BOOK OF ABSTRACTS
1

25th EM Induction Workshop
11-17 September 2022Çeşme-Turkey

emiw2022.emiw.org

BOOK OF ABSTRACTSInternational Association of Geomagnetism and Aeronomy
Division VI Electromagnetic Induction in the Earth and Planetary Bodies
www.emiw.org

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CONTENTS
Review Talks
Near surface EM technologies-Archaeological and environmental applications
I. AkcaPage
1

Unravelling electrical structure of the mantle with ionospheric, magnetospheric and oceanic
electromagnetic sources
A. Grayver

2

Electromagnetic Measurements on Volcanic Islands
D. Kiyan

3Electromagnetic Modeling Using Adaptive Grids - A Reflection on the Term Geometry
K. Spitzer

4Electromagnetic studies in the Eastern Mediterranean Region with Special Reference to Major
Transform (Strike-Slip) Faults
S. Tank

6

Electromagnetic technology for prospecting unconventional hydrocarbon resources
L. Yan7

Session 1 - Instrumentation, Sources and Data Processing
Archaeological prospecting using drone-towed electromagnetic and magnetic systems
T. B. Vilhelmsen

36

Identifying the causes for the vertical component geomagnetic field anomaly at Eskdalemuir
geomagnetic observatory, Scotland
G. Wang, J. Hübert and K. A. Whaler

37

From Coast to Coast: Ongoing Magnetotelluric Data Processing for the National-Scale Survey,
USMTArray-CONUS SouthJ. Crosbie, P. Bedrosian and A. Kelbert

38

ELMAR - the ELectroMAgnetic Recorder
O. Ritter, S. Rettig, R. Schmitt, M. Haxter, C. Müller-Brettschneider and U. Weckmann

39

Hybrid receiving dipole for broadband electric field measurement
N. Zorin, D. Epishkin, D. Yakovlev and A. Yakovlev

40

Theory and practice of Controlled Source Audio-frequency Magnetotellurics: discussion on two casestudies in France, at the Rochechouart impact structure and at the Strengbach catchment
P. Sailhac, Y. Quesnel, M. Lajaunie, S. Warden, C. Camerlynck, P. Lambert and J.-P. Malet43

The maximum possible distances to the remote reference when working in medium and high latitudes
D. Epishkin, A. Gubina, E. Shirokova and A. Yakovlev

44

New long-period magnetotelluric measurements to improve ground electric field modelling in the UK
during geomagnetic storms
E. Eaton, J. Huebert, C. Beggan, A. Montiel-Alvarez, A. Thomson, C. Hogg and D.Kiyan

45

Hilbert transform of the frequency normalized impedance data: Application to the dispersion relations
in magnetotellurics
A.T. Başokur

46

Synchronization optimization providing for MT stations at grid survey
A. Prystai, V. Pronenko and A. Bondarev

47

Smart data selection - Using machine learning for RMT data processing
A. Platz and U. Weckmann

48

Shaky data and where to find them - MT on frozen lakes
C. Patzer, U. Autio and J. Kamm

49

Results of FDEM-(CS)AMT test studies on the Alexandrovka area
E. Ermolin, N. Zorin, D. Epishkin and D. Sapunov

50

Distribution of source effects in the high latitude magnetotelluric data
S. Sanaka and Anne Neska

54

On the correctness of using plane-wave assumption and two-channel acquisition systems in MT
exploration at high latitudes
D. Yakovlev, E. Pogrebnykh, D. Epishkin and A. Yakovlev

55

Measurement of noise characteristics of graphite electrodes in the field and comparison with othertypes of non-polarizing electrodes
D. Epishkin and N. Zorin

56

Aurora: An open source magnetotelluric data processing package in Python linking MTH5 to EMTF XML
K.N. Kappler, G. Egbert, A. Frassetto, L. Heagy, A. Kelbert, L. Keyson, D. Oldenburg, J.R. Peacock and T.
RonanOpen-source Python software for the visualization of magnetotelluric data and three-dimensional
resistivity models
A. T. Başokur

59

60

The effect of radar trace number on tomographic images of cylindrical objects obtained by using GPR
O. Apaydın, T. İşseven, Y. Çıtır, S. Paker and I. Erer

61

Full correction of the electric field data biased by the ECR-effects
N. Zorin, D. Epishkin, D. Yakovlev and A. Yakovlev

62

Cloud connected low power, low noise systems for LMT & MT
V. Pronenko, K. Strack and A. Prystai

65

EM-ACROSS System: Installation at the Kusatsu-Shirane Volcano, Japan
S. Serita, Y. Ogawa, K. Ishizu, K.H. Tseng, T. Kunitomo, T. Minami, H. Ichihara, T. G. Caldwell, W. Heiseand E. A. Bertrand

66

Towards a AFMAG-capable airborne EM Sensor Platform - Identification of Noise Sources
A. Thiede and M. Becken
Session 2 - Theory, Modelling and Inversion
3D inversion of an integrated TEM survey
L. Xiao, G. Fiandaca, P. K. Maurya and A. V. Christiansen

67

68

Joint Probabilistic Inversion of 3D Magnetotelluric and Seismic Data in Southeast Australia
M.C. Manassero, J.C. Afonso, S. Özaydin, A. Kirkby, I. Fomin, A.G. Jones and K. Czarnota

72

Petrophysical-based constrained and joint inversions of magnetotelluric (MT) and gravity data-sets on
unstructured tetrahedral meshes
M. Kangazian and C. Farquharson

73

True 3D Land CSAMT Modeling
W. Soyer and R.L. Mackie

74

Using deep learning for model error estimation in 3D probabilistic inversion of controlled-source
electromagnetic data
M. W. Elías, M. Rosas-Carbajal and F. I. Zyserman

78

Surface geometry inversion of marine CSEM data
X. Lu, C. Galley, P. Lelièvre and C. Farquharson

79

Applying a multi-transmitter hybrid Conjugate Gradient-Occam algorithm to the inversion of 3D mCSEM
data
W. Lima, G. Egbert , N. Meqbel, A. Benevides, S. Fontes, E. LaTerra and P. Werdt

80

Real-time simulation of the electromagnetic field spatiotemporal evolution due to geomagnetic
disturbances
M. Kruglyakov, A. Kuvshinov and E. Marshalko

81

HIP-FEM: A Hierarchical, Induced Polarization Finite Element Method for analysis of thin, dispersive,geoelectric features
C.J. Weiss, G.D. Beskardes and A. Darrh

82

3-D radio-frequency CSEM at the Weidenpesch waste site in Cologne
M. Smirnova, B. Tezkan, A. Shlykov, S. Fadavi, A. Saraev, P. Yogeshwar

83

Hybrid GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion
problems
H. Dong, K. Sun, G. Egbert, A. Kelbert and N. Meqbel

87

Speeding up the inversion of the 3D MT problem
D. Varilsüha

88Open-source 3D inversion of semi-airborne electromagnetic data
R. Rochlitz, T. Günther and M. Becken

92

Application of the total and scattered field decomposition and perfectly-matched layers to improve theaccuracy in electromagnetic modelling
L. M. Buntin, T. Kalscheuer, G. Kreiss and Z. Ren

93

An efficient 3D EM modeling scheme based on a radiation boundary approach
R. Dehiya and A. Singh

94

Electromagnetic imaging using high-order FE and goal-oriented meshing
O. Castillo-Reyes, P. Rulff and E. S. Um

95

2D Electrical Resistivity Modelling on highly distorted, non-smooth, rough grids
D. Suryavanshi, R. Dehiya

96

Regularization of VLF inversion using rank order smoothing
G. Karcıoğlu, A. B.Tekkeli, Ü. Avşar, M. A. Üge and M. S. Arslan

97Frequency dependent complex resistivity inversion in 3D from Controlled-Source Electromagnetic data
J. Porte, F. Bretaudeau and J-F. Girard

98

Gradient and roughness regularization operators for geophysical inversion on unstructured meshes using
L2 and L1 norms
M. Kangazian and C. Farquharson

102

1D and 3D Inversion and Modelling of Airborne Transient Electromagnetic and Magnetic Data from Overa Potential Volcanogenic Massive Sulphide Deposit, Cripple Creek, Newfoundland
A. Demirbas and C. G. Farquharson

103

2D U-Net convolutional networks for 1D inversion of magnetotelluric data
M. R. Jevinani, B. H. Dehkordi, M. H. Rohban and I. J. Ferguson

104

3D Minimum-structure Inversion for CSEM Problems Using Potentials and Unstructured Tetrahedral
Grids
K. B. Kara and C. G. Farquharson

105

ModEM software: An update on the improvements, availability, and performance metrics
A. Kelbert, G. Egbert, H. Dong, N. Meqbel and L. Zhongyin

106

A MATLAB FE Library for the Simulation and Inversion of EM Problems
J. Blechta, R.-U. Börner, O. Ernst, M. Scheunert and Klaus Spitzer

107

3D modeling of CSEM data in the radio frequency band with different sources
S. Schöttle, M. Smirnova, B. Tezkan, P. Yogeshwar and M. Smirnov

108

CRT3DMT: A three-dimensional magnetotelluric inversion package with adaptively refined unstructured
inversion grid and an application to lithospheric conductivity structure beneath North China
H. Chen, Z. Ren and J. Tang

109

UHOMT: A novel 3D finite element Magnetotelluric forward modeling code with unstructured meshes
D. Ruiz-Aguilar and E. U. Gallardo-Romero

110

3D Inversion of Controlled-Source Electromagnetic Data using Non-linear Conjugate Gradients
P. Rulff and T. Kalscheuer

111

2D and 3D Forward modeling of electromagnetic fields in the time domain using Discontinuous Galerkin
Method and Spectral Element Method
B. Valdés-Moreno. M. A. Pérez-Flores and J. D. De Basabe

112

Time-dependent adaptive mesh refinement for 3D forward modelling of transient electromagnetic fields
in volcanic environments including topography
C. Schneider, K. Spitzer and M. Hort

113

A parallel adaptive finite-element method for 3-D large-scale controlled-source electromagnetic forward
modelling with hierarchical tetrahedral grids
Z. Liu, Z. Ren, H. Yao, J. Tang, X. Lu and C. Farquharson

117

3D EM modeling and inversion with a mixed finite-element and finite-difference approach to handle hightopography and bathymetry variations
S. Védrine, R. Rochlitz and F. Bretaudeau

118

Multi-scale 3-D conductivity model of the contiguous US from the inversion of MT USArray
F. Munch and A. Grayver

119

Joint inversion of magnetotelluric data and receiver functions using Pareto-based swarm intelligencealgorithm
E. Büyük and E. Zor

120

Development of an efficient 3D inversion algorithm for large-scale MT data
A. Singh and R. Dehiya

121

3-D Modeling of Airborne and Land-based Controlled-Source Electromagnetic Data: Comparison on CPU
and GPU Platform
İ. Demirci

122

POLYEM3D: A flexible 3D CSEM and MT modeling and inversion code
F. Bretaudeau, F. Dubois, J. Porté and S. Védrine

126

Magnetotelluric system NORD
D. Epishkin, A. Yakovlev, D. Yakovlev and N. Zorin

127

Well integrity monitoring with electric fields by using hierarchical geo-electric models
G.D. Beskardes and C.J. Weiss

131

Magnetotelluric imaging of the Mitidja Basin structure, North of Algeria
N. Kerbadj, A. Bouzid and A. S. Kasdi

132

Sensitivity of phase tensors to absolute resistivities in a 3-D world
L. Dambly, F. Samrock, A. V. Grayver and M.O. Saar

136

Conductivity structure beneath Australia constrained by 3-D inversion of tippers in spherical geometry
F. Cicchetti, A. Grayver, R. Rigaud, A. Kuvshinov and A. Yoshikawa

137

Constraining the 1-D electrical conductivity of the crust and mantle beneath continents by the joint
inversion of multi-source electromagnetic transfer functions
R. Rigaud, A. Kuvshinov, A. Grayver, F. Perrier and M. Kruglyakov138

Dual L-shape model: a possible cause of anomalous magnetotelluric phase in central India
K. Raju and P. K. Patro

139

Three-Dimensional Inversion of Magnetotelluric Data from the Tarawera Dome Complex, New ZealandP. Semper, E. A. Bertrand, G. Caldwell, W. Heise, M. Scheunert and K. Spitzer

140

Plane wave correction and 3D inversion of tensor CSRMT data
A. Shlykov, A. Saraev, N. Bobrov and B. Tezkan

141

Convolutional Neural Networks Applied to 2D and 3D DC Resistivity Inversion
S. Weit, R.-U. Börner, M. Brändel, P. Gödickmeier, R. Gootjes, S. Kost, O. Rheinbach, M. Scheunert and K.
Spitzer

145

Multivariate statistical analysis of geophysical data and models by neural network approaches
R. Vadoodi, T.M. Rasmussen and M. Abdolmalek

149

Application of the skew parameters in 1-D and 2-D inversion of MT data
D. Yakovlev, K. Koryagin, D. Epishkin, N. Zorin and A. Yakovlev

150

Impedance of capacitive electrodes and wires on the ground surface
N. Zorin, D. Epishkin, D. Yakovlev and A. Bobachev

151

3D imaging of electrical conductivity structures in the Eastern Cheb Basin across the Hartoušov andBublák mofettes
B. Aleid, U. Weckmann, A. Platz, J. Pek, S. Kováčiková and R. Klanica

152

Time-lapse resistivity imaging: CSEM-data 3D double-difference inversion and application to the
Reykjanes geothermal field
F. Dubois and F. Bretaudeau

153

Global Optimization Inversion of Horizontal Electric Dipole Time-Domain Electromagnetic Data Using
Particle Swarm Optimization
C. A. Hapsoro, W. Srigutomo and E. Agustine

157

Singular Value Decomposition of the Phase Tensor
M. Karaş and S. B. Tank

158

Modelling tippers in a spherical geometry
M. Kruglyakov and A. Kuvshinov

159

Anomalous Phase in Elongated Prism Body: A Synthetic 3D MT Forward Modelling
D. Kumar, A. Singh and M. Israil

160

Electrical resistivity tomography image enhancement using neural network
K. Phueakim, C. Vachiratienchai and P. Amatyakul

161

Hydrothermal system beneath Mt.Erciyes inferred from Three-dimensional Magnetotellurics, Central
Anatolia, Türkiye
R.Yazıcı, M.Karaş, S. Üner and S.B. Tank

162

Static shift correction in sedimentary basins
D. Yakovlev and A. Yakovlev

163

Near surface resistivity structure estimated from time domain electromagnetic data recorded along a
profile in HFT Zone in Mohand area, Uttarakhand, India
M. Israil, R. R. Ansari, M. Zubair, P. Yogeshwar and B. Tezkan

164

Some fragments of my 70 years activity in EM geophysics
I. I. Rokityansky

168

Improving geophysical model resolution with magnetotelluric and gravity joint inversion: application to
the Asal Rift geothermal region, Republic of Djibouti
R. R. Rageh, P.l Tarits, S. Hautot and M. Jalludin

172

3D inversion of drone EM data -- the DroneSOM project
L. Xiao, C. Patzer and J. Kamm

176

Session 3 - Exploration, Monitoring and Hazards
Drone based experimental TEM surveys over Lake Baikal and a uranium occurrence
V. Hallbauer-Zadorozhnaya, Yu. A. Davydenko, A. V. Parshin and E. Stettler

177

Regional to Deposit scale exploration in Fennoscandia based on mineral systems approach
M. Yu. Smirnov, G. Hill, J. Kamm, J. Vozar, Jirigalatu, P. Mishra, K. Muhumuza and D-REX Working Group

181

Results from the DESMEX semi-airborne EM survey at the Gosetal/Rammelsberg (Harz Mountains,
Germany)
A. Thiede, M. Becken, P.O. Kotowski and the DESMEX Working Group

182

Examination of geomagnetic data as precursors of the September 5, 2018 (MW = 6.6) and August 20,
2016 (MW = 6.0) earthquakes in Japan
H. Taherinia And S. Pourbeyranvand

186

Recognition of pre- and co-seismic electromagnetic signals in magnetotelluric measurements: a casestudy in the northern region of Algeria
A. S. Kasdi, M. Hamoudi, A. Bouzid and N. Kerbadj

190

Investigation of Earthquake Swarm and Buried Geothermal Resources By Magnetotelluric, Gravity
Modeling and Seismological Analyses of Upper Crust Structure of Yalova-Termal Region
E. Pekşen, D. Çaka, B. Tunç, B. Oruç, E. Budakoğlu, T. Türkmen, F. Sevim, D. Durdağ, K. Zengin, M. E.
Erkan, G. Durdağ and Ş. Barış

194

Assessment of geoelctric field variability in Yenisei-Khatanga oil and gas province and space weather
hazards for infrastructure
Е. Yu. Sokolova, Е. E. Marshalko, O. V. Kozyreva, I. S. Kupriyanov, D. V. Epishkin, G. E. Slinchuk and D. V.
Yakovlev

198

Induction Responses from Magnetotelluric Transfer Functions in Southland, New Zealand
K. Pratscherz, M. Ingham, W. Heise, T. Bertrand, D. Mac Manus, C. Rodger, M. Dalzell and T. Petersen

202

Cumbre Vieja volcanic eruption (La Palma, Canary Islands): Magnetotelluric monitoring experiment
P.Piña-Varas, J. Ledo, D. Martínez Van Dorth, P. Queralt, I. Cabrera Pérez, L. D’Auria and N. Pérez

203

Geothermal exploration via magnetotelluric surveys in non-volcanic geothermal fields in northern
ThailandP. Amatyakul, T. Rung-Arunwan, C. Vachiratienchai and W. Siripunvaraporn

204

CSEM/MT imaging for deep EGS geothermal project derisking in the Upper Rhine Graben (France)
M. Darnet, F. Bretaudeau, P. Wawrzyniak, J.-F. Girard, G. Marquis, V. Maurer, A. Genter

205

Integration of magnetotelluric data with ambient seismic noise and gravity models in order tocharacterize geothermal fault-controlled systems. The Valles Basin case of study (NE Spain)
G. Mitjanas, J. Ledo, P. Queralt, P. Piña-Varas and A. Martí

208

Hydrothermal model of Aso volcano, Central Kyushu, Japan, inferred from AMT and ACTIVE datasets
T. Minami, M. Gresse and M. Utsug

212

Using Magnetotelluric and Differential Magnetometer Data to Quantify Space Weather Risk in the UK
High Voltage Power Transmission GridJ. Hübert, C. Beggan, G. Richardson, N. Gomez Perez and A. Thomson

213

Time-lapse 3D CSEM for reservoir monitoring based on rock physics simulation
M. Ettayebi, S.Wang and M.Landrø214

3D modeling and inversion of ground-based TEM data, a case study of seawater intrusion on the eastern
coast of the Gulf of Aqaba, Jordan
J. Abu Rajab, H. El-Kaliouby, E. Al Tarazi and H. Al-Amoush

218

Using 3-D electrical conductivity model for understanding geological units of nonvolcanic geothermal
reservoirs
B. Erdenechimeg, F. Samrock, A.V. Grayver, A. Kuvshinov, M.O. Saar, D. Sodnomsambuu, Ts. Shoovdor
and P. Dorj

222

Exploration of deep aquifer in North Jordan using TEM and MT
G. Kapinos, N. Atteyat, N. Jahed, F. Brückner, F. Lindenmaier, A. Margane, M. Toll and P. Yogeshwar

223

Exploration of geothermal areas at the central part of Mexico through the application ofMagnetotellurics and Transient Electromagnetics
D. Ruiz-Aguilar, E. García-Suárez, I. J. Cruz-Aquino, E. Pioquinto-Arcos, L. S. Roque-Pineda, L. Peiffer, C.
Inguaggiato, L. Delgado-Argote, M. Contreras-López, and C. Arango-Galván

224

Determination of the cap rock integrity in the Çanakkale-Tuzla hydrothermal system from inversion ofmagnetotelluric data by using particle swarm optimization
E. Büyük and A.Karaman

225

Magnetotelluric investigations in the Ubaye valley, Western Alps: a connection between electricalconductivity, fluids, and earthquakes?
S. Byrdina, J-L. Got, L. Metral, P. Hering, M. Baques, L. De Barros, S. Garambois, P. Gueguen and V. Rath226

Two-dimensional electrical resistivity model of Sabalan geothermal field using Magnetotelluric data
G. A. Fanaee Kheirabad and B. Oskooi

227

Analysis of Geothermal Manifestation Distribution at Blawan-Ijen Geopark, East Java, Indonesia basedon Magnetotelluric and Gravity Data for Determining the Recommendation of PLTP Location
A. Ibrahim, C. A. Hapsoro and S. Zulaikah

231

Interpretation and modeling of airborne and ground magnetometry data for a geothermal reservoir in
the Abgarm region of Mahallat in Iran
B. Oskooi, A. Junge, S. H. Hosseini, B. H. Dehkordi and S. M. Ghiasi

232

New insight of the hydrothermal system associated with Tolhuaca volcano (South Chile) revealed bymagnetotelluric observations
M. Pavez, D. Diaz, V. Goldberg, H. Brasse, I. Budach, G. Kapinos and E. Schill

236

Magnetotelluric imaging of a shallow groundwater system in central Zagros, Iran
M. Montahaei

237

Large-scale mineral system study in Finland using 3D magnetotellurics
P. K. Mishra, J. Kamm, C. Patzer, U. Autio, K. Vaittinen, K. Muhumuza, M. Yu. Smirnov, G. Hill and D-Rex
Working Group240

Deep mineral exploration using semi-airborne electromagnetics: Investigation of a graphite deposit
W. Mörbe, P. Yogeshwar, B. Tezkan, P. Kotowski, A. Thiede, M. Becken, A. Steuer, H. Petersen, M.
Schiffler, R. Stolz, R. Rochlitz and T. Günther

241

The value of full tensor magnetotellurics, gravity and electrical resistivity tomography for Lithiumprospecting. A case study in Argentina
A. Curcio, E. Chanampa, L. Cabanillas and R.Piethe

242

Whole-lithosphere architecture of a mineral system and signatures of the sources and pathways of oreforming fluids
M. J. Comeau, M. Becken and A. V. Kuvshinov

243

3-D Magnetotelluric Forward and Inversion of the Chicontepec oil basin
O. Avila, F. Corbo and C. Castro
Sedimentary copper mineral systems: Large scale resistivity footprints in the Adelaide Rift Complex,
South Australia
B. Kay, G. Heinson, K. Brand, S. Thiel and G. Boren

244

Crustal geoelectrical distribution of Kalgoorlie gold camp mineral system (Western Australia)
P. Piña-Varas, M.C. Dentith

246

Geophysical signature of the sedimentary/basement transition zone from seismic and CSEM. Analysis
from a shallow analogue of the Rhine Graben
F. Bretaudeau, M. Darnet , J. Porté , C. Lerouge, S. Neeb, J.F. Girard, J.M. Baltassat, N. Coppo and C.
Dezayes

247

Combined 3D inversion of MT and CSEM-data from Malmberget northern Sweden
O. Rydman, M.Yu. Smirnov, H.V.D. Berg and N. Juhojuntti

248

245

Experience of the solution of engineering and environmental tasks using the CSRMT method
A. Saraev, A. Shlykov and B. Tezkan

249

Joint 3D inversion of nearshore and land MT and CSEM data in coastal areas of volcanic islands:
application to the Bouillante geothermal field
S. Védrine, P. Tarits, F. Bretaudeau, S. Hautot and M. Darnet

250

CSEM monitoring in Izu-Oshima volcano, JapanT. Koyama and M. Uyeshima

254

Magnetotelluric Images of Volcanic Zones in NE Japan Arc and Co-Seismic Deformations during the 2011
Tohoku M9 Earthquake
S. Masuda, Y. Ogawa and M. Ichiki

255

First experience with high power EM towards the energy transition
K. Strack, S. Davydycheva, T. Hanstein, Y. Martinez, A.Y. Paembonam, V. Pronenko, M. Smirnov, P.
Soupios and X. Xu

256

Multidimensional inversion of transient electromagnetic data for the exploration of clay pans in the
Atacama Desert, Chile
B. Blanco-Arrué, P. Yogeshwar, B. Tezkan, Y. Liu, R. Peng and V. Wennrich

