. (Viezzoli et  al., 2020). With ­permission of Andrea Viezzoli.

5.0

500 450

4.5

400

4.0

350

3.5

250

3.0

200 150

2.5

100

2.0

50 0

1.5

–50

Ωm

–100 –150 –200 500

1.0 0.5

1.0e0

2.0e1

5.0e2

1.0e4

450

5.0 4.5

400

4.0

350 300

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250

3.0

200 150

2.5

100 50

2.0

0

No Unit

Elevation [m]

No Unit

Elevation [m]

300

1.5

–50 –100

1.0

–150

0.5

–200

0.0

–250

25700 25800 25900 26000 26100 26200 26300 26400 26500 26600 26700 26800 26900 27000 27100 27200 27300 27400 27500 27600 Distance [m]

Figure 7.230  Resistivity models obtained with AIP modelling (top) and without AIP modelling (bottom) for the line identified in red on the geology map. Data misfit shown in black, to be read against the right axis. Depth of investigation is shown by the faded portions of the sections. Conductors and their geometries are sufficiently different to materially affect interpretation. Figure 7.232 contains the accompanying chargeability section. (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

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5.0

300 250 200 150 100 50 0 –50 –100 –150 –200 –250 –300 –350 –400 300 250 200 150 100 50 0 –50 –100 –150 –200 –250 –300 –350 –400

4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 5.0 4.5 4.0 3.5 3.0 2.5 2.0

No Unit

Elevation [m]

for. In fact, an apparently thin and strong near‐surface conductor, after accounting for IP effects, is more diffuse and has greater depth extent. The most significant different in the two sections with and without AIP modelling appears to be the conductivity contrast that may indicate the location of a mapped

No Unit

Elevation [m]

more resistive than the general background of the section. Figure 7.231 shows the results obtained along another acquisition line a where less strong impact on the resistivity structure appears to be visible. However, a notable result in terms of chargeability map (Figure 7.232) is to be account

1.5 1.0

Ωm 1.0e0

11400

11600

11800

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12200

2.0e1

12400

0.5 5.0e2

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Distance [m]

Figure 7.231  Resistivity models obtained with AIP modelling (top) and without AIP modelling (bottom) for the line identified on the geology map. The faulted unconformity is extended from the surface geology map, and there is another mapped fault intersecting this line at a high angle. Data misfit shown in black, to be read against the right axis. Depth of investigation is shown by the faded portions of the sections. (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

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5.0

300 250 200 150 100 50 0 –50 –100 –150 –200 –250 –300 –350 –400

4.5 4.0 3.5 3.0 2.5 2.0

mV/V 10

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FAULT

1.0 0.5 0.0

11400

11600

11800

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Distance [m]

Figure 7.232  Chargeability section accompanying Figure 7.231. The fault is extended from the surface geology map in Figure 8.231. Data misfit shown in black, to be read against the right axis. Depth of investigation indicated via the faded portions of the section. (Viezzoli et al., 2020). With permission of Andrea Viezzoli.

fault as it continues under recent cover (drawn on the upper resistivity section of Figure 7.231). This contrast is better defined over a larger depth interval when AIP is modelled. Also, Figure 7.232 that depict the chargeability section, shows a clear and confined anomaly in the hanging wall of the interpreted fault. Both the above examples illustrated and relating to two different acquisition lines, shows the amount of ­additional information given by the implementation of the AIP modelling with respect to the capability of interpretation of the processed and inverted AEM data, with respect to the geology of the subsoil. In conclusion, it can be concluded that the inclusion of IP effects when modelling AEM data can cause differences in resistivity distribution that are material to resource exploration, as well as provide a useful secondary dataset in terms of chargeability. Therefore, it allows the end‐users of AEM data to be aware of the degree and distribution of IP effects in the data. This approach is valuable for both historic and current datasets, and it has been developed and tested in a novel tool called the “AIP scanner.” This allows an end user to make informed decisions about how to treat the affected data, as well as where to trust conventionally inverted data without accounting for IP. It represents a valuable tool, for both explorers and government, that adds extra information to the AEM derived products and deliverables. Research is currently underway to explore the possibility of the AIP scanner providing some useful input to inversion parametrization to reduce modelling efforts. Full details of this work can be found in Viezzoli et al., 2020.