257

Characterization of a landfill using magnetotellurics: The Garraf case
A. Martí, P. Queralt, A. Marcuello, J. Ledo, G. Mitjanas, P. Piña-Varas, A. Freixes, J. Solà and P. Pons

258

Corrections for near surface effects contaminating MT data over a salt diaper, North West Kashan, Iran
E. Zare, M. Montahaei and H. Esmaili Oghaz

259

Current use of Frequency-domain Electromagnetic Induction in precision agriculture: Knowledge gained
from six years of experiments in Portugal
M. Farzamian, F. A. Monteiro Santos, N., A. M. Paz, F. J. Martinez Moreno, T. B. Ramos, M. C. Paz and M.
C. Gonçalves

260

An electrical resistivity model of the San Pedro-Ceboruco graben: 3-D inversion studies and comparisons
between standard and advanced Magnetotelluric transfer functions
C. Castro, A. Junge, H. Eysteinsson, P. Hering, L. González-Castillo and L. Ferrari

261

The use of the “floating” S-plane for effective interpretation of airborne TEM data
V. Hallbauer-Zadorozhnaya and E. Stettler

262

Local to Regional Scale 3D study around Gallivare, Sweden based on Integration of 3D Magnetotellurics
with other Geophysical Data
Jirigalatu, M. Yu. Smirnov, T. M. Rassmussen, O. Rydman, J. Vozar, T. Bauer, J. Gao, S. Kovachikova, N.
Juhojuntti, T. Hermansson, K. McGimpsey, H. Van Den Berg, G. Hill, J. Kamm and D-REX Working Group

266

MTHEK Project: MagnetoTelluric Assessment of the HEKla Volcano
D. Kiyan, Á. Benediktsdóttir, G. P. Hersir, M. T. Guðmundsson, C. J. Bean, C. Hogg, Þ. Jónsson and J. E.
Jónsson267

Calculating geoelectric fields using a lithospheric resistivity model of the Iberian Peninsula
R. Hafizi, A. Martí, P. Piña-Varas, G. Mitjanas, J. Campanyà, A. Marcuello, J. Ledo and P. Queralt268

An assessment of galvanic distortion effects contaminating MT data from Central Iran
M. Sajedi, M. Montahaei and H. E. Oghaz

269

Towards a new 3D conductivity model of the British Isles: Revisiting MT data from Isle of Skye, Scotland
A. Montiel-Álvarez, J. Hübert and K. Whaler

270

Geophysical Imaging of the Roter Kamm Crater in the Sperrgebiet National Park, Namibia, using TEM
and AMT
H. Nienhaus, P. Yogeshwar, W. Mörbe, B. Tezkan, B. Lushetile and M. Melles

271

The site selection procedure for a high-level radioactive waste repository in Germany: Future applicationof electromagnetic methods for exploration activities
D. Rippe, K. Bairlein and Frank Meier272

Donbas geoelectrical structure
I. I. Rokityansky and A. V. Tereshyn

273

Imagery down to one kilometer depth by airborne electromagnetics: New constraints for geological andhydrogeological modeling in volcanic contexts
A. Raingeard, P.-A. Reninger, A. Peyrefitte, G. Martelet, B. Aunay, A. Malard and F. Dubois

274Electromagnetic Studies on The Qarun Protected Area, Fayoum-Province, Egypt
M. Mekkawi, A. Ibrahim, A. Awad, A. Khalil and M. Ibrahim

277

Session 4 - Tectonics, Magmatism, Geodynamics
Inferring the roots of volcano-geothermal systems in the Rotorua and Okataina calderas withmagnetotelluric models
E. A. Bertrand, P. Kannberg, T. G. Caldwell, W. Heise, S. Constable, B. Scott, S. Bannister, G. Kilgour, S. L.
Bennie, R. Hart and N. Palmer

279

The formation of geothermal systems in the context of magma-assisted continental rifting:
Magnetotelluric models from the Main Ethiopian Rift (MER)
L. Dambly, F. Samrock, A.V. Grayver, H. Eysteinsson and M.O. Saar

280

Electromagnetic Study on The tenth of Ramadan City, Eastern Desert, Egypt
M. Mekkawi, A. Ibrahim, A. Awad, A. Khalil and M. Ibrahim

281

Plate coupling at the northern Hikurangi margin: new results from magnetotellurics
W. Heise, S. Bennie, G. Caldwell, T. Bertrand, Y. Ogawa, S. Bannister, G. Archibald, T. Nishizawa, R. Hart,
N. Palmer, K. Seki, M. Fukai, K. H. Tseng and J. McGrath

283

Investigation of Lithosphere Structure of Northwestern Anatolia with long-period magnetotelluric: Part
1. acquisition data by using remotely controlled system and comparison to previously collectedbroadband magnetotelluric data
İ. Demirci, N. Y. Gündoğdu, M. D. Oskay and M. E. Candansayar284

3D lithospheric structure beneath the Marmara Sea by Magnetotellurics
T. Kaya-Eken, Y. Ogawa, Y. Usui, T. Kasaya, M. K. Tunçer, Y. Honkura, N. Oshiman, M. Matsushima and
W. Siripunvaraporn

288

3D imaging of the subsurface electrical resistivity structure in West Bohemia/Upper Palatinate coveringmofettes and Quaternary volcanic structures by using Magnetotellurics
A. Platz, U. Weckmann, J. Pek, S. Kováčiková, R. Klanica, J. Mair and B. Aleid

289

Investigation of Deep Structure of Sultandag Fault by Magnetotelluric, Gravity, GNSS, and Tectonic
studies; First Results
Ö. Özyıldırım, İ. Demirci, Ç. Özkaymak, Ö. Bektaş, C. Başaran, İ. Tiryakioğlu, D. M. Özcan and A. Yıldız

290

Integrated geophysical modeling of 2D/3D data in the Western Carpathians
J. Vozár, V. Bezák, M. Bielik and L. Ondrášová

294

Magnetotelluric investigation of the Denizli graben in the Western Anatolian Extensional Province
Ü. Avşar and E. Türkoğlu

295

Investigation of Lithosphere Structure of Northwestern Anatolia with long-period magnetotelluric data:
Part 2. comparison to the 2D inversion of broadband and long-period magnetotelluric data
M. E. Candansayar, İ. Demirci, N. Y. Gündoğdu and M. D. Oskay

296

Magnetotellurics reveals a hidden caldera and its relation to regional tectonics in the Cappadocia region,
central Anatolia, Turkey
Ö. Hacıoğlu, A. T. Başokur, N. Meqbel, H. I. Arslan and T. Efeçınar

297

Estimating the melt fraction of magma reservoirs using MELTS and magnetotellurics
D. Cordell, S. Naif, J. Troch and C. Huber

298

Resistivity Models of Southwestern Canada: New insights into lithospheric structure, magma bodies,and geothermal systems
C. Hanneson and M.J. Unsworth

299

Constraining the size and state of magma reservoirs through a quantitative approach combining MT, lab
measurements and petrological modelling
F. Samrock, A.V. Grayver, O. Bachmann, Ö. Karakas, L. Dambly and M.O. Saar

303

3-D model of the deep structure of the Yenisei-Khatanga regional troughD. Yakovlev, G. Slinchuk and A. Yakovlev

304

Compaction-driven fluid localization and stagnation can explain lower crustal low-resistivity zones
M. J. Comeau, Michael Becken and A. V. Kuvshinov

305

The Curnamona Cube, new data and insights
B. Kay, G. Heinson, K. Brand, S. Thiel and G. Boren

306

First Magnetotelluric imaging of the northern Zagros orogenic belt (Preliminary report on measured dataand processing techniques)
Sh. Zhian, A. Junge and B. Oskooi

307

What are the compositional causes behind electrical conductivity variations in continental lithosphericmantle? Methodology and practice for quantified interpretations
S. Özaydın, K. Selway, M. Moorkamp, W. L. Griffin and C. Manassero

308

Imaging of an intraplate volcanic system from source to surface
M. J. Comeau, M. Becken and A. V. Kuvshinov

309

Crustal structure across Indus Tsangpo Suture zone NW Himalaya, India as revealed from
Magnetotelluric study
C. K. Rao

310

Long period magnetotelluric at the Antarctica: The role of asthenospheric mantle anisotropy in Glacial
Isostatic Adjustment
L. González-Castillo, A. Madarieta-Txurruca, G. Hill, C. Castro, J. Galindo-Zaldívar and A. Junge

311

Investigation into lithospheric mantle of Northern Tanzania utilising 3D magnetotellurics
S. Özaydın, K. Selway, S. Foley, P. Tarits and S. Hautot

312

The Electrical Signature of the Manzaz and Atakor Intraplate Cenozoic Volcanism (Central Hoggar, Southof Algeria)
Z. Boukhalfa, A. Bouzid and A. Benhallou

313

Electrical resistivity structure beneath the southern Tohoku, Northeast Japan, inferred from a joint
inversion of magnetotelluric and geomagnetic transfer functions
D. Diba, M. Uyeshima, M. Ichiki, S. Sakanaka, M. Tamura and Y. Usui

314

Crustal Structure Beneath East Himalayan Syntaxis and the Relation to its Rapid Uplift and Exhumation
H. Dong, J. Qi, S. Jin, G. Ye and W. Wei

315

Magnetotelluric investigations in south of Mexico to better understand the seismic hazard of the area
D. Ruiz-Aguilar, A. Husker, C. Arango-Galván, J. M. Romo-Jones, E. García-Suárez and S. Constable

316

The Geometry of the Main Himalayan Thrust along the Satluj river valley, Northwest Himalaya, India
retrieved from Magnetotelluric studies
S. Dhamodharan and R. Gautam

317

Integrated geophysical study of the deep structure of Yenisei-Khatanga regional trough: new results and
MTS contribution
Е. Yu. Sokolova, E. M. Bolshakov, I. A. Biserkin, M. Ya. Finkelshtein, I. S. Kupriyanov, N. N. Pimanova and
T.P. Shirokova

318

Mapping the geometry of volcanic systems with magnetotelluric soundings: results from a land andmarine magnetotelluric survey performed during the 2018-2019 Mayotte seismovolcanic crisis
M. Darnet, P. Wawrzyniak, P. Tarits, S. Hautot and J.-F. D’eu

319

Session 5 - Marine EM
3D CSEM inversion data at Campos basin Brazil constrained by seismic and well log
A. Benevides, N. Meqbel, W. Lima, S. Fontes, G. Egbert, P. Werdt and E. La Terra

323

Comparing results from a new bottom-towed CSEM system against seismic and core data
R. B. King, A. Gusick, S. Constable and J. M. Maloney

324

Multi- EM surveying and data analysis for deep-sea seafloor massive sulphide exploration
K. Schwalenberg, H. Mueller, U. Barckhausen and the INDEX Exploration Team325

Characterizing Offshore Freshened Groundwater in a Carbonate Shelf Using Integrated Geophysical andGeochemical Analysis: A Case Study from the Maltese Islands
Z. Faghih, A. Haroon, M. Jegen, C. Berndt, B. A. Weymer, K. Reeck, T. Müller and M. Schmidt

326

The dependence of the tsunami electromagnetic signals observed at islands on the subsurface resistivity
R. Shibahara and T. Minami

327

Revisit of the mantle electrical structure beneath the Tristan da Cuna hotspot by using a 3-D inversion
based on non-conforming deformed hexahedral mesh
R. K. Singh, K., Baba, Y. Usui, A. Grayver, M. Jegen, A. Morschhauser, W. Geissler, J. Matzka, A. Haroon
and A. Kuvshinov

328

Imaging deep resistivity in 3D in coastal areas and volcanic islands: Toward a multi-method and multiscale approach combining land and shallow water passive and active EM
F. Bretaudeau, S. Védrine, P. Tarits, J-F d’Eu, Q. Daverdisse, N. Coppo, P. Wawrzyniak, S. Hautot, F.
Dubois, E. Civallero, F. Beaubois, Y. Legendre and M. Darnet

329

Links between slab mantle dehydration and forearc seismogenic zone structure in the Shumagin Gap,
Alaska using magnetotelluric imaging
D. Cordell, S. Naif, R. Evans, K. Key, S. Constable, D. Shillington and A. Bécel

330

Modelling 3D coast effects in marine magnetotelluric data using edge-based finite element method
J. Long and S. Wang

331

Session 6 - Rock and Mineral Resistivity, and Anisotropy
Multi-Data Inversion Approach for Retrieving Rock Properties from Measurements on Drill Cuttings
J. H. Börner, V. Herdegen, J.-U. Repke and K. Spitzer

335

Comparative 3D inversion of magnetotelluric phase tensors and impedances reveals electricallyanisotropic base of Gawler Craton, South Australia
K. Tietze, S. Thiel, K. Brand and G. Heinson

336

Electrical monitoring of dynamic drainage and imbibition processes in rock-fluid-gas systems
M. Sonntag, J. Börner, V. Herdegen, F. Grahl and K. Spitzer

337

Imaging and inversion of potential field data, a case study for exploring Iron-bearing zones in Golgohar,
Iran
B. Oskooi, P. Mansourshoar and Maysam Abed

338

Imaging the weathering zone in Chile with active Radio-Magnetotellurics
U. Weckmann, J. Cruces Zabala, C. Patzer, O. Ritter, and J. A. Vargas

339

Anisotropy estimation using 1D joint inversion of DC resistivity and CSRMT methods in the granite-gneiss
terrains of Eastern Ghats, India
A. Singh, S. Agrahari, A. Shlykov, A. Saraev and A. Yadav

340

Session 7 - Global And Planetary Studies
Global induction response to 11-year period and the conductivity of the lower mantle
S. Constable, C. Constable, M. Korte and M. Morzfeld

344

Signatures of the global ocean circulation in geomagnetic secular variation and acceleration
C.C. Finlay, J. Velímský and C. Kloss

345

Limits of a-posteriori interpretation of electrical conductivity in terms of water content
O. Knopp

346

Regionality of mantle conductivity inferred from geomagnetic daily variation analysis
T. Koyama, S. Fujita, I. Fujii, K. Baba and H. Shimizu

347

Hunga-Tonga Hunga-Ha'apai Eruption lightning as seen by remote MT measurements in New Zealandand Japan
T. G. Caldwell, P. A. Jarvis, C. Noble and Y. Ogawa

348

MagVector/MFX-2 - a Planetary Laboratory on the International Space Station (ISS): Electromagnetic
Simulation and Inversion of Magnetic Field Data from Planetary and Asteroid Analogs
J. Börner, S. Garbade, S. S. Keßler, D. Konigorski, V. Schmid, L. Schmitt, C. Schneider, F. Sohl and K. Spitzer

349

Deep geomagnetic sounding by Sq variations in Europe: A 3-D inversion based on the regional-to-local
transfer functions
J. Velímský, L. Šachl and O. Knopp

350

3-D inversion of tippers estimated at a continental grid of Chinese geomagnetic observatories:
Preliminary results
S. Xu, A. Kuvshinov, C. Chen, M. Kruglyakov, R. Rigaud, Z. Ren and X. Hu

351

Investigation of the Impact of Convectively Coupled Equatorial Waves (CCEW) and Total ElectronContent (TEC) on the Diurnal Cycle in Indonesia as Early Warning System of Equatorial Climate Change
M. K. Rifai, C. A. Hapsoro and E. Latifah

352

Variations of the induction vector, worldwide study
I. I. Rokityansky

353

Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multisource magnetic transfer functions
C. Chen, A. Kuvshinov, M. Kruglyakov, F. Munch and R. Rigaud

357

Session 8 - EM Induction Education and Outreach
Making geo-electromagnetic (magnetotelluric) data accessible via EPOS portal
M.Yu. Smirnov, J. Hübert, O. Ritter, A. Neska, T.M. Rasmussen, P. Hejda, S. Flower, A. Chambodut, J. J.
Curto, J. Matzka, A. Thomson, A. Viljanen

358

Multidimensional Interpretation of Controlled-Source Radio-Magnetotelluric (CSRMT) of a waste-site in
Cologne, Germany
S. F. Asghari, A. Shlykov, M. Smirnova, A. Saraev, P. Yogeshwar and B. Tezkan

359

Delineating subsurface structures for deep aquifer study using magnetotellurics, and airborne
geophysics. Case study of the Voltaian sedimentary basin, Ghana, West Africa
R.A. Mejida, P. Tarits, T.E. Armah, S. Hautot and S.M. Yidana

360

Investigation of the topographic effect in the San Pedro-Ceboruco graben: real data andsynthetic studies
M. K. Díaz, C. Castro, A. Junge and F. Corbo363

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Near surface EM technologies – Archaeological and environmental applications
I. Akca1
1 Ankara University, Department of Geophysical Engineering, Turkey, iakca@eng.ankara.edu.tr

SUMMARYElectromagnetic methods (EM) are a special branch of geophysical studies applied for the solution of a variety
of problems where the target depth ranges in centimeters to hundreds of kilometers. The majority of EM
applications deals with deeper portions of the subsurface where the first few tens of meters generally
disregarded. However, these depths also include valuable exploration targets from various point of views and
EM also provide suitable tools for the investigation of such shallow targets. At this scale, the exploration targets
are generally man-made or natural features/structures embedded into first few meters from the ground surface.
The main EM tool used at near surface applications is the ground penetrating radar (GPR) a high frequency
method providing high resolution images of subsurface. Therefore, this review is mainly focusing on theapplication of GPR method at investigations such as sinkhole detection, groundwater related surveys,
agricultural investigations, ice and snow research and finally archaeological surveying. A recent trend in nearsurface application of EM studies is the determination of certain physical properties of soil and/or rocks directlyfrom geophysical data. The review consists of three parts: (1) development in the instrumentation of shallow
electromagnetic methods (mainly GPR), (2) latest data and image processing methodologies, (3) casehistories and examples. The examples presented in the paper are mostly from archaeological surveys whichhave a significant weight in the literature as well.

Keywords: GPR, Data Processing, Archaeology, Environmental Geophysics

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Unravelling electrical structure of the mantle with ionospheric,magnetospheric and oceanic electromagnetic sources
Alexander Grayver
Institute of Geophysics, ETH Zürich, agrayver@ethz.ch

1

SUMMARY
This review presents the progress made in the last decade in the mantle studies with natural electromagnetic(EM) induction sources, which span periods from seconds to years and have diverse origins. Thesemechanisms produce field variations that can sense Earth’s electrical structure at different scales in a depth
range from the crust to the lower mantle. The last decade has seen substantial progress in different areas
related to the methods, data collection and numerical modelling of EM phenomena. Specifically, new methodson handling complex ionospheric and magnetospheric sources were proposed, accompanied by more efficientforward and inverse modelling tools that allow us to combine several sources and constrain electricalconductivity on multiple scales simultaneously. Further, the launch of Swarm satellites in 2013 with extremely
successful operation have marked a new era in the field of large-scale EM induction, presenting a set of newopportunities. These developments were backed by continuing new lab measurements of electricalconductivity for mantle minerals at temperatures and pressures that are closer to the actual conditions
expected in the mantle. The latter enabled more accurate quantitative estimates of water content andtemperature in the mantle. In parallel, crust and mantle conductivity models along with developed modelling
techniques have become an integral part of geomagnetic field and geomagnetically induced current (GIC)modelling workflows.
Keywords: global induction, mantle conductivity, inversion, water content

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Electromagnetic Measurements on Volcanic Islands
Duygu Kiyan
Dublin Institute for Advanced Studies, Ireland, duygu@cp.dias.ie

SUMMARY
Volcanic islands develop in the geodynamic context ranging from mid-ocean ridges (e.g., Azores Archipelago)
to intraplate (such as the Hawaiian Islands, Cabo Verde) and to volcanic arcs (e.g., the Aeolian Islands, theAntilles). They are one of the most vulnerable environments on Earth not only because of their high exposure
to the impact of multi-hazards (e.g., volcanic eruptions, landslides, tsunamis etc.) but also because of theirisolation from the mainland. Many volcanic islands are densely inhabited, hence given the growing populationand changing climate, the need to identify, monitor, and mitigate geohazards has been increasingly importantto protect the public. Additionally, identifying and de-risking natural resources such as geothermal energy andfreshwater are also essential to economies and societies. This review provides an overview of electromagnetic
studies aimed at understanding and characterising the activity, dynamics and hazards of volcanoes on volcanicislands. These studies include passive and active electromagnetic data acquisition in the atmospheric,terrestrial, and marine (i.e., near-shore) realms. The review also addresses challenges related to survey
planning, data acquisition, modelling, and interpretation. The particular challenges posed on volcanic islands
include the following:
1. A potentially complicated internal structure of the volcanic edifice, which usually extends spatiallybelow the ocean. Deep investigations thus will require marine measurements.
2. Depending on different tectonic and environmental settings, the subsurface can be extremely
heterogeneous. The diversity of rocks present in and around the edifice is large, and often displays
the work of multiple episodes of growth and collapse.
3. The presence of the ocean, where the near-shore bathymetry is often not well known, as well as
the distribution of high-porosity sediments around the island, which are derived from the growth andcollapse of the volcanic edifice.
4. Volcanic islands often have strong and steep topography, which poses not only considerable
logistical problems, but also challenges for the model discretisation.
5. The ubiquitous presence of hydrothermal systems, which represent geochemically aggressive
environments, and have the potential of interacting with the original rock, leading to considerable
volumes of high-conductivity minerals and additional secondary heterogeneity.
Keywords: Electromagnetic induction, Electrical conductivity, Volcanoes, Volcanic islands

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Electromagnetic Modeling Using Adaptive Grids - A Reflection on the Term Geometry
Klaus Spitzer
Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Germany
klaus.spitzer@geophysik.tu-freiberg.de

S UMMARY
The goal of electromagnetic (EM) modeling is to find mathematical and physical models that represent ‘reality’as well as possible and allow us to predict and understand the behavior of EM fields in matter-filled space.
Inevitably, these models are always an abstract and incomplete representation of reality. They therefore containsources of error and are subject to a limited range of validity. Particularly, the geometry of our physical models
in the Earth sciences is extremely challenging due to its multi-scale nature.
On a small scale, we may end up in microscopic, possibly self-similar structures where non-inductive polarization effects are not covered anymore by our chosen mathematical model due to a variety of pore-scale andinner-surface electrochemical processes. Electric conductivity can become complex-valued, frequency dependent and non-linear. Moreover, texture or heterogeneity below a certain spatial scale can appear as anisotropy,
leading to a matrix representation of electromagnetic parameters in the equations to be discretized. Much ofthis is subject to current petrophysical research, which gives us new insights into the fundamental propertiesof these parameters.
On a large scale, and this is actually the main focus of my talk, there are sources of error associated withthe representation of macroscopic geometry and its discretization. Whereas geometry modeling started withhomogeneous and layered halfspaces with only a handful of parameters in the first half of the 20th century, wewere able to approximate 2D and 3D geometries in the 70s, 80s, and 90s using, in the end, millions of rectangular building blocks to construct geometric models in a brick-like and conformal fashion. This is simple and easy
to handle but sometimes also inefficient, particularly when it comes to adopting to the geometric idiosyncrasies
of our geo-reality. Staircase-like structures associated with rectangular Cartesian grids tend to introduce artifacts into the model response once the configuration of sources and receivers generates sensitivity towards
the artificial geometric features. Therefore, more elegant ways of representing geometry evolved during thelast 20 years incorporating unstructured and non-conforming grids or, recently, even meshless approaches.
I will focus on the numerical and computational side of modeling here and restrict myself to the classical scalarelectromagnetic parameter set taking into account that computers will continue to expand our capabilities in the
future. Since there are no closed solutions to Maxwell’s equations with spatially arbitrarily varying coefficients,
discrete solutions inevitably imply approximation errors whose magnitude is generally difficult to assess. A
priori and a posteriori error estimators have provided us with some means to quantify the accuracy of thecomputed solution or, at least, allow us to tailor the properties of the used discretization to a desired level.
Moreover, we realize that the description of the geometry itself, i.e., before we start discretizing it, represents
a major problem. We need tools to disassemble the computational domain into ‘water-tight’ subdomains with
arbitrary shapes. Large-scale structures, like underground voids, boreholes, bathymetry, or the topography of
the Earth’s surface can significantly influence the electromagnetic response within a specific frequency range
or time interval. Ultimately, this means we have to deal with a multi-scale issue which requires an appropriate
level of geometric detailedness and a flexible local refinement or coarsening to account for the true physicalresponse once a reliable mathematical model has been chosen. It turns out that the sensitivity plays animportant role to steer and direct the degree of resolution of meshes and geometry.
Due to brevity, the talk will not address the inverse problem and efforts to quantify uncertainty in a Bayesian
sense. The talk much more tries to review some attempts to gain control over the error sources in the forward

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Spitzer, K., 2022, Electromagnetic Modeling Using Adaptive Grids

process, particularly with respect to error estimators defined within the theory of finite elements. I will briefly
review the different types of finite elements used in our approaches to model vector electromagnetic fields andscalar potentials, and discuss the types of meshes underlying the discretization schemes with respect to theirability to represent arbitrary geometry.
Keywords: Finite elements, unstructured grids, error estimators, geometry modeling, level of detailedness,
adaptivity

25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Electromagnetic studies in the Eastern Mediterranean Region with Special
Reference to Major Transform (Strike-Slip) Faults
S.B.Tank 1
1Boğaziçi University, Kandilli Observatory and Earthquake Research Institute, bulent.tank@boun.edu.tr

SUMMARY
Even though extensively researched in the history of geosciences, human knowledge on the earthquake
generation is still severely limited. This is primarily due to the complicated behavior of the medium, namely theseismogenic zone, where the earthquakes originate and variety of the numerous physical parameters usedwhile acknowledging the earthquake phenomena. Leastways, our knowledge today reached to a certain levelthat allows us to affirm the fact that before, during and after earthquake processes are governed by theexistence of sub-surface fluids (and/or geofluids) in and around the major fault zones. Depending mostly on
fault’s structural properties as well as its focal mechanism type, the geofluids present at the fault zone are held
responsible for the occurrence of major and destructive earthquakes with their mechanical influence undervarious physicochemical conditions resulting in the weakening and lubricating effects. Thus, to comprehend
the characteristics of the seismogenesis, imaging the geometry of fluid-sheltering fault structures,
understanding the vivacity of the geofluids under varying stress conditions and determining the amounts of thegeofluids have become indispensable apparatuses for the illumination of the earthquake related phenomena.
In this regard, together with some other fluid-sensitive geophysical and geological methods, the major fault
zones worldwide are being examined with both time- and frequency-domain electromagnetic (EM) methods
such as transient electromagnetics and magnetotellurics. Since 60s/70s when the earliest EM studies
performed around the fault zones brought elementary results, there has been a tremendous amount of
improvements in this field. However, the evolution of the equipment, the observation techniques and themodeling schemes brought further complexities. Particularly, the ongoing transition from the two-dimensional
interpretation to the three-dimensional requires additional precautions and circumspections.
This narrative review attempts to shed some light on the pros and cons of utilizing some of these EM methods
while investigating the structure and dynamic properties of the strike-slip fault zones in the Eastern
Mediterranean region. The review begins with introducing the structural, rheological and electrical propertiesof the strike-slip faults in general. A historical perspective and global distribution of the EM studies on strikeslip faults will be given. Next, the tectonic, geodynamic and kinematic properties of the Eastern Mediterranean
region will be presented. The remaining part of the review highlights several systematic studies performed atand around the major strike-slip faults of the Eastern Mediterranean Region, namely, the Dead Sea Transform
Fault, the East Anatolian Fault and the North Anatolian Fault.
Keywords: Strike-slip Faults, Fluids, Eastern Mediterranean

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

Electromagnetic technology for prospecting unconventional hydrocarbon resources
Liangjun Yan1,2,3
Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of
Education, China, 430100. yljemlab@163.com
2
Cooperative innovation center of Unconventional oil and gas, Hubei, China
3
Key Laboratory of Geophysical prospecting, CNPC

1

SUMMARY
Unconventional oil and gas (normally tight gas and oil, shale gas and oil, coal seam gas, natural gas hydrate,etc.), which generally have the characteristics of source-reservoir symbiosis, are widely distributed. Still, theirhigh-quality reservoirs have large burial depths, small targets, complex electrical properties, and considerable
inhomogeneity, making their a great challenge for electromagnetic (EM) exploration to detect them. In recent
decade, A series of achievements have been made in the field of EM exploration of unconventional oil and gas
worldwide, including the EM response mechanism of unconventional reservoir rocks, new methods andtechnologies in controlled-source EM exploration on land, identification and evaluation methods for oil and gas
using EM parameters. These technologies have been successfully applied in unconventional oil and gas
exploration and development with good effect and have been recognized by petroleum geology anddevelopment circles. Firstly, this paper introduced the complex resistivity characteristics of organic-rich shale,tight sandstone, and dolomite in southern China occurring in low porosity and permeability reservoirs, anddiscovered that organic-rich shale has the characteristics of low resistivity and high polarization. At next, an IP
model for the shale reservoirs is established. Based on the mechanism of source-reservoir symbiosis in shale
reservoirs, the identification mode for the sweet spot is proposed. It is then proved this paper that there exist
a good petrophysics foundation for the EM exploration in the field of shale gas exploration and development.
Secondly, there has been a research focus on how to use the multiple IP parameters, such as resistivity andpolarizability, to estimate the characteristic parameters of the sweet spot of reservoir. The prediction method
based on IP model for the parameters of reservoir is introduced. Thirdly, there is an outspring of the controlledsource EM exploration methods for unconventional oil and gas exploration, such as Wide Field
Electromagnetic Method (WFEM), Wire-less Electromagnetic Method (WEM), Time-Frequency
Electromagnetic Method (TFEM), Long Offset and Window Transient Electromagnetic Method (LowTEM) andFocused Source Electromagnetic Method (FSEM). These methods and technologies share a common featureof using a long wire source, with high-power, large current multi-waveform transmission, multi-component arrayacquisition and hybrid processing and inversion. Therefore, not only the signal-to-noise ratio, exploration depth,
resolution and reliability, but also the efficiency, resolution, cost and adaptability have been significantly
improved, making these methods able to deal with the geology problems in the exploration and developmentof unconventional oil and gas under complex conditions. Finally, several cases are given to indicate theapparent application effects of the new methods and technologies of controlled-source EM method in
unconventional oil and gas exploration, sweet spot detection, fluid identification, fracturing monitoring, and at
the same time, look into the broad application prospect of EM methods in the exploration and development of
unconventional oil and gas. However, there are still plenty of unsuccessful cases. It is a long way to go in theeffective application of EM methods in unconventional oil and gas exploration and development. Therefore,
more achievements are expected to be made, especially on the EM response mechanism of unconventional
oil and gas reservoirs, 3D high spatial and temporal density data collection technology under complex
geological and topographical conditions, fast, stable and reliable high-precision inversion and imaging methods
with constraints based on prior logging and seismic data, reservoir parameters prediction method based onrefined IP model.
Due to the limitation of the author's ability and the paper’s length, the new methods, technologies, andapplication cases referred to in this paper are mainly from China.
Keywords: Unconventional oil and gas, Resistivity, Chargeability, Sweet spot testing, Controlled source EM

INTRODUCTION
Due to the greenhouse effect and environmental

concerns, human’s yearn for clean energy has been
inspired. However, the conventional oil and gas
resources shortage has been a global headache in

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022
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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

the Post-Petroleum Era, therefore, the exploitationand development of unconventional oil and gas
reserves are gaining more and more attentions
worldwide among governments and energy
companies. Unconventional oil and gas resources
including tight gas and oil, shale gas and oil, heavy
oil, coal seam gas, and natural gas hydrate, aresignificant parts of the global energy structure,
which occupy about 80% of the total oil and gas
reserves on the earth. The successful developmentof unconventional oil and gas resources, especiallyshale gas and tight sandstone gas in the UnitedStates (US) has enabled the country to become
self-sufficient in natural gas for the first time in thelast over 40 years. In 2009, the output of
unconventional natural gas in the US first exceededthat of conventional natural gas. Since then, the US
turned into an important shale gas exporter and the
supply pattern of the global natural gas market haschanged. China is the world's second-largest
energy consumer, and its unconventional oil andgas reserves is equivalent to the US. To meet thesocial needs and to reduce the energy dependency,
China has accelerated the research on
unconventional oil and gas related geologicaltheories, exploration technologies, development
methods and technical equipment. So far, many
advances have been made and huge achievements
have been achieved. With the continuous rising of
unconventional oil and gas output, the nationalenergy structure is considerably improved.
1.1 Geological and geophysical characteristics
of unconventional oil and gas reservoirs●
Geological characteristics
Unconventional oil and gas resources are different
kinds of oil/gas reserves that cannot be exploredand developed simply with regular technologies andmethods. Generally, the reservoir forming
conditions of unconventional oil and gas resources
are less demanding than those of conventional oil
and gas resources, thus, unconventional reservoirs
are more common and their occurrence modes are
more diverse, Unconventional reservoirs possess
the following geological characteristics (Zou, 2015):(a) source-reservoir symbiosis;(b) large distribution area, deep burial depth, andblurred boundaries;(c) tight, poor physical properties, low porosity, lowpermeability, extensive nanoscale cracks, andstrong heterogeneity;(d) lithological diversity (sandstone, limestone,
shale, coal and migmatite, etc.), small effective
reservoir;(e) rich in organic matter, high maturity, poor phasesegregation, no unified boundaries of oil, gas, andwater.
●
Geophysical characteristics
Reservoir rocks containing oil and gas usuallypossess the characteristics of high resistivity andhigh polarization (Zonge,1975; Lima and Sharma,
1992; Sigel et al., 2007; Davydycheva et al., 2006;
Burtman, 2015; Hu 2022). He and Wang (2007)
proposed the ‘Ring three storey’ model for oil andgas identification based on EM and IP parameters.
They confirmed that oil-gas reservoirs were of highresistivity and high polarization, while the waterbearing reservoirs were generally of low resistivity
and high polarization. Based on the spatial
resistivity and polarization differences, oil, gas andwater could be easily and effectively identified.
With this model, numerous favorable geologicalresults have been obtained by applying the EM
method in oil and gas exploration (He, 2005;
Davydycheva, 2006; He, 2010). However, thedifferences in geological characteristics make theelectrical features of unconventional reservoirs
noticeably different. Unconventional reservoirs aremainly shale, mud shale, tight sandstone, andmudstone, which present the features of low
resistance, low permeability and low polarization. A
great quantity of petrophysical experiments
indicated that shale gas reservoirs in southern
China generally demonstrated the characteristics oflow density, low speed, high resistance, and low
magnetism (Wang, 2015). By conducting
substantive complex resistivity measurements, Yanet al. (2014) and Xiang et al. (2014) found that richorganic shale in southern China had thecharacteristics of low resistivity and highpolarization. Similar results were obtained by
Burtman (2014) using TerraTek shale rock samples.
Similarly, changes in electrical characteristics arealso noticeable during thermal flooding andhydraulic fracturing process. For example, when
SAGD technique is applied, steam chambers andpossible steam channeling-paths may form, andphase-transformation zones would form surroundthe steam-injection wells. The resistivity variations
within different phase-transformation zones canprovide physical-property foundation to identify thesteam-flooding front. (Yang et al. 2005; Hu, 2022).
As for hydraulic fracturing in shale gas reservoir, lowresistance and high polarization anomalous body
would form when thousands of tons of fracturing
fluid are injected into the reservoir (Chen, 2000).
Therefore, EM method can be used to monitor the
spatial distribution of fracturing fluid and shed lighton fracturing optimization.
1.2 Exploration features of unconventional oil
and gasUnconventional reservoirs demonstrate different
source, lithology and physical properties, as well ashydrocarbon
conditions
fromconventional
reservoirs, thus, the exploration methods,
development and evaluation techniques can betotally different. To explore the unconventional oil

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and gas reservoirs, there are three stages: firstly,
screen out the core area based on the high-quality
reservoir evaluation criteria; secondly, identify the‘Sweet Spots Area (Segment)’ based on thereservoir integral/local tectonic characteristics, andfault and microfracture features; finally, drilling,
production and ‘Sweet Spots Area (Segment)’
evaluation. Compared with conventional oil and gas,
there are three changes in unconventional
reservoirs exploration (Zou, 2015), one is from‘finding oil in trap’ to ‘finding oil in the strata’, second
is from ‘oil/gas reservoir’ to ‘oil/gas layer’, and thirdis from ‘Sweet Spot’ to ‘Sweet Spot Area (Segment)’.
The ‘Sweet Spot Area (Segment)’ of unconventional
reservoirs refers to as the source-reservoir
symbiosis development area, which is an
unconventional hydrocarbon enrichment area withlarge distribution range, specific thickness, highquality source rock, good physical properties, highoil/gas saturation, high formation energy (i.e., highgas-oil ratio, high formation pressure), highbrittleness index, rich fracture and local structuredeveloped. Evaluation and optimization of “Sweet
Spots Area (Segment)” is the key of unconventional
oil and gas exploration and development, whichruns through the whole exploration anddevelopment process. The evaluation parametersslightly vary with different unconventional reservoirs.
The main evaluation parameters include TOC,
porosity, the development degree of microfractures,
brittleness, range (burial depth and distributionarea), etc. (Zou et al., 2015; Yang, 2019). To
improve the economic recovery efficiency of
unconventional reservoir, it is necessary to improvereservoir permeability or fluid viscosity by horizontal
drilling, fracturing, and other techniques toguarantee the continuous or quasi continuous
accumulation of oil/gas resources. According to thespecific geological problems and electricalcharacteristics in the three stages of unconventional
oil and gas exploration, EM exploration technology
can give full play to the advantages to achieve theexploration, detection and evaluation of sweet spots.
1.3 Opportunities and challenges
exploration technologies

ofEM

Due to the limitation of inherent method and theory,
EM exploration is far less important than seismic
exploration in oil and gas exploration, and only
applied in the early stage of oil and gas exploration
in basin survey. In unconventional reservoirs, thedifference of seismic wave impedance between oil,gas and water is small, which makes seismic
exploration difficult (Yuan, 2013). However, thedifference of resistivity and dielectric constant
between oil, gas and water is very large, andelectromagnetic method has superior conditions for
fluid identification and sweet spot detection (Yan,

2014). The high-power EM method can capture thehigh-resolution anomaly field distribution caused byresistivity and polarizability differences. When
combined with seismic exploration methods, thedistribution of ‘Sweet Spots Area (Segment)’,
residual oil distribution, spatial distribution of steam
or water injected can be elaborately described, andthe reservoir permeability and oil/gas saturation canbe accurately predicted. With the development of
unconventional and ultra-deep oil and gas
exploration, the cost of seismic exploration isincreasing and the exploration is becoming more
and more difficult. Electromagnetic exploration
methods are facing great opportunities as well as
severe challenges.
⚫ Inherent limitations of EM methods
The essential difference between EM and seismic
methods lies indifferent field properties. Seismic
wave field belongs to the wave field, whileelectromagnetic field belongs to the diffusion field.
Low-frequency EM methods are generally used foroil and gas exploration, which can be easily affectedby volume effect, static offset, field source effect,
overprint effect, shadow effect, etc. Fortunately, thedevelopment of high-precision 3D EM modelingtechnique has provided an effective tool to mitigate
these adverse effects. For instance, the joint
inversion of multiple field sources can effectively
suppress the field source effect (He, 2019).
Fictitious wave transformation for transient
electromagnetic field improves the interpretation
reliability and resolution (Chen, 1999; Li, 2005;
Mittet, 2015, 2018; Stoffa et al. 2018). Full domain,full coverage, full waveform field, and uniform
illumination three-dimensional EM data acquisitionand processing, special wave field transformationand inversion imaging techniques are the keys toovercome the limitations of the EM exploration
methods.
●
EM response mechanism of reservoir rocks
EM response mechanism is closely related to thereservoir physical properties. The simplified
resistivity model based EM methods is now facing
great challenges to solve complicate geologicalproblems, and the induced polarization (IP)mechanism of reservoir rocks should be valued( Kavian, 2012; Fiandaca, 2012). The time andfrequency dispersion characteristics caused by thefluids and minerals in reservoir should becomprehensively considered and studied (Burtman,
2014, 2015). Meanwhile, the physical and structuralcharacteristics
oftheconventional
andunconventional oil and gas reservoirs should be
distinguished, and an equivalent complex resistivity
model comprehensively considering EM induction,
IP and other effects should be established (Zhdanov,2008). Moreover, the foundation of controllable
source electromagnetic (CSEM) technology shouldbe established based on the IP mechanism ofreservoir rocks. Only by fully utilizing the sensitivity

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methodsof the IP parameters in complex resistivity model
can the purpose of precise multi-parameter joint
detection of oil and gas be realized. Therefore, it isessential to study the IP mechanism and anomaly
extraction method of unconventional oil and gas
reservoirs to improve the detection accuracy andthe interpretation reliability. It is an inevitable trend
in the development of EM exploration to seek IP
multi-parameters based CSEM method.
●
EM data acquisition
The effectiveness of exploration highly relies on thespace-time density and the quality of EM data, anddata acquisition technique is the prerequisite andthe guarantee for the development of EM
exploration methods. Acquisition technology
requires a deep combination of methods,
instruments, acquisition software, field geology andterrain conditions, geological targets and otherfactors, through feasibility analysis and simulation
test, to develop an effective acquisition scheme.
Firstly, In the acquisition method, it is necessary toachieve a substantial breakthrough in 3D
acquisition, realize multi-field source coverage anduniform illumination, and synchronize acquisition
with hundreds of thousands of instruments to record
massive electromagnetic multi-component timeseries data, so as to meet the requirements of highspatial and temporal density data for high precision
inversion. Secondly, high-quality acquisition
software capable to conduct noise level analysis,
feasibility analysis and data quality evaluation
should be developed to improve the exploration
efficiency. Finally, portable, intelligent, low-cost, lowpower and low-noise EM multichannel receivers
should also be manufactured to simplify theacquisition process.
●EM data processing
Data processing is significant part of the successful
application of EM methods. Without a proper dataprocessing technique, no matter how sensitive theinstrument, how advanced the data collection
techniques, and how high the quality of data, theresults obtained will be worthless. Data processing
mainly include data preprocessing, attributeparameter definition and extraction. Denoising andcorrecting is the first step in the data processing.
Due to the cultural noise and the signal distortion
caused by the method or the geological andtopographical factors, targeted de-noising andcorrection methods are required. Many modern
signal processing methods have been adapted todata de-noising, such as the robust estimation,
median filtering, wavelet transform, recursive flood
wave, and coherent analysis methods (Kass & Li
2011; Streich, 2011; Ji et al. 2016; Rasmussen et al.
2017; Wu, 2021). In contrast, the correction
methods, such as the field source correction andstatic shift correction, are developed based on thecharacteristics of the methods. Because of thecomplexity of EM methods and noise, there is nouniversal correction and denoising techniques.
Attribute parameter extraction is guaranteed to
improve the reliability and effect of the inversion andinterpretation. Conventional method is to define theapparent resistivity parameter. However, thecomplicated multidimensional source creates great
difficulties in defining the apparent resistivity. Thus,
using different field components or combinations ofmethods to define the attribute parameters bynumerical calculations, such as the all-timeapparent resistivity and all-time vertical conductivity,
oil-water
identification
factor,differentiallynormalized quantities, has attracted researchinterest, and several good application results havebeen achieved (Yan, 1999; Davydycheva et al.,2006; He, 2015; Xue, 2020; He, 2021).
●
EM inversion and interpretation methods
Theoretically, all geophysical inversions areunderdetermined problems. It is difficult to infer the
information about the abnormal undergroundtargets accurately from a single type of geophysicaldata. Comprehensive inversion and interpretation
by multiple geophysical datasets are becoming asignificant research trend. Joint inversion using as
many types of prior information as possible is aneffective means of reducing the multiplicity of
inversion, improving the data utilization, andmodifying the inconsistency of the inversion model.
Seismic information is relatively abundant in oil andgas exploration, and the method of joint inversion of
EM and seismic data has attracted researchattention and achieved better specific results (Pengand Liu, 2020, He, 2020). In unconventional oil andgas exploration, EM signals are sensitive to thesweet spot, and the electrical properties are
consistent with the characteristic parameters, suchas the porosity, brittleness, permeability, and TOC.
With the constraints provided by the reservoir
information obtained from the seismic data, theinversion resolution and interpretation reliability canbe improved using EM data to predict the reservoir
parameters. Furthermore, this method can reduce
the risk of exploration and development.
There are two types of joint inversion: one type isbased on petrophysical relationships and the otheris based on structural similarity relationships (Pengand Liu, 2020). In oil and gas exploration anddevelopment,
petrophysicalandgeologicalinformation can be acquired as the prior information.
Integrating the prior information, such as
petrophysical and geological data, into the inversion
can reduce the non-uniqueness of the optimization
problems and help obtain proper inversion results(Giraud et al., 2017; Astic and Oldenburg, 2019).
Joint inversion based on the empirical relationships
among the various geophysical parameters has
undergone vigorous development (Jegen et al.,2009; Heincke et al., 2010; Lelievre et al., 2012),
and the petrophysical empirical or statistical formulacan be applied in industry. Via the intersection

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

analysis of the P-wave velocity and resistivity, Jegenet al. (2009) obtained the sectional empirical
relationship between the P-wave velocity andresistivity. They confirmed that the empirical
relationship can be locally effective under a specificgeological background. Xu et al. (2016) and Peng etal. (2018) created marine EM and seismic data joint
inversion frameworks based on the relationshipbetween the reservoir and petrophysics and using
the Archie formula and Gassmann formula,respectively.
They
both
achieved
globaloptimization one-dimensional joint inversion andextracted the reservoir porosity and saturation
information. Hu et al. (2020) utilized the priorinformation obtained from the logging and seismic
profile to perform constraint modeling, and theyrealized the artificial fish swarm joint inversion using
the magnetotelluric (MT) and gravity data. Yang etal. (2021) proposed the petrophysical relationshipbetween the velocity and resistivity based on the
cross-variation function, and then, they adopted theguided fuzzy c-means clustering algorithm to carryout the multi-constraint inversion and completed thetwo-dimensional MT and seismic data joint
inversion. However, there is still no standard
petrophysical model that can link all of the physicalparameters. Therefore, it is difficult to the directly
integrate the petrophysical or other geologicalfeatures into a standard geophysical inversion(Astic and Oldenburg, 2019).
Compared with the constrained inversion based on
the empirical petrophysical relationship, the joint
inversion built via structural coupling requires lessprior information. Haber and Oldenburg (1997) first
proposed the concept of structural coupling
constraint inversion, and Gallando and Meju (2003
and 2004) subsequently proposed the crossgradient inversion method. Multiple parameters joint
inversion based on spatial structure similarity has
gradually become the mainstream algorithm. Hu etal. (2009) applied the cross-gradient algorithm
technique to joint inversion of two-dimensional
seismic and EM data with multiplicative
regularization and showed that the reservoir
evaluation based on the joint inversion was better
than that obtained through imaging interpretation of
the individual seismic data. Zhdanov (2012)
proposed a universal spatial structure constraint
algorithm named Gramian spatial constraint method.
It has many choices of operators for Gramian space;
and when the operator is transformed into a
gradient type, the cross-gradient method is obtained.
Lelievre and Farquharson (2013) obtained a linear
variation expression of the local model parameters
and a solution for the change rate in all directions
using the construction method, constructed
Gramian spatial constraint operator using theobtained gradient of the model parameters, andfinally achieved joint inversion of multiple datasets.
Gao et al. (2017) proposed a cross-gradient joint

inversion strategy using alternately updating model
parameters, which reduced the complexity ofalgorithm. Yan et al. (2020) derived the threedimensional (3D) discrete form of the cross-gradient
operator and realized 3D joint inversion of gravity,
magnetic, and MT data. Currently, the joint inversionof seismic and EM data is based on the
petrophysical information constraints. Research on
the structural coupling algorithm has primarily
focused on joint inversion of gravity, magnetic, andEM data, and structural coupling joint inversion
using seismic and EM data is the future goal in
unconventional oil and gas EM exploration.
1.4 Level and ability of the EM exploration
methodThe EM method is an effective means of resourceand energy exploration. The physical property
foundation is the differences of the resistivity,
dielectric constant, and polarization in rocks. The
detection of underground geological targets can berealized by observing the spatial-temporal
distribution of the EM fields. Although EM method
has a good electrical basis in unconventional oil andgas exploration, the sweet spots of unconventional
oil and gas reservoirs feature large burial depth,
small target and strong heterogeneity, which require
higher resolution, exploration depth and level ofdata
processing
andinterpretationofelectromagnetic exploration method. In the last 20
years of development, the methods of petroleum
EM exploration have been comprehensively
improved. In terms of the instruments, geophysicalinstrument companies such as Phoenix, Zong, KMS,
Metronix, and Geonics have developed various
types of high-power and strong current transmitting
systems and multi-functional networked portable
receivers. The wide frequency electromagnetic
instrument developed by China's Jishan High-techcompany,thehigh-power
time-frequency
electromagnetic system developed by BGP, CNPC,
and the ultra-high power and ultra-low frequency
transmitting and receiver developed by the Instituteof Geophysics, Chinese Academy of Sciences,have also been put into oil and gas exploration. The
Mag-power transmitter through a few kilometers todozens of kilometers long ground wire to feed
hundreds of amperes of current underground, canbe convenient to emit a variety of waveform, for thetime domain and frequency domain electromagnetic
method to provide a powerful source ofelectromagnetic field. Low power consumption,broadband, low noise level, large dynamic range,
portable, light weight, multi-function, distributed
receivers can realize the data acquisition to
thousands of channels array, which provided a
hardware guarantee for 3D EM data acquisition andhydraulic fracturing monitoring under complex
geology and terrain conditions. Lots of new control

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

source EM methods were emerging to improve theresolution, detection depth, and interpretation
reliability, such as the Wide Frequency EM method(WFEM), Wire-less EM method (WEM), Timefrequency EM method (TFEM), Long offset, long
window Transient EM method (LowTEM), andFocused Source EM method (FSEM). Thesemethods have greatly improved the anti-noise ability,
detection depth, resolution, reliability, work
efficiency, cost, and adaptability. The use of new
data processing, 3D inversion and interpretation
techniques had significantly enhanced the ability ofelectromagnetic methods to serve unconventional
oil and gas exploration. At the same time, the studyof unconventional reservoir rocks physics of has
been deepening, and the induced polarization
mechanism of unconventional reservoirs has madenew progress. The model that accurately describes
the IP mechanism of unconventional reservoirs has
been preliminarily established, and the parameter
prediction method of unconventional reservoirs
based on this model has also been basically formed(Tong, 2020; Burtman, 2015).
Lots of reviewers have summarized the EM
methods and technologies in oil and gas explorationand development (He, 2010,2019; Strack,
2013,2014;Tietze 2014; Streich 2015; Di 2019,2020; Xue 2016, 2020; Constable, 2010; Liu 2021).
This paper focuses on the study of electricalcharacteristics of unconventional reservoir, thesweet spot detection and its characteristic
parameters prediction methods based on the IPmodel, as well as the research progresses inregarding the new methods and technologies in EM
exploration. In addition, several cases studies of
shale gas the sweet spot detection and hydraulicfracturing monitoring with LowTEM were presented.
The summary of the cases studies will benefit thewide application of new EM methods andtechnologies in unconventional oil and gas
exploration and development.
2. Sweet spot detection and its characterparameters prediction
The detection and evaluation technology of shale
gas sweet spot is the key to shale gas explorationand development. Seismic exploration method
undoubtedly plays an important role in sweet spot
detection and development because of their highresolution and attribute identification capability.
However, most of the shale gas exploration areas insouthern China are rugged areas, and covered bylimestone, seismic exploration is much more
expensive and difficult to obtain high-quality data. In
addition, the sweet spots are distributed inargillaceous source rocks with deep burial depth,
high degree of evolution and rich organic matter.
The seismic impedance characteristics are notobvious and there are no clear trap characteristics.