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Radar, Expanded Abstracts, Proceedings, Ontario, Canada, 723–733. Teza, G., Viezzoli, A., and Menghini, A. (2015). Shallow Geothermal Exploration by Means of SkyTEM Electrical Resistivity Data: An Application in Sicily (Italy), Eng. Geol. Soc. Territory, 1, 363–367. Topp, G.C., Davis, J.L., and Annan, A.P. (1980). Electromagnetic Determination of Soil Water Content: Measurements in Coaxial Transmission Lines. Water Resources Research, Vol. 16, N. 3, 574–582. Thomson, S., Fountain, D., and Watts, T. (2007). Airborne geophysics: Evolution and revolution. Fifth Decennial International Conference on Mineral Exploration, Exploration 07, Extended Abstracts, 19–37. Tronicke, J., Tweeton, D.R., Dietrich, P., and Appel, E. (2001). Improved cross hole radar tomography by using direct and reflected arrival times. J. Appl. Geophys. 47, 97–105. Ulriksen, P. (1982). Application of impulse radar to civil engineering,” PhD Lund University of Technology, Lund, Coden, Lutvdg (TVTG‐1001), Sweden. Utsi, V. and Utsi, E. (2004). Measurements of reinforcement bar depths and diameters in concrete. Tenth International Conference on GPR, 21–24 June 2004, 659–662. Venteris, E.R. (2007). Three‐dimensional modeling of glacial sediments using public water‐well data records: An integration of interpretive and geostatistical approaches. Geosphere, 3, 456–468, doi: 10.1130/GES00090.1. Viezzoli, A., Christiansen, V., Auken, E., and Sørensen, K. (2008). Quasi‐3D modeling of airborne TEM data by spatially constrained inversion. Geophysics, 73, no. 3, F105– F113, doi: 10.1190/1.2895521. Viezzoli, A., Jørgensen, F., and Sørensen, K. (2013). Flawed processing of airborne EM data affecting hydrogeological interpretation. Ground Water, 51, 191–202, doi: 10.1111/j.1745‐6584.2012.00958.x. Viezzoli, A., Kaminski, V., and Fiandaca, G. (2017). Modelling induced polarization effects in helicopter TEM data: Synthetic case studies. Geophysics (82:2) E31–E50. Viezzoli A. and Manca, G. (2020). On airborne IP effects in standard AEM systems: tightening model space with data space. Exploration Geophysics, 51:1, 155–169. Wiecek, S. (2009). Municipality of Killarney, Turtle Mountain groundwater assessment study. W.L. Gibbons & Associates, Inc. Winter, T.C., Benson, R.D., Engberg, R.A., et  al. (1984). Synopsis of ground water and surface‐water resources of North Dakota: United States Geological Survey, Open File Report 84–732. Xian‐Qi, He, Zi‐Qiang, Zhu, Qun‐Yi, Liu, and Guang‐Yin, Lu. (2009). Review of GPR Rebar Detection PIERS Proceedings, Beijing, China, 23–27 March, 804–813

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8 General on Planning and Logistic

The aspects discussed so far attain to the theoretical, practical, and more generally, the technical aspects relating to the Georadar, FDEM, TDEM, and AEM geophysical techniques. However, any actual field activity cannot avoid taking into account several non-­technical factors to deal with prior, during, and after the activities on site. These must be evaluated on a project-­basis and, depending on conditions, may need the support of specialists to be executed. Geophysical survey, includes the following phases: 1. Planning the survey 2. Mobilization (both human and material assets) 3. Shipping the equipment 4. Clearing the equipment 5. Stocking the equipment 6. Organizing transport to/from work site of equipment and crew 7. Conducting the field activity mobilization (both human and material 8. De-­ assets) 9. Reporting (deliverables). Any of the above phases, imply specific accomplishment to be finalized according to requirements that may depend upon technical, regional, social, economic, and legal factors to be aware of and to be dealt with the due diligence. Some of these may not be applicable, depending on the place where the survey is carried out with respect to the origin of both human and material asset (i.e. the place of origin of the company performing the survey). For example, phases 3, 4, 5 do not apply when the survey is carried out in the nearby of the headquarter of the company performing the survey.

8.1. ­PLANNING A CAMPAIGN AND MOBILIZATION ASPECTS Before the planning phase and the actual mobilization to site for carrying out a measurements campaign, one should start considering two main aspects: 1. Where is the survey to be carried out (locally or abroad)? 2. Is the survey performed for economic (a payment is due by the customer(s) requiring the survey) or non-­ economic purpose (i.e. no compensation is expected for it)? When a survey is to be carried out in the vicinity, there is no need for shipping goods (i.e. instrumentation), and personnel that performs the measurements can be back at the headquarter by the end of the working day. This implies that the acquisition crew reaches the working site with the needed equipment, deploys the acquisition device(s), performs the measurements, and then leave the site with the same vehicle(s) used on the way forward. The operation is repeated as many times until the whole survey area is covered, based on project specifications. If, instead, the survey must be carried out far away (in a foreign country, overseas, for example), the need for shipping and temporary storing of equipment and planning for the transit and accommodation of personnel, must be considered. Based on weight and dimension of goods to be shipped and the distance where the site is located, the shipping procedure may be via aircraft, cargo ship, vehicles on land and its associated cost, transit time, delivery and availability on site may vary. In fact, when a survey is to be carried out abroad, it must be evaluated the following: ••Custom Regulation for Human and material asset in the Region

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••Lodging of personnel ••Possible restrictions in exporting survey equipment ••Shipping cost ••Appropriate packaging of good during transport ••Insurances for goods ••Taxes for exportation and re-­importation ••Time of transit ••Time of clearance ••Stocking and handling the equipment on site ••Local support and crew Beside the geographical aspects (i.e. where the survey is to be carried out) that plays and important role in the planning phase, also the economical factor has a strong impact in the definition and accomplishment of the operative aspects. On this respect, any geophysical survey can be performed within a non-­economic or an economic context. “Non-­economic” survey can be considered, for example: ••Academic study projects ••Research and development (carried out by either public or private organizations) ••University/school students’ practical classes on site The vast majority of the GPR, FDEM, TDEM or AEM surveys are carried out for “economic” purposes and, generally, there exist a client (private or public) requiring the acquisition of a set of data to be eventually delivered according to technical specifications given by the client itself and agreed beforehand with the service company collecting the data. In this case one single, or a group of public or private end user(s) requires the geophysical service provider to deliver a given output within the framework of a contractual agreement. In this regard, the “single-­client” contractual agreement is the most common one. However, a more pro-­active technical alternative can be the one that implements an approach typical of the Oil and Gas industry, that is seldom applied in the geophysical engineering context: the so-­called “multi-­client” approach. The difference among the two approaches can be defined as it follows: ••Single-­Client: the (two) subjects involved are, on one side the Geophysical Service Provider (i.e. The Company collecting the data and delivering the result) and on the other, the Customer (or group of Customers) requiring the service. In this case, the Customer(s) is the only owner of the data and the output delivered by the service provider company. ••Multi-­Client: In this case, the Geophysical Service Provider may decide to collect data, in a given area, without any specific request from any specific Customer or End User. This may apply, for example, for utility mapping, for cavity mapping, for geotechnical reconnaissance, or for geological mapping, over large urban or industrial, or natural areas. In these circumstances, the owner of the data is the Service Provider that collected the data and it can decide to “deliver” and re-­sell the data and the related output to multiple Customers who may have interest, at different technical level, in having and using these information. Potential Stakeholders in a Multi-­ client approach may be local administrations, water companies,