The shale reservoir itself is a source rock withextremely low porosity and permeability, and strong
heterogeneity. As a result, the seismic
characteristics of the sweet spot are not obvious,
which makes prediction difficult. Therefore, thedevelopment of high-precision seismic prediction
methods for shale gas reservoirs faces greatchallenges, and the development of cost-effective
non-seismic methods is an effective way to detectsweet spot in unconventional reservoirs.
As the most effective supplementary means ofseismic exploration, the electromagnetic (EM)exploration method has the advantages of large
exploration depth, high work efficiency, low cost andstrong adaptability, and has been playing animportant role in the early stage of basin oil and gas
exploration. The EM exploration of oil and gas
based on EM diffusion can only be effective when
the resistivity of the oil and gas reservoir is highenough compared with the surrounding rocks. The
importance of IP effect in rocks has been realized,
and two IP parameters (resistivity and chargeability)
have been tried to jointly detect oil and gas
reservoirs (Davydycheva, 2006; He, 2010; Commer,
2011). Complex resistivity (CR) method, which isbased on the IP theory, has been tried to identify
conventional oil and gas in China for more than 30
years (Wu, 1996; Xu,2004; Su et al., 2005), and two
kinds of IP anomaly models were put forward tointerpret the CR date. One considered that thereservoir itself was a dynamic multiphase balancesystem, in which the double electric layer formed
from bi-phase medium, and was the source in which
the IP field came from (He, 2007). Another wascalled ‘the micro-seepage model’ which considered
IP effect happened above the reservoir because thehydrocarbons migrated to the upper reducing zone,
and resulted in the formation of the rich pyrite halo
which was the source of strong IP effect (Veeken,2009).
Shale gas is stored in the reservoir in free oradsorbed state, and has the typical characteristics
of self-generation and self-storage and in-situ
accumulation, which inevitably determines thedifference in the IP anomaly model of datainterpretation in shale gas exploration. Are theabove two models suitable for shale gas exploration?
What are the geoelectric characteristics of organicrich shale? Does the shale gas reservoir have astrong IP effect? Can the resistivity andchargeability be integrated to identify the sweet spot?
What is the sweet spot detecting pattern for EM
exploration? Yan et al., (2014), Xiang (2014)
collected hundreds of shale samples in southern
China, performed compositional analysis, complex
resistivity and Total Organic Carbon (TOC)
measurements, and found a qualitative relationshipbetween pyrite content and TOC. On this basis,
combined with the IP theory study, the methods topredict the sweet spot’s characteristic parameters

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

and the detection pattern suitable for EM method
were proposed.
2.1 Electrical characteristics
southern China

of

shale

in

Polarizability(%)

Xiang et al. (2014) collected 243 geological outcrop
samples in Yibin, Sichuan and Zunyi, Guizhou insouthern China. The lithologies are mainly organicrich shale, sandstone, dolomite and basalt(surrounding rock formations). Through the complex
resistivity measurement and analysis, the IPparameters of the samples were obtained, and thephysical properties of each formation were shown in
Figure 1 and Table 1. Compared with thesurrounding sandstone, dolomite and basalt, theaverage resistivity of the organic-rich shale is about
120 Ω·m, and the average polarizability is about 20%
in Wufeng Group-Longmaxi Group; the average
resistivity of the organic-rich shale is about 147Ω·m, and the average polarizability is about 42% in
the Qiongzhusi Group. These indicated that theorganic-rich shale exhibited the characteristics of
low resistivity and high chargeability, which wasinconsistent
with
the
low
chargeability
characteristics of conventional shale reservoirs.

Organic-rich shale is abundant in Zhaotong, Yunnan,with shallow burial depth and large thickness. China
National Petroleum Corporation (CNPC) has madebreakthroughs in shale gas exploration in this area.
The Well Z104 has successfully produced industrial
gas, with a daily gas production of 20,000 cubic
meters. The logging curves of Well Z104 (Figure 2)
showed that the shale gas reservoir had obvious
characteristics of low resistivity, low velocity andhigh gamma value. The mineral composition
analysis of 34 cores from the groups of S1l-O3w andЄ1n in Well Z104 showed that the average content
of quartz and feldspar was 30% to 60%, the averagecontent of carbonate minerals is generally less than
30% (except mud shale), the average content ofclay minerals is 10% to 35%, the content of pyrite is
generally high, with a maximum value of 20% andan average content of 5% (Figure 3).

Resistivity/ΩmFigure 2. Z104 Geology Histogram and Logging
CurvesFigure 1. Polarizability and resistivity results of CR
measurement for geological outcrop rocks at
different groups (top: chargeability; bottom:
resistivity)
2.2 Prediction of the sweet spot characteristic
parameters●

TOC predicting

Figure 3. The mineral composition in the shales
from S1 l-O3w and Є1n (red: pyrite)
The complex resistivity measurement and TOC
analysis were performed on 11 cores from Well
Z104. Figure 4 indicated that the chargeability was

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

H 2 S + Fe2+ + 2OH − → FeS2 + H 2O

organic-rich shale, Xiang (2016) and Xu (2020)
gave an empirical relationship model between
polarizability and TOC, as shown in Figure 6.
1086TOC(%)

consistent with the variation of pyrite content, with ahigh positive correlation. Figure 5 presented a plotof TOC and pyrite content versus chargeability. The
results showed that there was also a goodcorrespondence between pyrite content and TOC.
Apparently, pyrite is an indicator mineral for IP, andis it also an indicator mineral for TOC? If therelationship between TOC and chargeability could
be established, it will have great significance for EM
exploration.
Why is pyrite abundant in organic-rich shales? Is it
necessarily related to TOC? A significant
phenomenon is that pyrite is not only acharacteristic mineral formed in organic-rich
deposits, but also a marker of sedimentary
environment and a characteristic mineral of strong
IP effect. Veeken (2009) explained its formation
process and controlling factors. The depositional
environment of shale was a deep-water anoxic
environment (reduction reaction environment),
coupled with its self-generation and self-storage
reservoir characteristics, the increase in TOC acted
as a catalyst, providing a chemical reaction
environment for the formation of secondary pyrite in
the shale clay, the chemical process was as follows
CH 4 + Ca 2+ + SO4 2− → CaCO3 + H 2 S  + H 2O

4
y = -13.048x2 + 21.038x - 1.2677
R²= 0.888

2
0

0.0

betweenpyrite

and

0.2

0.3

0.4 0.5 0.6
Chargeability

0.7

0.8

0.9

1.0

Figure 6. The Cross-plot of TOC and Polarizability●
Prediction of brittleness index
The mineral composition of shale reflected thedepositional
environment
anddepositional
conditions of shale, and also determines thebrittleness index of shale, which affects thefracturing and development of shale gas. It is ofgreat significance to study the relationship between
brittleness index and electrical parameters from theperspective of shale mineral composition. Quartz
content is the main reason for the shale’s brittleness,but the roles of other important brittle minerals
cannot be ignored. There were many definitions ofbrittleness index. Xu et al. (2020) defined thebrittleness index B as follows:
B=

Figure 4. Relationshippolarizability

0.1

quartz + feldspar +Pyrite
quartz + feldspar + Pyrite+carbonate + clay

(1)

The above formula highlighted the relationshipbetween pyrite content, brittleness index andelectrical parameters (resistivity and polarizability).
The brittleness of Well Z104 shale was statisticallyanalyzed by formula (1), and the relationshipbetween the brittleness index and the polarizability
was established, as shown in Figure 7. It can beseen that the quadratic nonlinear relationship was
obvious100

Brittleness Index(%)

B = 18.772ln(m) + 81.373

Figure 5. Relationship between TOC and pyrite
In addition, biodegradation and bacterial activity
might give rise to organic origin of pyrite in thesedimentary pile. It was proved empirically thatsignificant enrichment in pyrite was often related tohydrocarbon occurrences at deeper levels.
Based on the resistivity, polarizability and TOC of

R²= 0.9229

80

60
40
20
0

0.0

0.2

0.4
0.6
Chargeability

0.8

1.0

Figure 7. The cross-plot of brittleness index andpolarizability●
Prediction of the permeability
Unconventional reservoirs usually have low porosity

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

and low permeability characteristics (porosity lessthan 10%, pore throat diameter less than 1 μm or
permeability less than 1 mD). In order to achievesustainable and effective oil and gas production, it
is necessary to combine horizontal well drilling,
formation fracturing and other technologies toincrease reservoir permeability or reduce fluid
viscosity (Zou et al., 2013).

reservoir prediction. Tong et al., (2020) extended
the theoretical relationship between macroscopic IP
parameters and microstructure in the GEMTIP, andestablished MGEMTIP model under the assumptionof isotropic spherical perturbation (Figure 8).
MGEMTIP：

e =

0 


N

1 

1+l 1 −

M0 
i

+1
l

 l =1 N

where M 0 = 1- 3 fl 2
l =1

-1

(2)

l = 9 0 fl  M 0 (4 l +2  0 )  l = rl  2 sl (2 l + 0 )  .  0 is the DC resistivity of the
backgroundFigure 8. The schematic diagram of the effectivemedium theory. A heterogeneous media contains
many different types of perturbed media, each ofwhich contains resistivity  l , effective radius rl , andsurface-polarizability coefficient  sl . The resistivityof heterogeneous medium is equivalent to the CR ofan effective medium through the effective-medium
approach. (Tong et al., 2020)
Combining theoretical and experimental analysis of
the electromagnetic characteristics of the ‘sweet
spot’ region, establishing the theoretical relationshipmodel between electromagnetic response andreservoir physical properties is the key to realize the
EM detection and quantitative evaluation of sweet
spot. Reservoir physical properties such as porosity,
saturation, and permeability are sensitive
parameters for reservoir IP characteristics (Slater,
2007). Archie (1942) gave the empirical relationshipbetween pure sandstone conductivity and formationwater conductivity, connected porosity andformation water saturation. However, the rockmineral composition and pore structure ofunconventional reservoirs with low porosity and low
permeability was complex, and the Archie’s
relationship was no longer applicable. The
polarization
effects (electric double layer
polarization, Maxwell-Wagner dielectric polarization,etc.) existing at the interface of different minerals
and pore fluids will cause the IP phenomenon ofreservoir in the electromagnetic exploration
frequency range. Pelton et al., (1978) first used theCole-Cole model to characterize rock IP
phenomena, and it was widely used in EM
exploration. Zhdanov (2008) proposed thegeneralized effective-medium theory of induced
polarization (GEMTIP) model based on strict
mathematical and physical equations. GEMTIP issuitable for IP problems in anisotropic andmultiphase media, and established a quantitative
relationship between model parameters and rockphysical properties, which is more conducive tomediumf l ,  l , rl ,  sl reseectively

correseond to the volume comeonent of the l tyeeeerturbed medium DC resistivity equivalentseherical radius and surface eolarization
earameters N is the total amount of eerturbed
medium the equivalent medium eolarizability isNt = l . hhe relationshie between  t and thel =1

eolarizabilitymof the Cole-Cole model ism = t (1 + t ) . GEMGhP

is consistent with theeolarization characteristics of discrete metallic
minerals (Wong 1979) and also exelains the higheolarization ehenomenon in which the GawellWagner eolarization shifts to low frequencies underhigh oil saturation condition (Burtman and Zhdanov2015).
hhe electrical Kozeny-Carman (K-C) equation
characterized the rock eorosity and tortuosity
through the formation factor F and characterized
the caeillary radiusr through the seecific surface

area S por . hhe eermeability relationshie can beexeressed asQ(3)c
F b S por
Where Q, b, c are the fitting earameters (Slaterk=2007).Since the seeeage in unconventional
reservoirs was dominated by the diffusion of nonDarcy flow the validity of the electrical K-C equation
was greatly reduced in low eermeability rocks. Ganystudies eroeosed to use comelex resistivity toestimate thereby imeroving the erediction accuracyof eermeability (Börner et al. 1996; de Lima andNiwas 2000; Slater and Lesmes 2002). Figure 9
showed the eermeability erediction relationshie
based on normalized eolarizability (Weller et al.
2015) but there was a large error in the erediction
results of low eermeability rocks. Figure 10 showedthat S por correlated with the imaginary conductivity  (which mainly controls the eolarizability strength)
but the quantitative relationshie needed to be further
determined in combination with clay minerals (Revilet al. 2013). Conductive minerals

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

such as clay and metals in unconventional
reservoirs usually do not earticieate in the seeeage
erocess but directly affect the rock P ehenomenon.

Figure 11. Relationship between relative
polarizability difference and permeability of tightrock

Figure 9. Permeability relationship between
experimental measurement and electrical K-C
model estimation based on normalized polarizabilitymn ( mn = m 0 )(Weller et al., 2015)

Figure 12. Permeability relationship between
experimental
measurement
and
MGEMTIPestimation based on polarizability difference

Figure 10. Experimental relationship between theimaginary conductivity and specific surface area
between sandstone and clayey sandstone (Revil etal., 2013)
Whether conductive minerals actually eroduce
conductance or eolarization can be effectively
analyzed by combining the mineral size
connectivity and frequency band (Vinegar andWaxman 1984; Xiang et al. 2014; Revil et al.
2015). Numerical simulations showed that there
was a nonlinear effect between the eermeability offractured tight reservoirs (limestone) and the
conductivity of formation water (Kirkby et al. 2016).
hhe
electricalcharacteristics
ofthese
unconventional reservoirs seriously affect theeermeability erediction based on the electrical K-C
model. hhe existence of clay and minerals in
unconventional reservoirs makes it difficult todistinguish the P mechanism of unconventional
reservoirs. Removing or sueeressing eolarizationindeeendent of eore fluid can effectively imerove theerediction accuracy of eermeability. hhere is adifference between the theoretical eolarizability of
the GEMGhP (no seeeage) and the measured
eolarizability of tight rocks (weak seeeage). hhis
difference has a good correlation with the rockeermeability (Figure 11). hhe eermeability
erediction formula based on GEMGhP
waseroeosedQ(t -e )b Q b
k =
=
(4)
e c
e c
Where  t and  e correseond to the theoretical
eolarizability and measured eolarizability of the rockand Q, b, c are the rock characteristic indices. With

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

this model a good eermeability erediction for loweorosity and low-eermeability rocks containing lowresistivity minerals was achieved (Figure12).
2.3 Geo-electrical mode for sweet spot detection
Based on the complex resistivity test and analysis
of unconventional reservoir rocks, there were three
understandings: first, the excitation polarization ofsweet spot was mainly generated by electronconducting minerals such as pyrite, and its IP
mechanism is the electron polarization mechanism.
Second, the sweet spot region is characterized byhigh polarization and low resistivity. Third, the sweet
spot area, its IP anomaly and resistivity anomaly
overlap in space. Based on these three
understandings, the geo-electrical mode ofelectromagnetic exploration for sweet spot was put
forth(Yan, 2014；Figure 13). This mode provided atheoretical basis for the EM detection andcomprehensive interpretation of sweet spot.

line CSEM method has been generally applied in oil
and gas and deep structure exploration. The
horizontal electric field, the vertical magnetic
field/the induced electromotive force (EMF) are thetypical observed components. In terms ofinterpretation parameter definition, in addition to theuniformly conductive and non-polarized half-spacemodel, the uniformly conductive and polarized halfspace model and the earth-ionosphere model havealso been developed, as shown in figure 14.

Figure 13. Sketch map of geo-electrical mode forsweet spot detection
3. The innovations in CSEM on land
In recent years, based on the development ofmethods and instruments, especially that of the
CSAMT (Goldsten, 1975), CR (Zonge, 1975),
LOTEM (Strack, 1989), and MTEM(Hobbs, 2006), avariety of new CSEM methods with a significant
improvement in the adaptability, anti-interference
ability, exploration depth, inversion effect, andresolution ability have been developed. Thesemethods, including the TFEM, WEM, and FCEM,
LowTEM are currently being applied in
unconventional oil and gas exploration anddevelopment, and they possess the following
innovations.

Figure 14. (a) conductive non-polarized half-spacemodel; (b) conductive polarized half-space modeland (c) the earth-ionosphere model
For the uniformly conductive and non-polarized halfspace model, the time-domain electric andmagnetic fields of the electric dipole wereexpressed as follows(Piao, 1990):
Idl 
2 −u /2(5)
Ex =[(u ) −ue]
2 r 32

3.1 Definitions of EM property parameter●
Models and field components of the EM
property parameter definitions
Because the horizontal electrical dipole has better
attenuation characteristics in terms of offset andfrequency than the magnetic dipole, the grounded2
H z
Idl sin 2u2=[(u ) −u (1 + )e − u / 2 ]
4
t
0 r

3

(6)

the frequency-domain electric and magnetic fieldsof the electric dipole were expressed as follows:
Idl 
Ex =
[3cos 2  − 2 + (1 + ikr )e− kr ]
(7)
3
2 r

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

3Idl sin 
1[1 − eikr (1 + ikr − k 2 r 2 )](8)
2 4
2 k r3
where k = −i /  .
For the uniformly conductive and polarized halfspace model, the time-domain electric field of theelectric dipole can be written as follows(Davydycheva, 2006):
Idl   
2u − u 2 Ex (t ) =erf(u ) e 
3 2 r  
Hz = −

2 2 n +  e n u 1 − erf( ) u 
2nu  − u 2+  erf(u ) e
 


(9)

2 2
n


+ηe n u e 2n 1-erf( +u) u(1-2n+2n 2 )Where u =

0

2 t

r r is the offset t is the observation

 is the resistivity of the homogeneous half
seace n = 0.5r   .
time

For the earth-ionosphere model, the expressions of
the EM components of the ground source were as
follows (Li, 2015):
Idl F  J 0 ( r )d
4 0

k2
i Idl+ 2
cos 2   ( FF − 12 F )   2  J 0 ( r )d
0
k1 4


Ex = i

(10)


k2
i PE 1(1 − 2 cos 2  )  ( FF − 12 F )    J1 ( r )d20
k1 4 r
Idl  u1Hy =−F  J 0 ( r )d4 0 R1 (0)++*  R (0)
u 1 
Idl 1
(1 − 2 cos 2  )   − 1
FF + 1F     J1 ( r )d (11)04 r
R1 (0)  2 
 u1


P
R* (0)u 1+ E cos 2   (− 1
FF + 1F )   2  J 0 ( r )d04
u1
R1 (0)  2

where F and FF are related to the electric propertiesof the ionosphere layer (Li, 2015).
●
EM attribute parameters
Owing to the complicated relationships between thecomponents and the resistivity, polarizability inequations (5) - (11), the apparent resistivity andapparent polarizability can’t be defined like the DC
and MT method did, only by near field or far fieldapproximation can the expression of near or far fieldapparent resistivity be given. This imposes
restriction on the data collection area and creates
great difficulties in the EM data processing andinterpretation. In order to get effective attributeparameters the definition was mainly carried out
from two aspects: the whole area numericaldefinition and the way of complex resistivity methodand direct current method. Thus, new methods andtechniques of electromagnetic exploration withdifferent characteristics were produced.

Through further study on the numerical algorithms,
it has become possible to define the apparent
resistivity of the entire space using equations (5) –(11). Many scholars have proposed effective
methods of defining parameters based on numericalalgorithms. He (2010) defined the apparent
resistivity of the whole region based on Equation (7)and proposed the WFEM, which organically unified
the "near zone", "transient zone" and "far zone",
improved the data distortion in the transient zone,
and enabled electromagnetic sounding to be carriedout in a vast region without being limited to the farzone. In addition, this definition was also introduced
into the LOTEM, MTEM, and WEM, which has
resulted in several breakthroughs regarding thedata interpretation level and application effect(Yang,1986, Yan, 1999).
On the other hand, referring to the definition
methods of apparent resistivity, apparent
polarizability and charging rate in CR and DC to
directly define the electromagnetic property
parameters. According to the characteristics of thesimultaneous data acquisition in TFEM, the EM
attribute parameters, including the dual-frequency
amplitude, dual-frequency phase, triple-frequency
phase, and rate of change, were defined as follow:
A(i ) − A(i 3 )A(i ) =(12)
A(i )  (i1 ) − 
(i 3 )i2 (i ) = i 3(13)
i 3 − id (i ) 2 2 d 2 (i )(14)
3 (i ) =  (i ) − 
+ 
d
3
d 2
t
1
 (t )dt

t
t −t
(15)
M s = m m −1
0
m

m−1

Where i 3 is the 3rd

harmonic frequency of thefundamental frequency  i ; A and  reeresent theamelitude and ehase of the fundamental wave

reseectively;  0 reeresents the initial eotential t m
and tm-1 reeresent the mth and (m-1)th observation
time reseectively; the difference between the t m andtm-1 is the length of the mth observation time window; (t ) is the eotential decay curve of eotential withtime t.
The observation mode and the principle of the
FSEM were shown in Figure 15. Using equation (5),
Davydycheva (2006) defined four EM property
parameters using voltages observed at the center ofdual sources as follows:xU (t )x2U (t )t x2U (t )t x2U (t )
,
,
,
(16)




AB xU (t0 )AB xU (t )AB xU (t )AB t xU (t )WherexU (t ) = U x 2 (t ) − U x1 (t )xU 2 (t ) = (U x1 (t ) − 2U x1 (t ) + U x 3 (t )) t xU (t ) =  xU (t + t ) −  xU (t ) t x 2U (t ) =  x 2U (t + t ) −  x 2U (t )Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

It can be seen from Figure 15 that the FSEM has
the characteristic of current vertical focusing, which
improves the vertical resolution and the detection
depth, and effectively suppresses the interferenceby the inhomogeneous sub-surface targets.

waveform was generally adopted, and thus, thedata quality was significantly improved. Moreover,this ensured fast data processing and highresolution inversion and imaging. Figure 16 andFigure 17 showed the impulse response for the
homogeneous earth model obtained from the crosscorrelation between the observation field and thepseudo-random MT sequence, which wasconsistent with the theoretical modeling results.