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telecommunication companies, maintenance companies, mineral exploration companies that need to know the exact location and the quantity of buried natural (cavities, ore body, for example) or man-­made features (archaeological remains, pipes, cables, etc. . .), for avoidance, or mapping, or exploitations purposes. This need may arise prior excavations and maintenance and when/if “as-­built drawings” are either not available or outdated, or for exploitation purposes when applied to natural resources. Due to the large variety of possible scenarios (e.g. mineral exploration, utility mapping, geology and geotechnical applications, water resources research, archaeology, structures maintenance and so on. . .) in terms of data acquisition set up, the above geographic and economic considerations should be evaluated in detail beforehand, as they are of a paramount importance when assessing the planning and mobilization aspects of a survey. 8.2. ­SHIPMENT AND CLEARANCE OF SURVEY EQUIPMENT As mentioned, the location of the acquisition site and the “expected” revenue from the survey, define the qualitative and quantitative details of the mobilization regarding the selection of both human and material assets to be deployed for the service. In particular, goods to be delivered for the service must be prepared for their shipment and this includes the packaging, but also the needed documents accompanying the shipment. In fact, prior any shipment of material to foreigner Countries with respect to the Country of origin, the forwarder, should recall and consider the following: 1. Rules of Customs at Country of destination. 2. Rules for shipment of technical equipment (if applicable). 3. Customs Fees and taxes due for temporary exportation and re-­importation at the end of the activity. 4. Shipping cost (and taxes, if applicable) due to the Company that is taking charge of the activity of transporting to and from the Country of destination of the material. 5. Transit Time. Rules of Customs. The first thing to know prior sending any material abroad is the Custom regulation at destination. This is because products that are shipped internationally, are required to clear customs and to do this it is required to prepare and deliver along with goods, the appropriate paperwork. This must contain the description of what the shipment includes, the destination of use, and the possibility to be identified inside the packaging (for example through an identification numbering and labelling). In this regard it is also important that the packaging of the material is appropriate in relation to both the type of equipment and the possible, specific requirements of the Country of destination. The packaging should allow the products inside not to be damaged during transport and handling, but they should also be packed and labeled so that they may be clearly identified during transit and clearance.

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One further aspect to account for carefully is the possibility that the shipped equipment, or part of it, to be used for geophysical survey may or may not be included in the list of what are identified as “dual use” goods. With the term “dual use” it is meant an equipment, or part of it, that may be destined to either a peaceful or military use. The sensitivity of this subject calls for specialist mastering the matter, and here the sole purpose is to give the Reader an overview on the question. In general, there are items that may be restricted in some Countries and this should be verified beforehand by accessing the list of restricted items by Country. Customs fees and taxes. Depending on type and value of goods being shipped, Country of origin and Country of destination, type and duration of (temporary) exportation, a fee is charged by the Custom department at the Country of destination. Depending on the value of the product and the country where it is being shipped to, this fee may vary widely. Therefore, it is necessary to check the Custom fee rates of different countries before shipping the material. Besides the local fees due to the Custom department, taxes may be due, and this depends on the type of exportation. Under certain condition though, these may not be due, like in the case of the use of the so-­called ATA carnet. The ATA Carnet (acronym ATA is a combination of French and  English terms “Admission Temporaire/Temporary Admission) is a Customs document that permits, internationally, the tax-­ free and duty-­ free temporary export and ­re-­import up to one year, of certain items. This document is a globally accepted guarantee for Customs duties and taxes. It can replace security deposit required by Customs authorities. Shipping cost and transit time. The cost for the shipping includes the fee that will be charged by the company that take charge of the forwarding of the goods to the Country of destination. Also, any other additional freight taxes may be applicable, hence being part of the shipping cost. In the cost due for the shipping, it needs to be considered the medium (air, land, sea) selected for the shipment. Of course, air freight is the fastest method but also the most expensive one. Sea freight on the other hand is quite affordable but take a longer time for delivery. Land shipping may not always be applicable, as well as sea freight. It must be noted that, depending on what it is sent and how this is done, the custom clearance can take a longer time, accordingly. The packing list, i.e. the weight and volume of the goods to be shipped, also determine the cost of the shipment. A further note that may have an implication in the shipping cost, is the possibility to apply a temporary insurance to the goods part of the shipment. In fact, there is a risk of damage in international shipments, during handling and transport. This may be financially not convenient as it is considered an extra-­charge over the survey cost but, on the other hand, it keeps the owner of the equipment being shipped, safe from the risk of heavy damages on expensive equipment. The above elements eventually determine the time for the survey equipment to be delivered and available, after