Figure 16. the electric fields inspired by the pseudorandom M-sequence.

Figure 15. (a) The experimental setup withgrounded electrodes (the earth is gray); (b)the dc
current pattern for the transmitters, and (c) thereceiver. Arrows indicate directions of current flow.
The setup provides vertical current focusing.
(Davydycheva, 2006)
3.2 Application of the Pseudo-random technique
The auto-correlation of pseudo-random time series(such as M-series) satisfies
Rxx ( ) = K  ( ) .
(17)
The cross-correlation between signal and noise
satisfies
Rxn ( ) = 0(18)

Figure 17. the obtained earth impulse response andthe analytical solution.

Therefore, the cross-correlation between the earth
response and the system input (transmitting signal)isTs

Rxx ( ) =  g ( s)  Rxx ( − s)ds = K  g ( )

(19)

0

Equation (19) shows that due to the crosscorrelation between the wider transmission band Msequence and the observed EM field components,
it is easy to remove the noise and obtain the earth
impulse response with a high signal-to-noise ratio.
Using the apparent resistivity expression defined by
the impulse response of the homogeneous halfspace, the apparent resistivity of can be easily
calculated (Ou, 2019). In time-domain EM methods,
such as the MTEM, WEM, and LowTEM, thepseudo-random technique of M-sequence current

Figure 18. the pseudo-random seven-frequency
waveform(A=1,
T=1s),
thetime-domain
waveform(top), and the spectrum (bottom, Jiang,
2010)

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

Another use of pseudo-random is the application of
the 2n sequence. Because the main frequencies ofthe 2n sequence waveform are uniformly distributed
along the logarithmic coordinate, the amplitudes
and initial phases are essentially the same (Jiang,
2010). Using this waveform WFEM improved work
efficiency and anti-interference ability, and ensured
data quality. For example, the amplitudes andphases of seven frequencies can be obtained at one
observation (Figure 18).
Figure 20. The EM waves propagation of WEM
source (Di, 2020).

Figure 21. The comparison of power spectral
density between WEM and MT fields.
3.3 Utilization of the sky-wave
Figure 19. |Ex| (top), |Hy| (middle)and |Z| (bottom)
field decay curves for axial array at 32 Hz (Li etal.,2015

From the modeling of field components for different
lengths of wire source at different frequencies
based on equations (6) and (7), Di et al. (2020)
found that, when the source length is equal to the

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

8x10-58x10-5

S1-T1

(a)

S1-T2

(b)

6x10-5

Ex/V·m-1

Ex/V·m-1

6x10-54x10-52x10-54x10-52x10-5

0

0

-5

-2x10
-0.5

0.5

1.5

2.5

3.5

-2x10-5
-0.5

4.5

0.5

1.5

t/s
8x10

-5

6x10

-5

4x10

-5

2x10

-5

2.5

3.5

4.5

t/s
S1-T3

(c)

Ex/V·m-1

Ex/V·m-1

8x10

-5

6x10

-5

4x10

-5

2x10

-5

0

S1-T4

(d)

0

-5

-2x10
-0.5

-5

0.5

1.5

2.5

3.5

4.5

-2x10
-0.5

0.5

t/s

1.5

2.5

3.5

4.5

t/s

Figure 23. Raw data of 4 periods observed in thesuburban area with strong culture noise.
10-3

Normal(Windowed)

Ex/V·m-1

height of the ionosphere, the influence of the skywave (wave guide) should be considered. Figure 18
showed that the electric field observed on the
ground based on the earth model was significantly
larger than that of the homogeneous half-spacemodel when transmitter to receiver distance(r) was
more than several hundreds kilometers. Based on
this discovery, WEM was proposed by Di et al.
(2008) Figure 20 is the principle map of WEM. Since
the beginning of this century, China has carried out
the research and construction of ‘Extremely Lowfrequency Ground Exploration Project’, andestablished a fixed tensor source with a scale ofover 100 kilometers in Central China, thetransmitting power was up to 500kW, 21
frequencies were transmitted, and the frequency
band ranged from 0.1Hz to 300Hz. Due to theexistence of waveguide field, the WEM signal on the
MT background can be observed effectively at far
distance (Figure 21). Compared with MT data, theWEM data was considerable superior in quality(Figure 22), which has important theoretical andpractical significance for EM exploration in mainland
China.

10

-4

10

-5

10-6 -3
10

10-2

10-1

100

101

t/s

Figure 24. Ex Decay curve with stack and window
filtering processing.

Figure 22. the comparison of apparent resistivity
and phase curves between WEM(right) and MT(left)at site 31.
3.4 Reference observation and processing
technology of CSEM
In addition to conventionally increasing the current,
increasing the number of stacking and increasing
the observation time to improve the data signal-tonoise ratio, the application of reference observationand processing technology of CSEM was also the
key to ensure the quality of data. Yan (2012)
proposed the near-reference magnetic fieldobservation and processing technology for CSAMT.
This technique improved the quality of the data bycorrecting the high-quality magnetic fieldcomponent data from the near-reference
observation and introducing it into the Cagniard
apparent resistivity calculation of the CSAMT dataof the observed profile. With the increasing

popularity of distributed design, array measurement
and area data acquisition, the spatiotemporalcharacteristics of array data provided favorable
conditions for reference processing technology.
Zhou Cong and Tang Jingtian (2019) proposed a
space-timearray
mixed-fieldsourceelectromagnetic method (STAHSEM). It was basedon multi-input and multi-output system analysis,
established a spatiotemporal array equation systemusing multi-field sources and noise characteristics.
By processing the array data synchronously
observed by multiple stations, the high quality
electromagnetic data of natural field source andcontrollable source can be obtained while
suppressing the relevant noise effectively. In
LowTEM data de-noise processing, a method of thelocal noise compensation (LNC) combined with thevariational mode decomposition (VMD) was
proposed, which can effectively suppress the noisein the time-domain EM data and improve the decay
curve quality. Figure 23 was the LowTEM raw dataof four periods observed in the strong cultural noise
region. Figure 24 was the Ex decay curve obtainedby conventional stacking and filtering. And Figure 25
was the decay curve processed by LNC & VMD.

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

After comparison, it can be seen that the quality of
the head branch of the curve was obviously
improved, and the tail branch was more reasonable.

10-3

Ex/V·m-1

VMD-LNC(Windowed)

10

-4

10

-5

10-6 -3
10

10-2

10-1

100

101

t/s

Figure 25. Ex Decay curve with the VMD and LNC
joint processing.

observation fields with the prior information
constraints is the most effective way to improve theresolution and reliability (He, 2020, Liu, 2021). He(2020) established a joint objective function through
TFEM multi-component data, and proposed amethod to constrain parameters spaces ofthickness, resistivity, and polarizability using priorseismic information and well loging. They combined
the artificial fish and simulated annealing non-linear
algorithms and the step-by-step constraint schemes
together, which successfully overcame the nonuniqueness of inversion, ensured the exploration
accuracy and effect. Figure 26 was the dataconstrained inversion route. Figure 27 showed theresistivity and polarizability constrained inversion
results of the application in the tight oil and gas
exploration. In the inversion process, besides thepriori interface information, it was also constrained
based on logging and geological information (upperand lower boundaries). From the Figure 27, it canbe seen that there existed a variation for resistivity
and polarizability in the lateral direction in the same
stratum, and the resolution of the thin layer in thevertical direction was significantly improved.
Therefore, with more prior information, the inversion
was more accurate for the unknown physicalparameters.
4. Cases history

Figure 26. TFEM parameter extraction andinversion route with seismic data constraint

Figure 27. Inversion profile of resistivity andpolarizability (top is for resistivity; bottom is forpolarizability)
3.5 Joint and constraint data inversion
Various geophysical methods obtain the physicalproperty information from different perspectives.
Due to the existence of multiple interpretations ininversion, the different geophysical interpretation
methods produce significant differences in thegeological models, and even it cannot be reconciled.
Joint inversion of multi-methods and multi-

MT plays an important role in oil and gas explorationin the basin. At the beginning of this century, withthe development of CSEM, this method has been
expanded from exploration to development, from oil
and gas structure exploration to trap exploration,and to oil and gas detection, and the status of EM
method in oil and gas exploration has been rising.
As oil and gas exploration moved to unconventionalarea, the geological and geophysical properties of
exploration targets have changed greatly, new
exploration fields, such as hydraulic fracturing
monitoring of shale gas, tight gas and coalbed
methane, and sweet spot detection of shale gas andtight gas, have put forward higher requirements forelectromagnetic
exploration
methods
andtechnologies. MT method did not meet the needs ofoil and gas exploration in the strong electromagnetic
noise environment. In recent years, with the further
research on the geological and electricalcharacteristics of unconventional oil and gas
reservoir, many new CSEM methods andtechniques have emerged, and have been used in
unconventional oil and gas exploration anddevelopment (Zhang, 2013; Zhang, 2017; Yan,
2018; Wang, 2019). In the following, this paper will
present three cases on the sweet spot detecting,hydraulic fracturing monitoring and fluid property
identification with TFEM, WFEM, and LowTEM
methods.

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4.1 WFEM application in shale oil and gas
exploration
Jiangxi, China, is a province lacking conventional oil
and gas resources, but there are several sets of
mud shale and oil shale formations such as
Cretaceous, Permian, Silurian, Cambrian, etc.,
which are widely distributed, thick and relatively
stable. With the geological conditions for theformation of shale oil and gas reservoirs, shale oiland gas, tight oil and gas resources have great
potential and good prospects for exploration anddevelopment. A basin to be explored in southern
Jiangxi is mostly covered by red layers, developed
underground volcanic rocks, and has many small
local structures, and the geological andtopographical conditions are complex, the shaletarget layer is generally buried at a depth of about
1500m. In this area sweet spot detection, seismic
wave impedance difference was small, reflectedenergy was weak, seismic wave scattering wasserious, and data quality is low. Conventional
electromagnetic exploration data acquisition was
difficult, and the detection depth and accuracy wee
difficult to meet the requirements. To reveal theelectrical characteristics and EM response
principles for the source rocks in the basin, provide
the theoretical basis for the geological evaluation,and determine the location of well drilling, A
research group from Central South University
carried out the WFEM exploration in this region. It
deployed a survey line perpendicular to thestructural trend of the basin. In this project, they
expected to ascertain the basin structure, basement
properties, stratigraphic distribution, and fault
characteristics,
determinethestratigraphic
distribution of the lower Cretaceous Banshi
formation for the main target layer, and circle thesweet spots. Table 2 showed the statistical resultsof stratigraphic lithology and resistivity stratification.
From Table 2, we can see that from cover tobasement, it can be divided into four electricallayers: the first layer includes sandstone, siltstone,
and Mudstone of Guifeng group and Zhoutian
formation, showed medium resistivity, and with athickness of 700m; the second layer included
conglomerate, glutenite and siltstone of Maodian,Lengshuiwu formations, showed sub-high resistivity,
and with a thickness of about 600m; the third layer
included shale, oil shale, and Mudstone of Banshi,
showed obvious low resistivity, and with a thicknessof about 700m, the fourth layer included limestoneand quartz sandstone of Jilongzhang, Linshan
formations, and showed high resistivity.
In order to analyze the feasibility of WFEM detection,
two models of hydrocarbon-bearing shale(resistivity of 50Ω·m and non-hydrocarbon shale(resistivity of 200Ω·m) of the target layer (the third
electric layer) were established according to Table 2
for forward calculation. The blue and red lines in

Figure 28 were the apparent resistivity curves
corresponding to the oil-gas shale and the oil-freeshale, respectively. There was a significant
difference afterward, and the maximum anomaly
can reach 60%, which indicated that whether the
Banshi formation contained carbonaceous shale
can be identified according to the anomaly of theapparent resistivity curve of WFEM. Figure 29 was
the pseudo profile of primary apparent resistivity
curves formed by the electric field. According to thecontinuity and lateral variation in this figure, thestructures can be qualitatively interpreted. Thereexisted a sharp lateral variation of the curves
between site 116 and site 129, which indicated thatthe structure of this section was complex, and thecurve continuity of other sites performed well, whichshowed a relatively simple structure accordingly.
Table 2. Statistical results of lithology and resistivity
for the Banshi formation and its upper and lower
strataStratum

K2g

K2z

K2m

K1l

K1b

K1b
K1j
J1l

Main lithology
Finesandstone,
Sandmudstone,
Conglomerate,
Cobblestone
Sandstone,
Glutenite,
Siltstone,
Mudstone
Conglomerate,
Glutenite,
Siltstone
Glutenite,Finesandstone,
Siltstone,
Tuffaceous
sandstone,
Silty
mudstone,
Mudstone
Shale, Oilshale, Mudshale, Marl
Breccia lava,
Siltstone,
Conglomerate,
Tuff
Rhyolitic
brecciated tuff
Sandstone

Resistivity(Ohm-m)Electricalcharacteristics

100~200
Intermediate
resistivity
50~300230~650

Secondary highresistivity
300~500

36~125

Low resistivity

50~470Intermediate
resistivity

700~1800

High resistivity

1014~2893

Figure 30 was the profile of the 2D resistivity
inversion and geological interpretation. It showedthat the resistivity profile can be divided into three
sections laterally: the first part was the southeast
section (from site 101 to site 126) with the formation
resistivity as a high-low-high distribution from top tobottom; the second part was the middle part (fromsite 126 to site 170) with the formation resistivity asa low-high-low distribution from top to bottom; thethird part was the northwest section (from site 170
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low-high distribution from top to bottom. In general,
the survey line reflected a NE-dipping faulted basin.
The strata at both ends of the survey line were
uplifted as a whole, and the middle stratum wasoverall depression. In the depression, there was awide and gentle anticline in the Cretaceous (fromsite 126 to site160).

Figure 28. the apparent resistivity and abnormal
amplitude curves corresponding to oil-gas and nonoil-gas shales

Figure 29. the primary apparent resistivity curves
formed by the electric field for the survey line.

The uplift-in-concave feature in the middle of basin
was obvious. The development of fault structure
concentrated at the south and north ends of the
profile was intense. F70 and F26 were the primary
fault zones in the north and south faulted basins,
respectively. These two fault zones were reverse
faults, and some faults at the southern end of the
profile controlled the shale layer. Oil shale,
Mudstone, shale and other organic-rich source
rocks in the Banshi formation were self-generated
and self-stored, which was a favorable reservoir for
shale oil and gas.
The fault structure at the middle part of the survey
line (from site 126 to site 170) was relatively
promising area for oil shale oil development with adeeper target layer depth and a good stratigraphic
continuity. Figure 31 is the profile of polarizability
curve. It showed that the section from site 116 to site
170 had a high polarized anomaly. Integrated
information of resistivity and polarizability the sweet
spot can be interpreted in the area between site126
to site 170, with a buried depth of 1000-1600m, anaverage thickness of 560m. Drilling well 1 is locatedbetween site 142 and site 143. Figure 32 is thedepth histogram of drilling lithology formation. The
buried depths of the bottom of Guifeng, Zhoutian,and Maodian formations were 116m, 466.53m, and924.46m respectively. The depths of the threeinterfaces interpreted from the resistivity profile
were 155m, 490m, and 920m, respectively. This
showed that the inversion depths were very
consistent with the drilling results. In addition, thedrilling encountered a 2m thick fractured oil-bearing
shale in the Banshi formation at a depth of 1500m.
This indicated that the inversion depth was accurate,
the interface of the electrical layer was clear, and
the WFEM it was very sensitive to the conductive oil
shale.

Figure 30. The profile of the 2D resistivity inversion
and geological interpretation

Figure 31. The profile of the polarizability curve

Figure 32. The histogram of the drilling stratum,
lithology, and resistivity

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

4.2 TFEM Application in tight oil exploration
PM exploration area in the Middle East has been
fully conducted by 3D seismic, with hundreds of oil
Wells and abundant geological and geophysicaldata. However, the seismic method can not solved
the distribution of source rocks under the tightreservoirs and deep gypsolytes. TFEM can easily
penetrate the high resistivity layers and identify lowresistivity and high-polarization targets. BGP used
TFEM to detect sweet spots in the upper and lower
layer of the gypsolyte in the PM exploration area.
⚫ The Geo-electrical Model
The main electrical marker layer in this area is a setof gypsolyte layer with a depth of 2000m and amaximum thickness of 1000m, which is partially
missing. The seismic exploration have identified
undulations in the top interface, the rock formation
was characterized clearly by drilling data, and theresistivity was stable, about 2000 Ω· m. There arethree sets of reservoirs in the exploration area, themain reservoir is clastic reservoir of the upper
Paleozoic Khlata Group, the other two aresandstone reservoir of the Safig formation on the
top of the lower Paleozoic Ordovician andsandstone reservoir of the Haima group under theplaster rock.
Based on the geological and drilling information, thestrata above 3500m depth can be divided into 16
layers, with the 8th layer being the targeted
reservoir. The main oil area of this reservoir has
been exploited. The resistivities of the waterbearing and oil-bearing reservoirs are 1.5 Ω·m and200 Ω·m, and the average resistivity is 70 Ω·m. The
Lithology and geo-electric model data were shownin Table 3 and Figure 32.

Figure 33. The layered geo-electric model⚫ Feasibility analysis of the methods
Based on the geo-electric model (Figure 33), andthe ranges of the constraint model spaces wereused in Table 3. The effectiveness of 1D model
space constraint inversion method with artificial fishswarm no-linear algorithm can be tested. Table 4
showed the inverted resistivities. From Figure 33,
and Tables 3 and 4, the inversion results were
almost consistent with the model, which showedthat the artificial fish swarm inversion was correct
and feasible.
Layer number Inversion
Theoreticalresultsmodel(  m )(  m )
1
85.54
80
2
102.58
100
3
7.94
8
4
49.81
50
5
4.98
5
6
9.61
10
7
25.94
25
8
69.66
70
9
13.59
15
10
20.31
20
11
4.98
5
12
2211.80
2000
13
48.35
50
14
192.45200
15537.10500
16989.801000
The
inversion
results
oftheelectrical
Table 4.
parameters⚫ Interface constrained resistivity inversion
Based on the interpretation results of 3D seismic
data in PM exploration area and the well data
nearby, a geometric model of formation interface ofthe survey line was constructed (the dot lines in
Figure 35). The model interface was fixed when the
TFEM data inversion was conducted, and then theresistivity model (Table 3) was applied in theconstraint inversion, and the inversion results wereshown in Figure 34. It showed that the resistivity
coincided well with the formation interface, thegypsum resistivity coincided with the logging data,and it represented clear interfaces and strong
horizontal continuity. In addition, the inversion
results presented a clear Carboniferous oil-bearing
reservoir (from site 1014 to site 1104). Comparedwith the drilling data, the interface depth wasentirely consistent with the resistivity for each layer.
The section from site 1054 to site 1074 was an oil
field area that has been exploited for over ten years.
Due to the water injection production, the resistivity
was generally lower than that of the undevelopedarea. The high resistivity anomaly between the site1094 and site 1154 was quite apparent. Whether it
was a favorable sweet spot can be comprehensively
judged in combination with the anomaly ofpolarizability.
⚫ Interface and resistivity joint constrainedpolarizability inversion

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

To improve the accuracy of polarizability inversion,an artificial fish swarm inversion with simultaneous
constraints of interface geometry model andresistivity model was applied. Table 3 showed thepolarizability model and the model space. The
polarizability model space for different strata wasdetermined based on the lithology analysis data, but
the polarization of the reservoir was not constrained.
Figure 34 showed the polarizability inversion profile.
The section from site 1014 to site 1104 was thelocation of the known oil reservoir, it can be seenthat the main Carboniferous reservoir hadpronounced high polarization characteristics.
Combined with the high resistivity characteristics forresistivity profile (Figure 35), the area from the site1094 to site 1154 was circled as a sweet spot, andthe high resistivity and low polarization anomaly
near the site 1114 was not. The section of 11541184 was also characterized by high polarizationand high resistivity, and it was also circled as asweet spot, this interpretation was confirmed by the
drilling results. Hence, joint interpretation of thesweet spot and its distribution area with resistivity
and polarization increased the success rate of oil
and gas exploration for TFEM.

Figure 34. The interface and resistivity model
constrained polarizability inversion profile

Figure 35. The interface constrained resistivity
inversion profile.
4.3 Application in dynamic monitoring of
hydraulic fracturing
Jiaoshiba shale gas field is the largest integrated
gas reservoir in China. The effective application of
hydraulic fracturing technology is conducive tostabilizing and increasing production in this area.

Micro-seismic monitoring plays a vital role in
improving the fracturing effect. However, affected by
the complex terrain and surface geologicalconditions in southern China, the signal-to-noise
ratio of micro-seismic monitoring was low, and thepositioning accuracy was not high. In addition,
limited by the micro-seismic method, the estimationof effective volume (ERV) is not accurate very much
(Hoversten,，2015). It is well known that when alarge amount of fracturing fluid is injected into thereservoir rock, it will flow along the microfracturesand continuously expand the fractures, this leads tothe change of electrical properties. Due to the low
resistivity characteristics of the fracturing fluid andthe good connectivity caused by fracturing fluids,
the stimulated reservoirs show good low resistivity
and high polarizability characteristics, therefore, EM
fracturing monitoring has a good physical property
foundation. The application of EM methods canobtain sensitive parameters such as fluid trend,
volume change and connectivity during thefracturing process, so as to achieve the purpose ofmonitoring in unconventional oil and gas reservoir
fracturing.
In 2017, the EMLAB group of Yangtze University
applied the LowTEM method to conduct continuoustime-domain EM fracturing monitoring test in
Jiaoshiba shale gas field. Through 224 Ex channelsof data acquisition array, nearly 9 hours Ex
component time series data had been acquired overthree fracturing stages buried at a depth of 2800
meters. Through 4D LowTEM processing, thesensitive parameters of electrical changes causedby the fracturing of shale reservoirs were obtained,
and the spatial distribution of the fracturing fluid wasimaged. The results have guiding significance forfracturing production and well pattern adjustment.
⚫ Electric characteristics of stratum
Table 5 showed the parameters the geo-electricmodel in test area based on JIAOYE #1 well logging.
The model showed a good correspondence
between resistivity and stratum, and the shale
resistivity is obviously lower than that of thesurrounding rocks.
⚫ Electrical characteristics of fracturing fluid
Fracture liquids were sampled from three horizontal
platforms (The base fluid is 2% potassium chloride).
The measured complex resistivity results (Figure 36)
showed that there were specific differences in theresistivity amplitude of fracturing fluids on different
platforms, ranging from 0.8-6.4Ω·m. Still, they were
all about an order of magnitude smaller than shales.
The phase showed that they had different minimum
values at frequency 50-500Hz, which indicated thatthey had strong polarization characteristics due tothe presence of proppant.
⚫ Normalized residual time-lapse imaging
All-time apparent resistivity was calculated fromelectric field components observed before and after

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

fracturing, then resistivity imaging was carried out
based on the theory of chimney effect of current
diffusion. To highlight the changing of resistivity
caused by fracturing, a normalized residual
processing method was developed in the log-log
domain. The formula isDF = 100  d | log 1 − log  2 | / d log h(20)

length of the ground source was 4000m, in an eastwest direction, the minimum offset was 5000m, thecurrent was 65A, the current waveform was abipolar square wave with a period of 8s and a duty
cycle of 50%, the sampling rate was 400Hz. The
schematic diagram of the field construction layout
was shown in Figure 38.