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Custom clearance, for the use on site. This is commonly called Estimated Time of Arrival, and its acronym is ETA. 8.3. ­MANAGING THE OPERATIVE ASPECTS OF THE FIELD ACTIVITY As all the technical material to collect the GPR, FDEM, TDEM, or AEM data is cleared and made available for the actual field activity, the survey crew should deal with the following: ••Storage of the survey equipment. ••Transport of crew and equipment to site, and back. ••Data acquisition. Once on site, the phases listed above represent the daily routine of a field crew conducting a geophysical survey. Storage Area. The equipment deployed on site during the daytime (working hours) should be made available throughout the whole duration of the campaign and stored when not in use (for example during night-­time). For this reason, it is important making sure that the equipment storage area is safe with respect possible theft, acts of vandalism, and any possible source of damage; that the equipment, when stored, it is not likely to cause any damage to people or other’s properties; that the material is available at any time and not far away from the area where it is supposed to be used; that it is not far away from the lodging area where the field crew is accommodated. Also, both at the equipment storage area as well as on field, the crew should be able to test the equipment and be ready for carrying out possible repair, replacement, or maintenance in case of need. For this reason, a toolkit and a reasonable amount of spare parts should be shipped along with equipment or, alternatively, to be sourced locally, upon need. GPR and FDEM survey do not need, in general, very wide area for assembly and functionality testing purposes. For TDEM and, above all AEM equipment instead, it is needed large areas for assembly purposes and for the system testing to verify the correct functioning of the whole parts prior the actual data acquisition. For the AEM hardware, depending on systems specification, the transmitter and receiver loop together with the control unit to be host inside the aircraft and the sensors, may take one to two days to be assembled: of course, to do this, the space must be adequate to the dimension of the transmitter loop that can cover up to a total area of 1000 m2 or more. Transport. The transport of crew and equipment from lodging place and storage area respectively, to site and vice versa, must be done by using vehicles adequate to the situation, in good maintenance conditions, and drivers should be up to the expectations in terms of local traffic regulations. When the survey is carried out in a Region far away from the Country of origin, with habits and rules that may impact over the driver’s capability to cope with the expected promptness while conducting a vehicle carrying goods and human being, it is always recommended evaluating the support of a local team. Regardless, any operation implying the use of a vehicle should be done respecting local safety regulation, besides common sense.

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Data Acquisition. The data acquisition phase is the core of any geophysical measurements campaign and, during its performance the following aspects must always be fulfilled: 1. To carry out field activity data acquisition permission should be verified. For example, collecting data along public roads, monumental or archaeological areas, restricted areas such as airports or railways, may require specific permissions. 2. Safety conditions. Data acquisition must always be conducted safely, and crew should always abide by local rules and wear safety equipment during operation. Also, all instrumentation should never be used if not properly verified from the correct functioning point of view, above all if this may jeopardize the health and safety of operators. 3. Scope of work and data quality. The survey area must fully be covered according to scope of work as per agreement with the end user. The quality of the data should also be checked and calibrated according to expectations. Prior commencing any field activity, the crew should make sure that permission to collect data are in place and available any time throughout the whole duration of the measurement’s sessions. And this is especially true, but not necessarily limited to, for flight permissions for AEM, or when data are collected in sensitive areas like airports, roads, tunnels, railways, etc. . . Safety is the most important aspect to consider when gathering data. For this reason, the following aspect needs always to be recalled: ••Availability and knowledge of local safety rules. ••Safety induction course for personnel (if applicable). ••Use of personal protection device (gloves, safety shoes, helmets, reflective jackets). ••Use of instrumentation always properly working. Finally, the full coverage of the survey area is the “target” to have in mind for the correct finalization of the survey. In this regard, beside fully covering the expected scope, a field data quality check is always recommended prior using the data for analysis, interpretation, data export, and reporting for final delivery. In those cases when the data quality, for any reason, is not up to the expectations, one should consider the part of the survey area where acquisition should be performed once again or reviewed until the quality is up to the level set up in advance. GPR, TDEM, FDEM, and AEM are indirect techniques, and for this reason, a good “tool” for calibration (i.e. quality) purposes, is the comparison of the field output with ancillary information deriving from other type of measurement (geotechnical, geological, historical, existing drawings, etc. . .) or from visual inspections. Raw data collected on field are then processed and interpreted in a shape that is rendered as images and finally included in the report that is delivered along with raw and processed data. 8.4. ­DE-­MOBILIZATION At the end of the field activity, material and personnel must prepare for the final part that is the de-­mobilization. This includes the inverse operations that have been done

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on the way forward when shipping the material from the Country of origin. 8.5. ­REPORTING The conclusion of a geophysical measurement campaign is summarized in what is generally named “deliverables”. Deliverables are normally agreed a priori, and they may include: ••Raw data ••Processed data ••Interpretated maps and data ••Draft report ••Final Report. Raw data relates to the field measurements with no filtering or other advanced processing and analysis. Processed data, are the data after specific quality data analysis and processing routine are applied, and Interpreted data relates to the rendering of the data after processing and interpretation of the features (natural or man-­made) found in the dataset and relating to the purpose of the survey. Interpreted data may be rendered as output of a different aspect and format, including CAD drawings or data files that may be opened into other software for visualization purposes and integration with other sort of data. Data to be delivered, are normally associated to the final report as an appendix. Depending on the level of information required, reports to be delivered may be of two types: ••Descriptive report ••Interpretative Report. A descriptive report includes Raw and Processed data and no interpretation of the dataset is required. An Interpretative report also include Interpreted data. The effort to be put into this latter deliverable is higher than the previous one as it implies that the processed data are interpreted based on the operator experience and calibrated against the field evidence possibly coming from ancillary information for validation purposes. Regardless the type of report to be delivered, this is the final part of the activity describing what has been done and what is delivered to the end user, hence it must be clear, written using a formal language, describing thoroughly all the part of the activity. The minimum part any final report should include are: ••An Introduction, where the scope and purpose of the survey is illustrated. ••The description of the survey area. ••The description of the technique(s) being deployed. ••The Instrumentation(s) used and its technical specification for the survey being performed. ••The acquisition pattern (area coverage). ••Example of raw data for quality assurance purposes. ••Example of processed data with typical output respect to findings. ••Results and Interpretation. ••Final summary. The level of each of the above should be evaluated project by project and filled in accordingly.