Where DF is the value of normalized residual,1 is imaged resistivity before fracturing,  2 isimaged resistivity after fracturing, and h is the depth.

Figure 36. The complex resistivity of fracture liquid(Top: Amplitude; Bottom: Phase)⚫ Model testing
In geo-electric model(table 5), the resistivity of thereservoir stratum was assumed as 42Ω·m before
fracturing, and 5Ω·m after fracturing. The
normalized Ex and the normalized residual curves
had shown in Figure 37. The top curve showed thatthe change was identified from decay time 5ms to
100ms when the reservoir stratum fracturing was
conducted in the depth of 2330m. Normalized
residual curve can depict the fracture change clearlyin the range from 30ms to 40ms (bottom curve).
⚫ LowTEM monitoring and analyzing
8 lines with length of 1400m each, 224 sites
with 50m interval, were setup above the stage of 6,7, 8 of the horizontal hydraulic fractured well. The

Figure 37. The EM field (top) and normalized
residual (bottom) curves
S

5 Km

Line Design

Horizontal well 51-5，Fracture Segment 6，7，8

Shale Sweet Spot

Figure 38. LowTEM Layout for the monitoring ofshale fracture

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

The fracturing production information in themonitoring section is shown in Table. The
continuous recording time of Ex while stage 6,7,8
were fractured was up to 9 hours, which ensured thewhole process of the fracturing process of eachstage. In order to guarantee the quality of decay
curve, 30 minutes of electric field component time
serious of segment 6, 7, 8 each was used to stackprocessing, and the normalized residual curves had
been acquired on the sites of L01-14 and L01-16. In
Figure 39, the weak changes of fracturing can beseen in the decay time range from 0.03s to 0.1s.
The shape and time window were consistent withthe forward results in Figure 37.

600 m, in which 400m was in the west of horizontal
well, 200m was in the east of horizontal well. The
width from north to south was about 200-300m.
After segment 7 fracturing the length of the fractured
reservoir stratum in the direction of east to west was
about 700m, in which 400m was in the west ofhorizontal well, 300m was in the east of horizontal
well. The width from north to south increased to 300400m. Overall, the trend and spatial distribution of
fracturing fluid were dumbbell-shaped, and thefracturing effect in the west area was better than thatin the east area.

Figure 40. Anomaly in the space before and after
fracture(top：after segment 6；bottom：after segment
7)
5 Conclusion and future
5.1 Conclusion
Figure 39. hhe curves of electric field comeonent
Mx observed at eoints 14 (toe) and 16 (bottom) of
L01 over the segment 6 and 7 in horizontal wells
The 3D fracturing anomalies showed in Figure 40
which were imaged based on the normalized
residual curves, and at the same time the calibration
was carried out with the help of seismic and welllogging. The results showed that, after segment 6
fracturing the length of the fractured reservoir
stratum in the direction of east to west was about(a) The complex resistivity analysis of reservoir
rocks showed that the organic-rich shale in southern
China had the characteristics of low resistivity andhigh polarization. Tight oil and gas reservoirs werecharacterized by the high resistivity and highpolarization. The hydraulic fracturing reservoir
stimulation area had the characteristics of low
resistance and high polarization. All these indicatedthat the CSEM detecting the sweet spots had a firm
petrophysical basis.

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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods(b) The high spatiotemporal density data collection,
combining with updated methods, such as the highpower transmitters, pseudo-random waveforms,
reference observation and processing, variationalmode decomposition, have improved signal-tonoise ratio and guaranteed data quality, which can
profit the accurate inversion.
(c) The new EM attribute parameter definitions
greatly improved the sensitivity of CSEM to detect
the target attribute and the fluid property, and also
facilitated geological interpretation.
(d) With the prior information constraints, such as
geology, seismicity, and well logging, the joint
inversion of for multiple methods and multiple
component data, can produce an accurate andreliable interpretation model.
(e) The sweet spot detection model and theparameter prediction method formed by the EM
characteristics of shale reservoirs have laid aphysical foundation for applying EM methods ingeological evaluation. This techniques can be used
for reference in the data interpretation of EM
detection in other unconventional reservoirs.
5.2 Future(a) Research on the inversion method based on IP
model under seismic, logging and other priorinformation constraints should be carried out so thatto improve the resolution and reliability of TDEM in
unconventional oil and gas exploration.
(b) At present, the application of WEM method has
played an essential role in engineering geology,
mineral exploration, disaster prevention, andenvironmental governance. As the WEM source isimproved further，it has become a dedicated fixedsource that can transmit multi-segment frequency
codes, which has improved exploration efficiency,
reduced production costs, and ensured effective
exploration depths up to 5km. the WEM method has
become an essential means in the exploration anddevelopment of unconventional oil.
(c) The data collection methods should becontinuously optimized. The EM forward model,
analysis, and acquisition parameter evaluation
before field work are the key to the success of EM
survey. Softwires should be developed for EM datacollection method and parameter optimization,
further to provide robust means for data collection.
(d) The heterogeneity and electrical anisotropy of
unconventional oil and gas reservoirs and hydraulicfracturing areas are pretty severe, which will lead tothe misunderstandings in data processing andinterpretation. It is an unavoidable problem to study
the high-precision anisotropy forward modeling andinversion method.
(e) Based on the characteristics of EM explorationand micro-seismic monitoring, an acquisition arraysystem that can simultaneously collect controlledsource EM methods and micro-seismic signals

should be developed, effectively achieve the deep
integration of EM and micro-seismic in all directions,and form a hydraulic fracturing controlled-source
EM method and micros-seismic joint monitoring
method.
(f) When we are faced with the difficulty of effectively
suppressing the electromagnetic noise, the timeconsuming and multi-resolution of 3D inversion, andthe unsatisfactory application effect of sweet spot
detection and evaluation, the arrival of artificial
intelligence and big data show us a new hope.
Through big data analysis, we are expected to findthe real laws of the noise world, the model world andthe underground electrical world hidden behind thebig data of electromagnetic exploration. The
analysis and prediction of big data through artificial
intelligence and cloud computing will make the EM
processing and interpretation more accurate,
convenient and efficient, thus mining more hidden
value of electromagnetic data.
Acknowledgements
This review was supported by Key Project (N0.:42030805) of National Natural science foundation ofChina. In the process of references collection andwriting this paper, I have received the assistancefrom Prof. Struck, Prof. Qingyun Di, Prof. Zhanxiang
He, Dr. Zhigang Wang, Professor Jingtian Tang,
Professor Zhengyong Ren, Professor Diquan Li, Dr.
Kui Xiang, Dr. Lei Zhou, Dr. Xin Huang, Dr. Xiaoyue
Cao, Dr. Tong Xiaolong and graduate students of
EMLAB. All thanks here.
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Table 1. the laboratory measurement
Stratum
LongmaxiGroup,
Silurian
WufengGroup,
Ordovician
Qiongzhusi
Group,
Cambrian
Dengying
Group, Sinian

Layer
number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Stratigraphy
P2ch-C2hl
S2h-S1l
S1l
S1l -O3w
O3-O1

Lithology

Rock
NumberDensity(g/cm3)Magnetisability(10-5SI)

Permeability
(mD)

Vp
(m/s)

Vs
(m/s)

Resistivity(Ohm-m)Polarizability
(%)

Shale

52

2.54

2.4

1.232

3580.00

2309.80

111.2

18.1

Shale

5

2.52

3.3

0.001

4898.942880.18

168.7

21.7

Shale

112

2.43

5.4

0.933

4178.00

2623.62

147.0

42.5

Dolomite

23

2.81

1.9

3.606

4278.00

2877.972507.8

12.5

Table 3. the electric model and the model space for the PM area
Thickness and model
Resistivity and model
Polarizability andspace (m)space (   m )model space
40(39.6,40.4)80(50,100)
0.040(0.010,0.150)
110(109.9,110.1)
20(5,80)0.120(0.010,0.200)
175(173.3,176.8)
10(1,30)
0.060(0.010,0.250)
250(2247.5,252.5)50(10,70)
0.150(0.050,0.500)
140(138.6,141.4)
5(2,15)0.050(0.010,0.350)
50(49.5,50.5)
10(5,15)0.150(0.010,0.350)
50(49.5,50.5)25(20,80)
0.250(0.010,0.450)
60(59.4,60.6)
70(1.5,200)
0.400(0.050,0.700)
90(89.1,90.9)
15(5,20)0.100(0.010,0.400)
700(693.0,707.0)20(2.5,100)
0.010(0.005,0.100)
450(445.5,454.5)
5(1.5,20)
0.075(0.010,0.150)
650(643.5,656.5)
2000(1950,2050)
0.005(0.000,0.001)
100(99.0,101.0)50(10.80)
0.010(0.001,0.100)
400(396.0,404.0)
200(150,300)
0.100(0.010,0.200)
180(178.2,181.8)500(100,2000)
0.120(0.010,0.200)
1000(990.0,1010.0)1000(300,2000)
0.150(0.010,0.200)

Table 5. Geo-Resistivity model based on JIAOYE #1 Logging curve
Lithology
Burial depth(m)Thickness(m) Resistivity（Ω·m)
Limestone
0--1430
1430>2000
Mudstone
1430--2290
860
30-40
Sandstone
Gas-bearing
shale
Limestone

2290--2330

40

244

2330--2410

80

42

2410—

>1000

Table 6. Production information record of shale gas fracturing in test horizontal wells
Well section
FracturingStart Fracturing
Fluid/Proppant
Fracturing datenumberTime
Termination Time
(m3)
XX-6
February29,
18:07:23
20:35:03
1849.9/40.4
2016
XX-7
March 1, 201608:41:33
11:37:00
1955.6/49.6
XX-8
March 1, 2016
17:07:50
19:53:00
1847.7/48.1

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Archaeological prospecting using drone-towed electromagnetic and magnetic
systems
Tobias Bjerg Vilhelmsen
tobjer@space.dtu.dk

SUMMARY
Drone-borne magnetic and electromagnetic sensor systems solutions have gradually expanded their use
within geophysical prospecting. Magnetic and electromagnetic sensors in a drone setup allow much movability
compared to a standard handhold walking survey and will also downscale the cost and risk typically associatedwith helicopter-borne surveys. Hence, a drone-based solution seems very attractive for magnetic andelectromagnetic prospecting and could potentially replace some of the existing setups.
This abstract presents drone-towed magnetic and electromagnetic systems and datasets (Figures 1 and 2)
from the same area in the southern part of Denmark (Thy), where both the magnetic and electromagnetic data
show features from sub-surface structures in their raw data form. The area is known for its prehistorical flint
mines, and using both magnetic and electromagnetic data will provide a better background for mapping these.
To further enhance and verify any archaeological structures, we will apply levelling, micro levelling and grid thedata to make interpretation possible.
The sensors and drone are off-the-shelf products, but the arrangement and construction of the survey are
unique. We towed the sensors in a 6 meters wire configuration underneath the drone to avoid electromagnetic
interference (Vilhelmsen and Døssing 2022) and flew with 0.7m line spacing approximately 1 meter above thesurface. The vertical magnetic gradient shown in Figure 1 is measured in the same area as the quadrature
response, and the legend is comparable. The quadrature response shown in Figure 2 is a stacked response
from a transmission frequency of 40025Hz, 65675Hz, and 91275Hz measured with the multi-frequency
broadband electromagnetic sensor GEM2-UAV from Geophex.

Figure 1: The raw vertical magnetic gradientfrom the drone-towed magnetic survey isplotted here in the limit +/- 1nT.

Figure 2: The stacked quadrature response fromthe drone-towed frequency-domain electromagnetic
induction survey, with the transmission frequency of40025Hz, 65675Hz, and 91275Hz.

Vilhelmsen, T. B. and Døssing, A.: Drone-towed CSEM system for near-surface geophysical prospecting: On
instrument noise, temperature drift, transmission frequency and survey setup, EGUsphere [preprint],
https://doi.org/10.5194/egusphere-2022-217, 2022.
Keywords: Drone-towed system, Archaeological prospecting, electromagnetic induction, and magnetic.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Identifying the causes of the vertical component geomagnetic field anomaly at
Eskdalemuir geomagnetic observatory, Scotland
G. Wang1,2 , J. Hübert2 and K. A. Whaler1
University of Edinburgh, UK, s2127818@ed.ac.uk
2British Geological Survey, Edinburgh, UK, guanren@bgs.ac.uk
1

S UMMARY
Magnetic field observations in the Scottish Southern Uplands reveal a dampened amplitude in the verticalcomponent variations at Eskdalemuir when compared with the horizontal components for periods less than anhour. This relationship is not observed elsewhere. A high conductivity feature beneath Eskdalemuir has been
the focal point of past literature to account for this local anomaly. However, the impact of solar activities on theexternal field variations at Eskdalemuir has yet to be investigated as a possible cause. This is an importantfactor to consider because it is assumed that large current systems are uniform at mid-latitude for geomagnetic
depth sounding techniques to be applied. When this assumption is violated, the vertical magnetic field is mostsensitive to the presence of non-uniform contributions. In this study we aim to characterise the space-weather
driven external field contributions at different timescales to the measured vertical field component, using longtime series of modern data recorded at the Eskdalemuir magnetic observatory.
Vertical magnetic transfer functions (tippers) are estimated using three-month datasets at one-minute and onesecond cadences between 2003 and 2019 in order to examine their temporal variations due to the impact fromsource effects. Our results show that at Eskdalemuir, there are significant variations in the tipper estimates forperiods above 1000 seconds between summer and winter months for the same year, especially in the RealTx component. These findings have implications for data collected during other field campaigns with shorter
recording times.
Keywords: magnetic induction, space weather, geomagnetic depth sounding, tipper

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

From Coast to Coast: Ongoing Magnetotelluric Data Processing for the NationalScale Survey, USMTArray-CONUS SouthJ. Crosbie1, P. Bedrosian2 and A. Kelbert3
1USGS Geology, Geophysics, and Geochemistry Science Center, Denver, CO - USA, kcrosbie@usgs.gov
2USGS Geology, Geophysics, and Geochemistry Science Center, Denver, CO - USA, pbedrosian@usgs.gov
3USGS Geologic Hazards Science Center, Golden, CO - USA, akelbert@usgs.gov

SUMMARY
The U.S. National Science Foundation’s (NSF’s) EarthScope program (2006-2018) produced many valuable
geophysical datasets, including a large-scale, long-period magnetotelluric transportable array (USMTArray).
Through EarthScope, approximately two-thirds of the conterminous U.S. was covered at a quasi-regular 70km station spacing. NASA funded the project through 2019, and since June 2020, Oregon State University,
cooperatively with the U.S. Geological Survey, has been completing the southern conterminous U.S., marchingstations from California to Florida. Disseminating an ongoing, national-scale survey to the public is a novel
undertaking in the magnetotelluric community, as it requires continuous processing of magnetotelluric array
data, quality control for the raw time series, and maintaining an updated database of transfer functions.
Transfer functions are used to estimate space weather power grid hazards; thus, timely processing is a national
priority.
Here, we present the latest workflow for the ongoing real-time magnetotelluric data processing and archiving.
We discuss processing and automation improvements to the workflow, including approaches to expand therange of long-period data into the “dead band.” Weekly feedback is given to field crews about instrumentation
issues as well as recommendations to continue data collection or to extract quality stations. We discuss thevariety of field issues encountered, from cultural noise in California, to flooding issues in Florida. In addition,
we focus on unidirectional noise in transfer functions in Florida and other gulf coast stations that may haveoceanographic origins. The processing workflow for the USMTArray data has been expanded to work for
wideband data, building upon the original USMTArray long-period workflow. We have applied the wideband
processing workflow to a diversity of datasets and geologic settings including Yellowstone (Wyoming), Katmai(Alaska), and the Stillwater Complex (Montana). Finally, transfer functions are openly available and can bequeried by survey (USMTArray) at the EM Transfer Function Product Query on IRIS SPUD
(http://ds.iris.edu/spud/emtf).
Keywords: magnetotelluric, transfer functions, data processing, national-scale survey, archiving

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

ELMAR - the ELectroMAgnetic Recorder
Oliver Ritter1, Stefan Rettig1, Reinhard Schmitt1, Martin Haxter1, Carsten Müller-Brettschneider1, Ute
Weckmann1
1GFZ - German Research Center for Geosciences, oritter@gfz-potsdam.de

SUMMARY
For many years, the Geophysical Instrument Pool Potsdam (GIPP) of the German Research Centre forGeosciences (GFZ) has provided the international research community with geophysical and in particular
Magnetotelluric (MT) instruments to conduct field experiments around the world. The instruments are provided
free of charge, but with the obligation to archive and eventually publish the data. In total, recording equipment
and sensors can be provided for about 50 broadband and 30 long-period MT stations. The most important
workhorse in recent years has been the S.P.A.M. Mk IV data logger, which provides recordings in the frequency
range from DC to about 10 kHz (max. 25 kHz sampling rate), with a typical power consumption of 6 – 8 W (nosensors connected).
Here we introduce ELMAR - the Electromagnetic Recorder - a new generation of data loggers designed anddeveloped by the GIPP. The main advantages over existing systems are: a wider frequency range (max. 256
kHz sampling rate), lower power consumption (1.5 – 3 W), a wide range of supply voltages (8 – 36 V DC,
Power over Ethernet, USB-C PD) and overall reduced dimensions and weight. User interaction is based on
the http protocol, which means that ELMAR systems can be controlled from any web browser. A graphical userinterface is provided to enter the required site information, check system status, monitor power consumption,
voltages, and most importantly, the incoming data.
The analogue section provides software-controlled amplification (or attenuation) with variable gains. Gain
control can be manual or automatic. All analogue inputs are differential with high impedance inputs (GΩ range).
Optionally, a DC offset can be subtracted before the gain, e.g. to compensate for self-potentials of the electrical
dipoles. Analogue-to-digital conversion is with 24-bit resolution.
The digital time series data can be filtered in a cascade decimation scheme, using a range of low-pass andhigh-pass filter settings. All data streams are continuous. Time series data, (calibrated) Fourier spectra, andMT transfer functions can be monitored (virtually) in real time. A comprehensive scheduler allows simultaneous
acquisition of data streams with continuous data or pre-set (periodically recurring) intervals. An accurate timebase is provided by a (dual) GNSS receiver and a voltage-controlled oscillator. The time series data is stored
on SD cards.
The ELMAR recorder has a programmable test signal generator, which is used for self-testing the analogue
electronics, but also for measuring contact resistances between the electrodes without polarizing them or for
feeding a test signal into the induction coils.

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th

Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Hybrid receiving dipole for broadband electric field measurement
1,2*

N. Zorin

1

1, D. Epishkin , D. Yakovlev and A. Yakovlev

2

1

STC Nord-West, Moscow, Russia
Moscow State University, Moscow, Russia*
corresponding author; nikita.zorin.geophys@gmail.com
2

SUMMARY
High-frequency electric field measurements with a regular receiving dipole with poor electrode contact
are greatly influenced by capacitive leakage between the wires and the ground. At the same time, noncontact
receiving lines are not generally suitable for low-frequency electric field measurements. We introduce a novel
hybrid galvanic-capacitive receiving dipole, which could be used for broadband electric field measurement
even with high contact resistance of grounded electrodes.
Keywords: poor electrode contact, capacitive leakage, ECR-effect.

INTRODUCTION

CLASSIC RECEIVING DIPOLE

In electrical prospecting there are two ways tomeasure the electric field in the ground, that is, thecontact, or “galvanic” method, and the noncontact
or “capacitive” one. In the first case, the field sensor
is a receiving line connected to a pair of groundedelectrodes, while in the second case the receivingline includes special capacitive electrodes or just a
pair of ungrounded wires. The galvanic method
shows the best results when working at very low
frequencies (up to ~100 Hz) and is widely used inresistivity methods, induced polarization (IP) andmagnetotelluric (MT) sounding. The capacitive
method shows best performance at very highfrequencies (>1 kHz) and is successfully used innoncontact modifications of resistivity methods(Gruzdev et al. 2020; Kuras et al. 2006) and inradio-MT (RMT) exploration (Shlykov et al. 2020).
Unfortunately, the application of either of theindicated electric field sensors for carrying out
measurements at both low and high frequencies is
associated with serious difficulties. In particular, aconventional grounded array, as applied in theaudio-MT (AMT) method, with a frequency ranging
from ~1 Hz to ~10 kHz, requires very low grounding
resistance values (<1 kΩ). Otherwise, the arisingparasitic electrode contact resistance (ECR) effects
may result in large distortions of the high-frequency
parts of the sounding curves (Zonge and Hughes
1985). At the same time the non-contact technique
is not applicable for frequencies well below ~1 kHz.
This problem severely limits the applicability ofsuch methods as AMT when working on frozen,
sandy, or stony soils, rocks, etc.
Below we present a novel electric field sensor,
which allows taking broadband measurements
without any excessive requirements for the contact
resistance of the grounded electrodes.

The classic version of the receiving electricline and its simplified equivalent circuit are shownin Figure 1 (Vishnyakov and Vishnyakova 1974;
Zonge and Hughes 1985). The following symbols( ) is the grounding
are used in the figure:( ) is theresistance of the electrode M (N);capacitance between the cable M (N) and theground;is the complex input impedance of thereceiver;is the distance between the groundedelectrodes;
is the electric field in the ground; andis the observed voltage at the receiver’s input.
andare related as follows:
0.5 +(ω) = (

0.25
1 + 𝑖ω
1+

0.25
1 + 𝑖ω
(ω)
(ω)
+

)

(ω),

where 𝜔 stands for the angular frequency; 𝑖stands for the imaginary unit;=+
isthe complex contact impedance of the dipole MN;⁄(1 + 𝑖ω),).
== ⁄(1 + 𝑖ω
Separating in this expression the coefficientdue to wire-to-ground capacitive leakage fromthe coefficientdue to voltage division at thereceiver’s input, we get:
(ω) =
(ω) =

(ω) = 0.5 +

(ω)

(ω)

(ω),

(ω)
,
(ω) +
(ω)
0.25
1 + 𝑖ω

+

(1𝑎)
(1𝑏)

0.25
1 + 𝑖ω

.

(1𝑐)

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Zorin, N. et al., 2022, Hybrid receiving dipole for broadband electric field measurement

For a perfectly grounded dipole Equation 1
yields≡≡ 1, and the observed voltage
is related to the measured electrical fieldwith asimple expression:
(ω) =

(ω).

(2)

In a real grounded dipole, the voltage recordedby the receiver is always less than the expectedvalue (2), and the degree of bias is governed by thevalues of complex coefficientsand.
HYBRID RECEIVING DIPOLE
The proposed hybrid “galvanic-capacitive” line
and its simplified equivalent circuit are shown in
Figure 2. It consists of an ordinary grounded dipole,
supplemented with two more wires of the samelength connected to each of the electrodes andstretched in different directions, as shown in Figure
2 (the far ends of the additional wire sections are
insulated). Since the centers of the resulting M andN cables coincide with the grounding points of thecorresponding electrodes, the effective lengthofthe hybrid dipole at very low frequencies (“galvanicmode”) equals to that at very high frequencies(“capacitive mode”). As a result, the measured
electric field in the ground is related to the observed
voltage at the receiver’s input as follows:
(ω) =
(ω) =

(ω)

(ω),

(ω)
.
(ω) +
(ω)

(3𝑎)
(3𝑏)

Comparison of Equations 1 and 3 shows that,irrespective of the contact resistivity of groundedelectrodes, the electric field measurements with ahybrid receiving dipole are not vulnerable to thewire-to-ground capacitive leakage coefficient,and the only remaining source of possible distortionis the coefficientdue to voltage division at thereceiver’s input. The influence of the latter could be
minimized by using a receiver or pre-amplifier with
sufficiently large input impedance
or by taking
broadband in situ measurements of the complex
contact impedanceat every observation siteand correcting the acquired electric field data withthe help of Equation 3.

th

CONCLUSIONS
Unlike conventional grounded or ungroundedelectric field sensors, hybrid receiving dipoles arecapable of performing equally well in galvanic,
capacitive, and mixed operating modes. At very low
frequencies this dipole behaves like a classic
grounded line, at high frequencies – like a classic
ungrounded line, and at intermediate frequencies
there is a smooth transition from one mode ofoperation to another without changing the effective
length of the receiving dipole. Thus, the hybrid
setup successfully combines the advantages ofgrounded and ungrounded lines without showing
their main drawbacks. The principal advantage ofthe hybrid dipole is its potential ability to record
unbiased data in an unlimited frequency bandwidth,even under poor grounding conditions. At the sametime, the way of its practical implementation
proposed in our research is rather simple, requires
minimal additional labor and, therefore, should notlead to a noticeable rise in the costs of field work.
REFERENCES
Gruzdev AI, Bobachev AA, Shevnin VA (2020)
Determining the field of application of thenoncontact resistivity technique, Moscow Univ
Geol Bull 75 (6), pp. 644–651.
Kuras O, Beamish D, Meldrum PI, Ogilvy RD (2006)
Fundamentals of the capacitive resistivity
technique, Geophysics 71(3), pp. 135–152.
Shlykov A, Saraev A, Tezkan B (2020) Study of apermafrost area in the northern part of Siberia
using controlled source radiomagnetotellurics,
Pure Applied Geophys 177, pp. 5845–5859.
Vishnyakov AE, Vishnyakova KA (1974) Field
excitation and measurement in electricalprospection. Nedra, Leningrad [in Russian].
Zonge KL, Hughes LJ (1985) Effect of electrodecontact resistance on electric fieldmeasurements, Expanded Abstr. Technical
Programme of 55th Ann. Intern. SEG Meeting,contrib. MIN 1.5. Tulsa. OK. pp. 231–234.

Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Zorin, N. et al., 2022, Hybrid receiving dipole for broadband electric field measurement

Figure 1. Classic receiving dipole (a) and its equivalent circuit (b).

Figure 2. Hybrid receiving dipole (a) and its equivalent circuit (b).

thAbstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Theory and practice of Controlled Source Audio-frequency Magneto-Tellurics:
discussion on two case studies in France, at the Rochechouart impact structure andat the Strengbach catchment
P. Sailhac1, Y. Quesnel2, M. Lajaunie3, S. Warden4, C. Camerlynck5, P. Lambert6, J.-P. Malet3
1University of Paris-Saclay, France (UMR8148 GEOPS), pascal.sailhac@universite-paris-saclay.fr
2Aix-Marseille Université, France (UMR730 CEREGE), quesnel@cerege.fr
3University of Strasbourg, France (UMR 7063 ITES), m.lajaunie@hotmail.fr
4Hyperion, France, sheldon.warden@gmail.com
5Sorbone University, France (UMR 7619 METIS), christian.camerlynck@sorbonne-universite.fr
6Center for International Research and Restitution on Impacts and on Rochechouart, France (CIRIR),

philippe.lambert@gmail.com

SUMMARY
Audio-frequency Magneto-Tellurics (AMT) is a passive noninvasive prospecting method; it is based on theelectromagnetic induction in the ground from background electromagnetic plane waves caused by far distancesources of natural or anthropic origins; their frequency range is 20 Hz-20 kHz. Controlled Source Audiofrequency Magneto-Tellurics (CSAMT) method is an alternative to AMT which is considered to cope withpractical field situations where background anthropic noise from near sources has high amplitudes comparedto other incident electromagnetic waves; controlled source can be used to avoid high acquisition times oradvanced robust processing that are necessary in AMT (Larnier et al. 2018). Controlled source is particularlyinteresting at frequencies in the so-called “AMT-dead band” of low natural activity near 1-3 kHz, corresponding
to skin-depth of tens to hundreds of meters, depending on the underground resistivities.
In CSAMT theoretical principles, transmitter must be a set of large electric dipoles (hundreds of m) located far
from the receiver area (tens of km); this geometry is necessary to eventually obtain good estimates of all
impedance tensor components. However, actual field situations often impose different configurations withshorter dimensions, and one should deal with the measured electric and magnetic records by using the moregeneral theoretical framework of Controlled Source Electro-Magnetic (CSEM) methods.
We consider two case studies of CSAMT surveys whose size of the electric transmitter antenna is relatively
small (100-300 m). One study shows of a profile at the Rochechouart-Chassenon impact structure, France(Quesnel et al. 2021); CSAMT was used because of one High Voltage power line crosses the breccia area ofinterest. The other study shows the investigation of the Strengbach catchment, France (Lajaunie et al. 2022);
CSAMT was used because one telecommunication antenna is on the summit, at one boundary of thecatchment. Papers cited above essentially show CSAMT apparent resistivities and their inversion with shallow
ERT resistivities; a very few information has been shown concerning the underlying CSAMT questions:
Experimental set-up as well as simulation of radiation patterns, data processing, apparent resistivities andinversion will be discussed at EMIW22.
Keywords: Controlled-Source Audio Magneto-Tellurics, CSAMT, Power line noise, radiation pattern

References
Lajaunie M, Gance J, Sailhac P, Malet J-P, Warden
S, Larnier H (2022) Hydrogeological structure ofa granitic mountain catchment inferred frommulti-method electrical resistivity datasets.
Geophysical J. Int. (under review)
Larnier H, Sailhac P, Chambodut A (2018)
Detection and characterization of lightningbased sources using continuous wavelet
transform: application to audiomagnetotellurics. Geophysical J. Int. 212(1):103- 118. doi:10.1093/gji/ggx418
Quesnel Y, Sailhac P, Lofi J, Lambert P, Rochette
P, Uehara M, Camerlynck C, (2021) Multiscalegeoelectrical properties of the Rochechouart
impact structure, France, Geochemistry,
Geophysics, Geosystems 22(9).
doi:10.1029/2021GC010036

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The maximum possible distances to the remote reference when working inmedium and high latitudes
D. Epishkin1*, A. Gubina1, E. Shirokova1, A. Yakovlev1,2
1Nord-West Ltd, Moscow, Russia
2Moscow State University, Moscow, Russia*corresponding author: dmitri_epishkin@mail.ru

SUMMARY
This work compares the maximum possible distances to a magnetotelluric remote reference when operatingin middle and high latitudes. Several illustrative examples show that the maximum distances at which themagnetotelluric fields correlate strongly depend on the latitude. In 2020-2021, we took magnetotelluric
measurements in Yakutia region, on the regional profile 4-SB. Profile length is 900 km. Throughout all the work,
registration was carried out at a fixed remote reference site. We analyzed the efficiency of using a remotereference for sites located at different distances from it (from 0 to 650 km). Also, we analyzed quality ofhorizontal magnetic tensors for sites located at different distances from each other. We used all measurements
along the profile, which made it possible to study in detail the correlation of fields at different distances. The
work carried out showed that at latitudes of about 60 degrees it makes sense to locate a remote reference at
a distance of no more than a few hundred kilometers from the measurement sites. At large distances, thecorrelation between low-frequency field is lost, which must be taken into account when designing work in theselatitudes. When carrying out work in middle latitudes, the distances at which fields correlate are increasing,and it is possible to locate remote reference at distances up to a thousand kilometers.
Keywords: remote reference, horizontal magnetic tensor, data processing

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

New long-period magnetotelluric measurements to improve ground electric fieldmodelling in the UK during geomagnetic storms
E. Eaton1 , J. Huebert1 , C. Beggan1 , A. Montiel-Alvarez2 , A. Thomson1 , C. Hogg3 and D. Kiyan3
1
British Geological Survey, Edinburgh, UK, eleaton@bgs.ac.uk
2
University of Edinburgh, a.montiel@ed.ac.uk
3
Dublin Institute for Advanced Studies, Dublin, Ireland, duygu@cp.dias.ie

S UMMARY
To assess the hazard posed by space weather events to grounded technological infrastructure, such as Geomagnetically Induced Currents (GICs) in the UK and Irish power transmission networks, a key component
is to accurately model how the ground electric field varies during geomagnetic storms. A model of electricalconductivity of the subsurface based on magnetotelluric data can be used for this purpose. For the UK-funded
SWIMMR-SAGE (N4) project, we are currently planning to collect long-period MT data at more than 45 sites
across England, Southern Scotland and Wales with ∼50 km grid spacing over two years between 2021 and2023. Measurements of up to six weeks at each site are made using a rolling deployment of five LEMI-417
systems.
The measured magnetotelluric impedances provide a reliable method to compute the ground electric fields
necessary for the elucidation of GIC estimates in the high voltage power grid model of Britain. Geoelectric fields
can also be used to infer risk to pipelines and rail line infrastructure. Results from some of the 30 sites collected
so far are presented, including magnetotelluric impedances, ground electric field data measured during large
space weather events between May 2021 and June 2022, and improvements to the modelled geoelectric fieldat sites across the UK. Future work in the SWIMMR-SAGE project includes using the improved model ofelectrical conductivity of the subsurface and modelled geoelectric fields to provide nowcast and forecasts ofthe space weather impacts on ground-level infrastructure in Britain.
Keywords: Magnetotellurics, Space Weather

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Hilbert transform of the frequency normalized impedance data: Application to thedispersion relations in magnetotellurics
Ahmet Tugrul Basokur
Lemnis Geosciences, basokur@ankara.edu.tr

SUMMARY
The real and imaginary parts of the Fourier transform of a causal signal can be obtained from each other by
the Hilbert transform. The numerical computation can be carried out by the Fast Fourier Transform or the
convolution of the data with an appropriate Hilbert kernel. However, magnetotelluric data are usually unequally
sampled because the data points with less noise contribution are selected. A method and Python software aredeveloped for the Hilbert transformation of unequally sampled data and applied to the real and imaginary partof the frequency normalized impedance to examine if the dispersion relations are satisfied for a given
impedance tensor. The method involves the reconstruction of the measured data by a linear combination ofan analytic interpolation function distributed along the horizontal axis. Then, the decomposition coefficientsthat provide a fit between the measured and the reconstructed data are solved by the linear least-squares
method with singular value decomposition. Finally, the discrete Hilbert transformed data are constructed at any
desired abscissa values by the sum of the analytic Hilbert transform of the interpolation function using thedecomposition coefficients solved in the previous step. Examples from the magnetotelluric synthetic and field
data, including out-of-quadrant phase cases, are given.
It is demonstrated that the dispersion relations of the frequency normalized impedance function are valid for
all types of structures, including 1D, 2D and 3D cases. A set of theoretical data calculated for a 3D subsurface
by ModEM software and corresponding dispersion curves computed with the suggested algorithm provided
perfect fits. The dispersion relation for the FNI function is also valid for out-of-quadrant phase cases causedby 3D strong resistivity contrasts. However, electrical anisotropy and galvanic distortion could also be
considered to explain the out-of-quadrant phase. Therefore, the validation of dispersion relations in such cases
needs to be investigated.

Keywords: Magnetotellurics, Causal systems, Dispersion relations, Hilbert transform, Unequally sampled
data.

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022thSynchronization optimization providing for MT stations at grid survey
1

1

2

A. Prystai , V. Pronenko and A. Bondarev
LLC “LEMI”, Lviv, Ukraine, pron@isr.lviv.ua
2
Lviv Polytechnic National University, Lviv, Ukraine, bondap@ukr.net
1

SUMMARY
Magnetotelluric (MT) survey is a leading electromagnetic method to probe the electrical conductivity structureof the Earth at depths ranging from few tens of meters to several hundreds of kilometers. As passive
exploration technique, MT involves measuring fluctuations of the natural electric and magnetic fields in wide
frequency range in two orthogonal directions at the Earth’ surface. Recent application of multiple MT stations
survey – remote reference or grid stations location – together with the modern computational technology
allows significantly improve the quality of the results by eliminating noise and improving the signal-to-noise
ratio and, as a consequence, to determine the parameters of Earth’ geoelectric cross-section with a highdegree of reliability.
It has to be stressed that the successful application of MT method greatly depends on the used instrumentation
parameters. For grid survey, especially important is the precise synchronization of measurements
simultaneously in every location. To organize the synchronous operation of all instruments, the systems for
global positioning and time reference - GPS receivers - are widely used. However, despite GPS availability, thenormally obtained synchronization precision is not enough because of the instability of the GPS
synchronization signal, especially in the moments of the GPS signals loss.
To raise the synchronization accuracy, a new structure of the synchronization circuit has been developed. A
linearized model of the circuit was constructed and its computer simulation was carried out. The stochastic
model of a set of synchronization circuits is developed what allowed us to derive the equation of the evolutionof cumulates of the random process of the clock. The computational experiments made it possible to identify
the necessary parameters of the model and then the verification of the results was carried out using thestructure of the broadband MT station LEMI-423. The experimental results confirmed a significant reduction of
the obtained error from the instability of the GPS synchronization signal. As a result, we achieved sampling
timing accuracy better than ± 60 ns, without phase jumps and distortion. To this, what is important for anautonomous field instrument, the obtained power consumption of the synchronization circuit was very low,what allowed us to reach the total power consumption of the LEMI-423 MT station below 1.7 W.
In conclusion, the short description of LEMI-423 MT station and its parameters are given.
Keywords: GPS synchronization, MT station, stochastic model.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Smart data selection – Using machine learning for RMT data processing
A. Platz1, U. Weckmann1,2Helmholtz-Zentrum Potsdam, Deutsches GeoForschungsZentrum, Potsdam, Germany,
aplatz@gfz-potsdam.de2
University of Potsdam, Institute of Geoscienes, Potsdam, Germany

1

SUMMARY
The Radio-Magnetotelluric (RMT) method is a geophysical near-surface imaging technique with a broad rangeof possible applications. The GFZ Potsdam has recently acquired a newly developed horizontal magnetic
dipole transmitter that allows the application of the RMT method even in regions with an insufficient coverageof radio transmitters serving as source signal. First measurements were conducted at three different locationsin Chile in 2020. We have full control over the subsequent data processing by storing the raw time series.
Processing the entire time series has a couple of drawbacks: The different emitted frequencies are transmitted
in a sweep implying that only a smaller fraction of the time series contains the required signal for a particulartarget frequency and leading to an unfavourable signal-to-noise ratio. However, since it is technically
impossible to have the same time base for the data logger and the transmitter with an accuracy of a few
nanoseconds, an automated detection scheme is required to find time segments that contain the transmitter
signal. Usually, several Gigabytes of raw time series are collected during field measurements, making manual
editing and supervision of the time series virtually impossible. However, a careful selection of appropriate time
segments is essential for the success of the data processing. Since the underlying physics are comparable tothose of low-frequency MT, the same tools for statistical time series analysis and data processing can be usedwith partly significant modifications. The processing tools at GFZ include the modular processing suite
EMERALD, which was originally designed for MT processing, but has recently been quickly adapted to process
RMT data. One main difference is that in RMT the transmitter data is considered as signal, while in natural
source MT this would be regarded as (near-field) electromagnetic noise that needs to be removed using
automated robust statistical approaches. To address the challenge, machine learning algorithms have a highpotential to solve both problems. Initial experience was gained with a recurrent neural network approach inorder to identify suitable time segments (Patzer & Weckmann, EMTF 2021 – conference contribution andpersonal communication). However, many questions remained open, e.g. if other machine learning algorithms
can result in better performances, which machine learning algorithms are in principle suitable for thecharacteristics and properties of RMT time series or which parameters should be used as input variables forthe algorithms. A large number of machine learning algorithms exist, which can be divided into different groups
according to their operating principle and their activity fields. We will test unsupervised methods, especially forclustering the data, as well as supervised methods as e.g. logistic regression, support vector machine or neuralnetworks. We will present first results using the RMT data from Chile as training data set.
Keywords: Radio-Magnetotellurics (RMT), Machine learning, Neural networks

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Shaky data and where to find them – MT on frozen lakes
C. Patzer1, U. Autio1 and J. Kamm1
1Geological Survey of Finland, GTK, cedric.patzer@gtk.fi

SUMMARY
Finland is commonly advertised as – a land of thousand lakes – in the media and public. However, in reality
there are approximately 168 000 lakes covering up to 10% of the land area of the country. This large numberof lakes can seriously obstruct planning and execution of any geophysical (in particular EM) fieldwork.
Within the BATCircle2.0 project (funded by Business Finland) GTK is currently collecting 3D MT data in northeastern Finland north of the city of Kuusamo. In the centre of the study area lies the 240 km2 lake Yli-Kitka
which by applying traditional MT fieldwork procedures would cause a large gap in station coverage. As Finnish
lakes reliably freeze over in winter, we used this opportunity to conduct a pilot study on the feasibility ofmeasuring MT on the lake ice.
In beginning of February 2022, we installed 5 MT sites on the frozen lake Yli-Kitka with a site spacing of 2 km.
For reference purposes we installed an additional site on land directly at the shoreline of the lake. While weobtained good data quality at the land site, we found very poor data quality between Periods of 100 s to 1 s at
all lake sites. Closer inspection shows that this poor data quality is caused by a spurious signal on the magnetic
channels. The measured signal is approximately 2 orders of magnitude larger than the natural MT signal
simultaneously recorded at the land site, while the electric field channels seem to be unaffected by this effect.
This spurious signal is likely caused by movement of the lake ice due wind. This effectively prohibits standard
single site processing of the measured time series to obtain MT transfer functions. Our findings are inaccordance with McNeice and Jones (1998). One way to deal with such “shaky data” is to use magnetic fields
from the land site to compute MT transfer functions. We are also discussing other options to remove or filter
the spurious signal from the time series itself prior to any transfer function estimation.
Despite these challenges and difficult conditions in the field we found this pilot study to be successful. It showsthat with some care it is possible to collect broadband MT data on frozen lakes. Allowing for easier survey
planning with lake sites measured in winter and land sites during summer. In addition, being able to perform
fieldwork during wintertime may also allow access of large frozen swamp areas which may not be accessible
during summer months.
Keywords: MT, time series

McNeice, G. and A.G. Jones, 1998. Magnetotellurics in the frozen north: measurements on lake ice
Contributed paper at: 14th EM Induction Workshop, Sinaia, Romania, August 16-23.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Results of FDEM-(CS)AMT test studies on the Alexandrovka area
E.Ermolin1, N.Zorin2, D.Epishkin2 and D.Sapunov1
1 "GM-Service" LTD, geophysmethod@gmail.com
2"Nord-West" LTD, mail@nw-geophysics.com

SUMMARY
The article presents the results of electrical surveying acquisition tests carried out in the winter of 2021 in thearea of the educational and geophysical base of the Faculty of Geology of the Lomonosov Moscow State
University "Alexandrovka" in the Kaluga region. The purpose of the experimental and methodological work
was to obtain data in the "dead" frequency range of the audio magnetotelluric sounding (AMT) range. The
paper describes the methodological features of the work performed by the method of frequency domain
electromagnetic sounding (FDEM) and AMT with a controlled source (CSAMT). The obtained result iscompared with a priori geological information, with geophysical studies of previous years. Some methodical
features of test studies have been described in this paper.
Keywords: audio-magnetotelluric method, frequency-domain electromagnetics, skin-effect, resistivity

INTRODUCTION
The authors of this article performed test surveys of
AMT and FDEM. The main purpose and objectivesof these studies are as follows:• combining the AMT technique with the method of
frequency domain electromagnetic sounding(FDEM) and AMT with a controlled source (CSAMT)with the possibility of using the same set of
equipment to record data from all methods;• obtaining CSAMT data in areas complicated bycultural noise, as well as in the "dead" frequency
range of AMT;
• obtaining data on the transverse resistanceparameters of insulator layers based on theinterpretation of the individual components
frequency characteristics (curves) of frequency
sounding.
Methods
Frequency domain electromagnetic sounding
method providing information about changes in theresistance of the medium with depth by studying thefrequency dependencies of the components of theelectromagnetic field created by the artificial source.
In this paper, a horizontal electric dipole was used
as a source. In a medium with resistivity ρ, thecomponents of electric and magnetic fields in the farzone (ωμ0r/ρ≫1) of a source of HED type with
dipole moment PAB = IAB · AB, oriented along the xaxis, are expressed as follows (Modin et al. 2018):Ex =

PAB (3 cos 2 φ − 2)
∙ ρ,
πr 3

Hy =
Hz =

PAB (3 cos 2 φ − 2)
∙ √ρ,
πr 3
√−iωμ01

1

3PAB sin φ

∙ ρ,
(1)
2πr4
Ex
Zxy =
= √−iωμ0 ρ,Hyr – distance from source, φ – azimuth of themeasurement point. Since the method works in the
far zone, it is necessary to use a maximum spacing
and a maximum current to perform measurements,
which in turn leads to the need to use power
generators and good grounding (<30 Ohm ∙ m).
−iωμ0

To achieve the objectives, the authors made anassessment of the parameters of the installationand practical measurements. Experimental andmethodical works were performed on the territory of
the scientific and educational geophysical base of
the Lomonosov Moscow State University geologicalfaculty "Alexandrovka" in Kaluga region.
AMT field measurements were performed withmeasurement of five components of theelectromagnetic field. NORD stations (Nord-WestLTD) were used as measuring devices, IMS 5
sensors (Vega LTD, Nord-West LTD) were used toregister the magnetic field components, providing
operation in the frequency interval from 1 Hz to 20
kHz. The electrical components of the field wererecorded using nonpolarizing graphite electrodes
NW-4 (LTD "Nord-West"). The works was
performed using a cross-shaped system with acentral electrode Figure 1. The length of eachelectrical line was 100 m (2 legs of 50 m). Azimuthsof system of electrical lines and horizontal magnetic
sensors: x – 73º, y – 163º. The x-axis is directed
parallel to the HED. Induction sensors (two
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Ermolin E. et al., 2022, Results of FDEM-(CS)AMT test studies on the Alexandrovka area

horizontals, one vertical) were buried in the ground.
The electrodes were grounded in pits 0.2–0.3 m
deep in a mud mixture moistened with salt water.

Figure 1. Measuring system for AMT, FDEM и
CSAMT.
Evaluation of system parameters
Works by the FDEM are performed in a far zone
condition corresponding to (ωμ0r/ρ≫1). In practice,in order to achieve the far-zone conditions, so thatthe distance from source exceeds the effective
depth of the skin layer by several tens of times,
using the very low frequency (Modin et al. 2018).
The depth of the skin layer is calculated using theformula:
hδ ≈

√ρT
2

(2)

(km)

Let's estimate the distance satisfying the far zone
condition for a structure with an average electricalresistivity of the order of 150 Ohm∙m. This resistivity
was determined by the results of acquisition tests
performed by the authors in 2019 (Ermolin et al.
2020). The final stack according to the results ofacquisition tests in 2019 is shown in Figure 2. The
curve in Figure 1 is measured in the same area. A
minimum at a frequency of 50 to 100 Hz
corresponds to a conductor at an altitude of 0 to 100 meters in Figure 3. Thus, to study the "dead"frequency range, information is needed on the
average electrical resistivity of rocks located above
the altitude of 0 m. The average level of resistancein the depth range is an average of 150 Ohm ∙ m.

Figure 2. Electrical model from 2D inversion data
AMT and ERT-IP (Ermolin et al. 2020).
The length of the transmitter line is 1150 m. The
grounding resistance of the AB line is 30 Ohms,
which made it possible to use a current of up to 7 A.
The configuration is shown in Figure 3. The same
figure shows the position of the profile along which
the electrical model is built, shown in Figure 2.
When performing the work of the FDEM, the mosttime-consuming is the organization of the HED(work shift of 4 operators), while the installation timeof the measuring device is from 15 to 45 minutes (bytwo operators).

In order to close the "dead" range of AMT, it isnecessary to obtain high-quality data for thefrequency of 800 Hz and above. The depht of theskin layer at 800 Hz can be estimated as:

hδ ≈

√ρT
2

√

=

150
800

2

≈ 0.2 (km)(3)

As a result, a distance from the source of 6 km wasused for work by the FDEM, providing therealization of the far zone condition (ωμ0r/ρ≫1).

Figure 3. Scheme of acquisition tests perfomed
Due to the absence of time limits, all possible

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Ermolin E. et al., 2022, Results of FDEM-(CS)AMT test studies on the Alexandrovka area

frequencies of the transmitter GER-1000 from 1.22
Hz to 5000 Hz were used. The total time to use all
frequencies (1.22, 2.44, 4.88, 9.77, 19.53, 39.06,
78.13, 156.25, 312.5, 625, 1250, 2500, 5000 Hz)
was 15 minutes. A problem occurred while working
with the transmitter. Due to the complex characterof the transmitter line resistance at high frequencies,
the output current had to be reduced to 3-4 A andthe signal lost its rectangular shape, characteristicof a stabilized meander at low frequencies. An
example of an unstabilized current is shown in
Figure 4.
The lack of current stabilization is not a problem for
the CSAMT method, as well as the classical FDEM,
provided that the factual waveform in the transmitterline is registered witch a Hall sensor and taken into
account during processing.