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INDEX Absorption constant, 8 Altimeter, 108, 109, 192 Amplitude reflection coefficient, 11 Antenna diagram, 49 Antenna, effective area, 49 Antennas, 6 Apparent resistivity, 257, 261 Archaeology, 123, 124, 143, 148, 157, 292 ATA carnet, 293 Background removal, 68, 78, 125, 131, 144, 149, 158, 164, 171, 181, 192, 199, 205, 221, 227 Bandwidth, 33, 40, 41, 50 B scan, 171, 172, 213, 214, 216, 227 Cavity, 172, 204, 206, 207, 292 CDP, Common Depth Point, 6, 223 Center frequency, 33, 40, 43, 53, 143, 175, 183, 205, 225 Chargeability, 117, 118, 119, 274, 275, 277, 277, 282 CMP, Common Mid Point, 6, 8, 67, 68 Coil, receiving, 97, 99, 100, 101, 110, 259 Coil, transmitting, 99, 110, 111 Cole‐Cole, 117, 275 Component, in‐phase, 231, 234, 238, 240, 241, 252, 252, 254 Component, quadrature, 240, 241, 241 Conductivity, electric, 8, 9, 10, 11, 15, 16, 17, 18, 19, 22, 23, 26, 27, 28, 29, 52, 93, 103, 115, 117, 118, 252, 254 Constant, dielectric, 6, 9, 11, 12, 15, 16, 27, 28, 50, 53, 76, 77, 79, 81, 82, 175, 175, 186, 219, 223 Corrosion, 174 Coupling, 111, 114, 116, 128 Cover, asphalt, 227 Cover, concrete, 169, 170, 174, 227, 229 Cover, manhole, 202, 206, 207 Current, Eddy, 16, 18, 24, 26, 83, 97, 107, 109 Custom clearance, 293

Data processing, 125, 144, 171, 185, 192, 15, 205, 226, 255, 257, 262 De‐mobilization, 291, 294 Depth slice, 134, 140, 141, 159, 160, 222 Directivity, 49 DOI, Depth of Investigation, 116, 117, 257, 268 Electrode, 212, 241 Envelope, 74, 75, 127, 129, 185 EPSG, 103 Equation, Maxwell’s, 8, 15, 27, 28, 39, 43, 44 ERT, Electrical Resistivity Tomography, 125, 131, 210, 212, 217, 218, 240 ETA, Estimated Time of Arrival, 293 Fiber optics, 191, 198, 201, 203 Filter, band‐pass, 68, 72, 144, 171, 192, 199, 205, 221, 227 Filter, F‐K, 70 Fixed‐wing, 107, 110, 112 Footprint, 12 Forensic, 123, 251 Frequency bandwidth, 33 Frequency spectrum, 39, 40 Frequency, stepped, 34, 36, 38, 44, 45, 46, 47, 48, 82 Fresnel zone, 12 Gain, 49, 50, 68, 71, 72, 78, 99, 125, 126, 131, 134, 144, 158, 164, 171, 176, 192, 199, 205, 221, 227 Gate, 99, 102, 110, 112, 115, 211, 245, 251, 252, 256, 275, 276 GCM, Ground Conductivity meter, 18, 19, 83, 84, 85, 86, 93, 97, 251 Geology, applications, 255, 263 Geophone, 210 Geo‐referencing, 84, 100, 137 GNNS, 26, 51, 52, 55, 56, 57, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 99, 100, 101, 108, 109, 113, 143, 192, 198, 205, 252

GPR, investigation depth, 52 GPR, purpose of the survey, 52 Heritage, monumental, 123, 148, 163 HFEM, 107 Hierapolis, Turkey, 124 HTEM, 107, 110, 112, 119, 262, 263, 275 Hydrogeological mapping, 123, 262 Hyperbola fitting, 125, 131, 158 Input, impedance, 49 Inclinometer, 108, 109 IP, Induced Polarization, 112, 117, 118, 274 Isotropic radiator, 49 Japan, bridge deck, 225 Joint, 172, 210, 226, 227, 228 Joint inversion, 260 Kirkoff migration, 125, 131, 158, 164 Law, Ampere, 15, 16, 27 Law, Faraday, 15, 16, 22, 25, 26, 27 Law, Gauss, 15 Law, Ohm, 16, 27 LCI, Laterally Constrained Inversion, 28, 115, 116, 118 Leakage detection, 123, 210, 219 Lecce, Basilica of Santa Croce, 187 Lecce cathedral, Italy, 148 Loop, receiver, 98, 108, 294 Loop, transmitter, 97, 98, 102, 107, 108, 114, 256, 294 Map, conductivity, 231, 238, 239, 243, 244, 245, 247, 247, 249, 250, 252, 254 Mapping, cavity, 204, 292 Massive array, 51, 58, 65, 143, 143, 191, 197, 204 MASW, Multi‐Channel Analysis of Surface Waves, vii Maxwell’s equations, 8 Micro‐gravimetry, vii

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296 Index Migration, 65, 68, 78, 125, 127, 131, 149, 158, 164, 171, 181, 185, 192, 199, 205, 213, 213, 221, 227 Mobilization, 291, 292, 294 Moisture map, 227, 229, 229 MT, Magneto Telluric, vii Multi‐client, 292 NDT, Non destructive techniques, 204 NMEA, 84, 100 Nyquist, 36, 41, 43, 44, 45, 53, 82, 107, 119 Performance figure, 50 Permeability, Magnetic, 8, 27, 29, 119 Permittivity, Electric, 8, 28, 179, 223 Phase constant, 8 Pipes leakage, 210, 219 Point Cloud, 207, 208, 209 Pollutants search, 123, 245 Post‐processing, 144, 171, 172, 195, 200, 205 Pyrgi, archaeological site, Italy, 143 Radargram, 158, 159, 168, 171, 179, 186 Radiation efficiency, 49 Radiation intensity, 49 Radiation lobes, 49 Radom, transform, 67 Ramp, 116 Rate, repetition, 109, 110, 257

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Rebars detection, 133, 169, 174, 175 Reflection, seismic, 263, 265, 270, 271 Refraction, seismic, 124, 125 Refresh rate, 84, 100 Reporting, 291, 294 Resolution, Horizontal, 11, 12, 33, 38, 46, 47, 53 Resolution, Vertical, 11, 12, 33, 38, 46, 53 Ringing, 67, 68, 70, 125, 131, 158 RTK, Real Time Kinematic, 56 Sampling, 33, 34, 35, 36, 38, 40, 41, 42, 44, 45, 46, 48, 50, 52, 53, 55, 83, 86, 87, 90, 91, 99, 101, 110, 111, 112, 179, 231, 235 Scattering, 50, 67, 68, 82, 113, 178 SCI, Spatially Constrained Inversion, 28, 115, 116, 118, 119 Seismograph, 125, 131 Shipment (instrumentation), 292, 293 Skin Depth, 18, 19, 24, 28 Smart‐Cities, IX SNR, Signal, to, noise ratio, 67, 83, 99 Sounding, TDEM, 99, 100 Spectrum, 38, 39, 40, 41, 42, 44, 45, 48, 72, 166 Spirito Santo Crypt, Italy, 163 Stacking, 44, 50, 51, 67, 68, 74, 83, 99, 102, 103, 109, 110, 112, 113 Taiwan, 204 Tilt‐meter, 108

Time, early, 98, 108, 109, 113, 119 Time, late, 98, 109, 113, 257, 261, 276 Time slice, 127, 131, 144, 145, 151, 155, 156, 171, 172, 185, 192, 199, 206, 222, 222, 223, 227 Total propagation loss (TPL), 50 Transform, Fourier, 45, 66, 67 Transform, Radon, 66, 67 Transform, Wavelet, 66, 67 Transillumination, 6, 8 Travel time, 53, 76, 176 Two‐way travel time, 7 Utility mapping, 123, 191, 197, 198, 204, 292 UXO, 21, 52, 85, 86, 123, 231, 231, 234, 251 Velocity of EM waves, 6, 7 Ventarron, Peru, 157 Visualization 3D, 70 VLF, Very Low Frequency, viii WARR, Wide, angle Reflection and Refraction, 6, 8, 223 Water table, 219, 223 Wavelength, 38, 39, 40, 41, 43, 47, 50, 51, 53, 178 Wave number, 39 Zone, Fresnel, 12, 50

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