Figure 6. Component Hy, PK 01

Figure 7. Component Hz, PK 01

Figure 4. Example of transmitter line current record
for frequencies: a) 1.22 Hz b) 1250 Hz c) 5000 Hz
Results
The result of processing of the FDEM field data are
the curves of the electrical (Ex), magnetic
components (Hy, Hz) and impedance (Zxy), shownin Figures 6-9.

Figure 8. Component Zxy, PK 01
To interpretation the data of FDEM and CSAMT,
selection was applied within the framework of 1Dmodels, using the program CsmtSolver (author
Dmitry Epishkin). An example of the selection
performed for the CSAMT of the Zxy impedancecomponent is shown in Figure 10.

Figure 5. Component Ex, PK 01

Using a priori geological information the following
geological interpretation of the model can be carriedout at measurement point 01. The first three layers
are interbedding of sands and loams. Next, a more
conductive layer is clay with inclusion of coals of theTula formation and Bobrikovian horizon of the Lower
Carboniferous.

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Ermolin E. et al., 2022, Results of FDEM-(CS)AMT test studies on the Alexandrovka area

Conclusion
As a result of the acquisition tests, it was shown thatthe application of the FDEM allows obtaining data in
the "dead" range of AMT for a component parallel tothe direction of HED. In this experiment, the levelsof the AMT and CSAMT curves did not coincideaccording to the measurement results.

Figure 9. Selection of the geoelectric model by the
Zxy impedance of frequency sounding in the
CsmtSolver program window. PK 01.
Below is a high-resistivity layer represented bylimestones, dolomites and marls of the Upper
Devonian. This formation in the model isrepresented by the fifth, sixth and seventh layers.
This range of resistances was selected to accountfor vertical anisotropy. The conductive layer isconnected to the aquifer, the layers with highresistivity correlate with gypsified limestones, marlsand dolomites. The eighth layer is terrigenous
sediments, represented by clays, mudstones andsands of the Middle Devonian. The ninth layer is aconductive layer of Vendian sandstones withinterbedded clays in which high-salt water is present.
The tenth layer is a crystalline basement with highresistivity, composed of crystalline shists andgneisses of Lower Proterozoic and Archean age.
The result of AMT field data processing are splines.
Figure 10 (left). At the next stage, a 1D inversion
was performed using the AMT invariant curve
Figure 10 (right). The results in Figure 10 (right) do
not contradict the results of 2019 (Ermolin et al.
2020).

In this test studies, it was not possible to perform anappropriate selection of the 1D resistivity model for
the Ex component of the FDEM. But the results of
the 1D interpretation of the AMT and CSAMT data
are comparable. According to CSAMT data insulator
layers (dolomites with interbedded gypsum) have ahigher resistance value than according to the resultsof the interpretation of the AMT curve. Thus, the
CSAMT results most correctly reflect the highresistances of gypsified dolomites, the resistivity ofwhich exceeds tens of thousands of Ohm·m.
Importantly, it is necessary to register the transmitterline current when performing FDEM, because thesignal at frequencies above 1000 Hz is not a correct
meander.
References
Berdichevsky MN and Dmitriev VI. [2009] Models
and methods of magnetotellurics. Book: ScientificWorld, 2009. 680 p.
Chave A.D., Jones A.G. [2012] The magnitotelluric
method – theory and practice. Cambridge University.
Press, 2012. 552 p.
Ermolin, E.Y., Ingerov, O., Yankilevich, A.A.,
Pokrovskaya, N.N. [2019] AMT soundings in thedead band within the chukotka region (Russian Fareast). Journal of Mining Institutethis link is disabled,
2019, 236, p. 125–132
Ermolin, E., Kulikov, V., Melnikov, A., Asoskov, A.
[2020] Features of experimental-methodical
electrical exploration studies at the MSU training
area (Aleksandrovka village). Engineering andMining Geophysics 2020 - 16th Conference andExhibition (Abstract)
Modin I.N., Yakovlev A.G., Pushkarev P.U..,
Shevnin V.A. and others. [2018] geoelectric survey:
a manual on electrical survey practice for students
of geophysical specialties. Volume 1 - 2nd ed.,revised. and ex. - Tver: "PolyPRESS", 2018 - p. 274

Figure 10. Example spline (left) and 1D inversion(left) smooth Occam inversion (model marked inpink) and eight-layer model (in green).

Rokityansky I.I. [1975] Investigation of electricalconductivity anomalies by geomagnetic-variation
profiling. Kiev: "Scientific invention". 1975, p. 279
Vozoff K. [1972] Geophysics. 1972, 37, p. 98-141

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

DISTRIBUTION OF SOURCE EFFECTS IN THE HIGH LATITUDE MAGNETOTELLURIC
DATA
Sarasija Sanaka1 Anne Neska1
1Institute of Geophysics Polish Academy of Sciences, Warsaw, Poland.,ssanaka@igf.edu.pl

SUMMARY

The magnetotelluric (MT) and related passive electromagnetic induction methods work on the condition that the
far-field assumption is fulfilled by the source signals. Whereas possible violations of this condition due toanthropogenic signals have been understood well, it becomes more and more evident that part of the naturalelectromagnetic signals is not homogeneous either, and unlike told in textbooks these near-field effects are notlimited to polar and equatorial electrojet regions or the Sq variations with their harmonics. Such evidence comesmainly from long-term (monitoring) data, which results in transfer functions and induction arrows. The latter ones
are most affected by source effects which expresses itself in the fact that they are not constant in time in a waythat cannot be explained by changes in subsurface conductivity. To assess this problem, we have considered
four years long time-series data of 15 stations from the IMAGE (International Monitor for Auroral Geomagnetic
Effects) network. We have processed these data with Egbert’s processing and also applied the Remote
Reference Technique, which is necessary to eliminate cultural noise. Preliminary results clearly show temporalchanges. In the induction arrows, the nonstationarity behaviour is prominently seen in the high but also in midlatitude regions. The spatial and temporal pattern these source effects are exhibiting are partly familiar, inparticular for seasonal changes in mid-latitude, and partly not understood well yet. We will further investigate
them with the aim to identify their generation mechanism and to recommend ways to avoid MT results being
influenced by them.

Keywords: Source effects, Seasonal changes

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022th

On the correctness of using plane-wave assumption and two-channel acquisition
systems in MT exploration at high latitudes
1

2

1

2

D. Yakovlev , E. Pogrebnykh , D. Epishkin and A. Yakovlev
STC Nord-West, stcnordwest@gmail.com, Moscow, Russia
2
Lomonosov Moscow State University, Moscow Russia

1

SUMMARY
In 2019 about 5,000 MT sites were acquired during an MT survey in the Russian Arctic across the areaof 120x80 km with a uniform site distribution over the network of 1x2 km. A single remote site was used for the
remote reference processing. In the summertime we got a record at the reference site with duration for morethan three months. These data allow us to better understand the applicability of the plane-wave assumption insuch regions, since some experts suggest that application of classic Tikhonov-Cagniard source model in polar
latitudes could lead to erroneous results due to the peculiarities of the local MT field source and proximity to it.
The whole record at the reference site was processed by 1-day fragments. Careful analysis of thefrequency-time sections and graphs of variations of amplitude and phase of the impedance for 3 months, andgraphs of their spectral density, we conclude that in the area of work located about 72° N the source-effect on
the impedance tensor estimations is insignificant. Classic data processing based on plane-wave assumption
yielded stable values of the impedance tensor and MT curves.
Maps of the horizontal magnetic tensor for several characteristic frequencies showed that within the
study area the distance at which the magnetic field varies for less than 5% in amplitude and 3° in phase isabout 2-3 km. Within this distance it is possible to consider the magnetic field recorded at an adjacent station
as that recorded at the survey site, which could be employed for data processing acquired by two-channel MT
acquisition systems.
Keywords: the plane-wave assumption, high latitudes, two-channel acquisition system

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Measurement of noise characteristics of graphite electrodes in the field andcomparison with other types of non-polarizing electrodes
D. Epishkin1, N. Zorin2
1STC Nord-West, dmitri_epishkin@mail.ru
2STC Nord-West, nikita.zorin.geophys@gmail.com

SUMMARY
Graphite electrodes have been successfully used in MT measurements by a number of Russian companies
for decades. This type of electrode has proven itself extremely well in practice. During the continuous use ofgraphite electrodes, an optimal design was developed, which made it possible to achieve a low level of noiseand high reliability in various climatic conditions. However, up to the present time, this type of electrodes has
hardly been mentioned even in large western review papers. This work aims to correct this omission. In thecourse of this work, a technique for measuring electrode noise was developed and an attempt was made toinvestigate the noise characteristics of graphite electrodes, as well as to compare them with other types ofelectrodes. It was found that the noise level of graphite electrodes is slightly lower than that of the widely used
Pb-PbCl2 and Cu-CuSO4 electrodes. Also, this work demonstrates the importance of proper electrodemaintenance for the best performance.
Keywords: magnetotelluric, non-polarizing electrode, parallel test

INTRODUCTION
Electrodes are the most critical component in themeasurement of the telluric response, and indeed
are often the weakest component of any MT
system. If reasonable care is taken with all otheraspects of the electric measurements, then it is theelectrode noise that limits the quality of the electricfield measurements (Petiau & Dupis 1980).

In a simplified form, graphite electrode is basicallyjust a graphite rod placed in Zn-graphite powder(Figure 1). But, obviously, to make a high-quality
electrode, it is important to comply with a number ofconditions. Among other things, it is important tomonitor the quality of all contacts. For example, thecontact between the rod and cable must be wellisolated from the environment; the shell of theelectrode must be well permeable to moisture, etc.

Most land MT surveys in the world are carried out
with porous-pot Pb-PbCl2 or Cu-CuSO4 electrodes.
In addition to these two types of non-polarizing
electrodes, in the post-Soviet space, a number ofgeophysical companies actively employ graphite
electrodes. Our numerous comparative tests andmany years of practical experience show thatgraphite electrodes have an extremely low noiselevel and give good results over a wide frequency
range under a wide range of temperature andhumidity conditions.
This type of electrodes is not widely used outside
Russia. Moreover, in a number of publications it isnoted that graphite electrodes have a high noiselevel comparable to the level of the telluric field and,as a result, their use is not recommended for MT
surveys (e.g., Petiau 2000; Ferguson at al. 2012).
This contradicts our experience in the practicalapplication of electrodes of various types.
The production technology of graphite electrodes
was developed by Soviet geophysicists at the dawnof the MT method, and since then this type ofelectrode has not lost its relevance.

Figure 1. Graphite electrode.
If all conditions are met, then you can get a sensor
with decent characteristics.

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Epishkin D, Zorin N, 2022, Measurement of noise characteristics of graphite electrodes

The main goal of this work is to study the noisecharacteristics of graphite electrodes and comparethem with other types of non-polarizable electrodes.
Another important point is to develop a suitable
technique of measuring electrode noise in the field.

observed for different electrical lines and can be
estimated using a simple formula:

MEASUREMENTS AND PROCESSING

For the noise function obtained this way, we havecalculated the spectral density in a wide range oftimes (from 0.0001 to 1000 seconds). Examples ofspectral density for different types of electrodes are
given in Figure 3.

Comparative tests involved three types of widelyused electrodes: graphite (manufactured by the
Nord-West company), Cu-CuSO4 and Pb-PbCl2(both manufactured by internationally recognizedgeophysical companies). We choose 8 electrodesof each type. All electrodes were brand-new andproperly serviced before use.

𝑁𝑜𝑖𝑠𝑒(𝑡) =𝐶ℎ1 (𝑡) − 𝐶ℎ2 (𝑡)2(1)

To estimate the noise, a parallel test technique wasapplied. Electrodes of each type were grouped into
pairs. Each pair formed an electrical line 1 meter
long. All electric lines were located in parallel at asmall distance from each other. Figure 2 shows two
setups for measuring electrode noise (2 pairs ofgraphite electrodes installed on the left, and 2 pairsof Pb-PbCl2 electrodes installed on the right).

1 meter

Figure 3. Noise spectral density of different typesof electrodes after 24 hours of recording. Raw data
(before applying the robust spline approximation).

Figure 2. Simultaneous noise measurement for
two types of electrodes. Left setup for graphite
electrodes (4 electrodes), right setup for Pb-PbCl2
electrodes (4 electrodes)
To study the stability of the characteristics of theelectrodes, the signals were recorded periodicallyfor 2 weeks.
Since the electric lines are very short, parallel andhave the same length, the measured signals due toelectric field in the ground are weak and roughly thesame. Noise is the difference between the signals

Figure 4. Robust spline approximation. Black line –raw spectra, blue line – spectra after industrial
frequencies filtration, red line – smooth spline
approximation
The place chosen for observations was located in
the area of influence of industrial noise, therefore,
distorted values were obtained at certain
frequencies (50 Hz and harmonics). These peaks
are not related to the self-noise of the electrodes;
therefore, to obtain the final result, they were
filtered. Then, spline approximation was carried out
for the remaining values. This stage is shown in
Figure 4.

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Epishkin D, Zorin N, 2022, Measurement of noise characteristics of graphite electrodes

RESULTSCONCLUSIONSAs noted earlier, in order to obtain reliable noise
estimates for each electrode type, it is necessary toensure that each of the electrodes is in goodcondition and properly maintained. Figure 5 shows
how the noise level of new (initially dry) graphite
electrodes depends on the time elapsed after theirfirst installation in moist soil. More than 12 hours
should pass before the dry electrode starts to givethe best results.

We believe that graphite electrodes deserve more
attention of the international EM community thanthey used to have in last decades (e.g., see Lu andMacnae 1998). The presented work is the first stepin our planned study of the characteristics of suchelectrodes. It is necessary to carry out long-termmonitoring noise measurements and determine
how the noise characteristics of the electrodes will
depend on temperature, humidity and other factors.
However, according to the measurements alreadytaken, it can be said with confidence that properly
manufactured graphite electrodes demonstrate anoise level low enough to carry out magnetotelluric
measurements in a wide frequency range. In all ourobservations, the noise level of graphite electrodes
turned out to be consistently lower than that of thewidely used Pb-PbCl2 and Cu-CuSO4 electrodes.
Also, the graphite electrodes have a low production
cost and are more environmentally friendly thanmost of metal-electrolyte electrodes, which makes
them a very attractive option for MT exploration.

Figure 5. Change in noise level of a dry graphite
electrode over time when placed in moist soil. Bluelines demonstrate noise levels for different times
since installation. Gray line corresponds to receiver
self-noise.

In addition, our measurements have demonstrated
that the telluric field registration quality strongly
depends on timely electrode maintenance. Before
starting work, dry (new) electrodes should be kept
in moist soil for about 1 day. This will provide thebest measurement quality. This observation applies
to any type of electrodes.
REFERENCES
Ferguson IJ (2012) Instrumentation and fieldprocedures. In: Magnetotelluric method. Theory
and Practice, ed. by Chave AD and Jones AG.
Cambridge University Press, pp 421–479
Lu K, Macnae J (1998) The international campaign
on intercomparison between electrodes forgeoelectrical measurements. Explor. Geoph. 29:
484-488.

Figure 6. Comparison of the self-noise level of
non-polarizable electrodes of various types
Figure 6 shows typical levels of noise of electrodesof different types. The highest noise level was
consistently observed for Cu-CuSO4 electrodes.
Graphite and Pb-PbCl2 electrodes both show
noticeably better results quite similar to each other;
but the noise level of graphite electrodes is slightly
lower in the entire frequency range.

Petiau G & Dupis A (1980) Noise, temperature
coefficient, and long time stability of electrodes
for telluric observations. Geophys. Prosp., 28:
792–804
Petiau G (2000). Second generation of lead-lead
chloride electrodes for geophysical applications.
Pure Appl. Geophys., 157: 357-382

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Aurora: An open source magnetotelluric data processing package in Python linking MTH5 to
EMTF XML

Aurora: An open source magnetotelluric data processing package in
Python linking MTH5 to
EMTF XML†

K.N. Kappler1, G. Egbert2, A. Frassetto3, L. Heagy4, A. Kelbert5, L. Keyson3, D. Oldenburg4, J.R.
Peacock5, and T. Ronan3† Authors are listed alphabetically except the corresponding author.

Independent, karl.kappler@berkeley.edu
2Oregon State University
3
Incorporated Research Institutions for Seismology (IRIS)
4
University of British Columbia
5
United States Geological Survey
1

SUMMARY
Aurora is a software package that calculates robust estimates of electromagnetic transfer
functions (EMTF) from magnetotelluric time series data. It is being developed for two primary
purposes: first, to streamline the generation of high-quality, exchangeable EMTFs for archiving
at the IRIS data center and second, to provide an open-source software package to the EM
community that has been benchmarked against the legacy Egbert’s EMTF codes. Aurora is a
Python representation of Egbert's latest EMTF Matlab code that is designed to interface withthe self-descriptive MTH5 data structure for magnetotelluric time series. We use Numpy,
Pandas, Scipy, and Xarray as package dependencies. Comprehensive tracking of data
provenance is provided within the pipeline via Xarray. Data products are outfitted with MTH5
metadata which can be converted to FDSN StationXML for surveys and EMTF XML for transfer
function archiving. The Aurora software package, which includes worked example datasets, will
be released via both PyPI and Conda-Forge in the autumn of 2022. We present an overview ofthese codes and outline a path forward over the following 1-2 years. The longer term vision
includes capability for time and frequency domain data cleaning, data visualization, and data
QC as well as novel array deployment geometries and controlled source data processing. We
invite members of the community to join us in using these codes and collaborating on their
future development.

Keywords: Magnetotellurics, Processing, Open Source, Python, Transfer Function

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Open-source Python software for the visualization of magnetotelluric data
and three-dimensional resistivity models
Ahmet Tuğrul Başokur
Lemnis Geosciences, basokur@ankara.edu.tr

SUMMARY
I present an open-source Python software (MTdmv) for MT data and three-dimensional (3D) modelvisualization that is available for academic use. The code can be directly run from a Python console without
installation. The matplot.lib is the main library to create plots and picture files. The Google Earth ‘kml’ files canbe produced from the edi files, or the coordinates and information in the edi files can be changed from a text
file. The data visualization consists of plotting the frequency normalized impedance, apparent resistivity
definitions, and Schmucker and Bostick transformations. The mentioned MT quantities can also be plotted as
pseudo-section and maps for the measured and model response data. The real and imaginary parts of thefrequency normalized impedance can be estimated from one another using the Hilbert transform to validate
the dispersion relation.
The model visualization part could read ModEM and other data formats and shrink, modify and resave in
different file formats. The extraction of a resistivity section in any direction can be achieved by defining thecoordinates of two points or a point and angle pair. A resistivity section can be created along a crooked line,
such as a seismic line through the coordinates given in a text file. The resistivity variation with depth can beplotted for a given coordinate. The model maps can be superimposed on Google Earth images. The software
could automatically create all possible maps and sections for a given attribute.

All colourmaps contained in matplot.lib library can be activated. The picture files can be saved in pdf, jpg andall other formats supported by matplotlib and in any desired resolution. Furthermore, the image files can beobtained in any language after adding someone’s native language to the language text file.

Keywords: Magnetotelluric, Python, Data visualization, Model visualization, Three-dimensional visualization.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The effect of radar trace number on tomographic images of cylindrical objects
obtained using GPR
O. Apaydın1, T. İşseven2, Y. Çıtır3, S. Paker4 and I. Erer5
1Istanbul Technical University, Department of Geophysical Engineering, apaydinor@itu.edu.tr
2Istanbul Technical University, Department of Geophysical Engineering, isseven@itu.edu.tr
3ARAR Oil and Gas Exploration Production Marketing Inc., ycitir@arar.com.tr
4Istanbul Technical University, Department of Electronics and Communication Engineering,spaker@itu.edu.tr
5Istanbul Technical University, Department of Electronics and Communication Engineering, ierer@itu.edu.tr

SUMMARY
Ground penetrating radar (GPR) method is a non-destructive method that has been used in geological,
archaeological, military and engineering researches for many years. GPR can be used to investigate structuresat different depths, from several centimetres to a few hundred meters. In recent studies, it has also been used
to image the internal structures of objects with shallow depths, such as tree trunks and historical buildingcolumns. With GPR, discontinuities, cavities and cracks in these structures can be detected. While
investigating the internal structures of cylindrical objects, measurements are taken around the objects with
GPR at certain angle intervals along a circular line. And by using the radar traces taken from these different
angles in the backprojection method, tomographic images showing the interior of cylindrical objects are created.
Vector network analyzer (VNA) and Vivaldi antenna pair are used to take radar measurements. The
measurement system is fixed at one point and the object to be investigated is placed on a table that can berotated with certain angle intervals around its own axis. Thus, measurements are taken at desired angle ranges.
In this study, the effect of radar traces collected with different measurement intervals on tomographic images
is compared and examined.
Keywords: GPR, backprojection, cylindrical objects

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th

Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Full correction of the electric field data biased by the ECR-effects
1,2*

N. Zorin

1

1, D. Epishkin , D. Yakovlev and A. Yakovlev

2

1

STC Nord-West, Moscow, Russia
Moscow State University, Moscow, Russia*
corresponding author; nikita.zorin.geophys@gmail.com
2

SUMMARYElectrical exploration on frozen or stony soil is associated with high difficulties in achieving good qualityof electrode grounding. In such conditions, the results of high-frequency electric field measurements are often
biased due to the electrode contact resistance (ECR) effects, i.e., the capacitive leakage between wires andground and voltage division at the receiver’s input. In this paper we discuss a new method of full analyticalcorrection of the ECR-distorted data.
Keywords: poor electrode contact, capacitive leakage.

INTRODUCTION
High contact resistance of grounded receiving
dipoles can significantly distort alternating electricalfield measurements in the ground (Vishnyakov andVishnyakova 1974; Zonge and Hughes 1985).
These distortions are mostly due to various kinds ofcapacitive effects in the receiving circuit, which
increase with operating frequency. Thus, in the DC
resistivity, induced polarization and magnetotelluric(MT) methods, electrode contact resistance up to
10-20 kOhm is usually considered to be acceptable,
while in the audio-MT (AMT) and controlled-source
EM induction (CSEM, CSAMT) methods it isgenerally recommended to achieve values lessthan 1 kOhm.
In many cases (for example, when working on
ice, frozen, sandy and rocky soil), making a dipolewith less than 10-20 kOhm grounding resistance isvery labor-intensive, not to mention the almost
infeasible values of 1 kOhm or less. Unfortunately,electrical explorations with the above methods areoften carried out exactly in such unfavorable
conditions, especially typical for the Polar Regions.
Thus, better understanding of the electric fielddistortions caused by poorly grounded electrodes,
as well as elaborating new ways to solve thisproblem, is a relevant task for EM scientists.
Below we present a novel approach of fullanalytical correction of the ECR-distorted electricalfield data.
RECEIVING DIPOLE MODEL
To quantify the distortions associated with highgrounding resistance, or, as called by Zonge andHughes (1985), ECR (electrode contact resistance)
effects, let us review the equivalent circuit of agrounded receiving dipole (Figure 1).

The following symbols are used in the figure:( ) is the grounding resistance of the( ) is the capacitanceelectrode M (N);between the cable M (N) and the ground;is thecomplex input impedance of the receiver;is thedistance between the electrodes;
is the electricfield in the ground; andis the observed voltage
at the receiver’s input.
andare related asfollows:
0.5 +(ω) = (

0.25
1 + 𝑖ω
1+

0.25
1 + 𝑖ω
(ω)
(ω)
+

)

(ω),

where 𝜔 stands for the angular frequency; 𝑖stands for the imaginary unit;=+
isthe complex contact impedance of the dipole MN;⁄(1 + 𝑖ω),).
== ⁄(1 + 𝑖ω
Separating in this expression the coefficient𝐾
due to wire-to-ground capacitive leakage fromthe coefficient 𝐾 due to voltage division at thereceiver’s input, we get:(ω) = 𝐾 (ω)𝐾𝐾 (ω) =

𝐾(ω) = 0.5 +

(ω)

(ω),

(ω)
,
(ω) +
(ω)
0.25
1 + 𝑖ω

+

(1𝑎)
(1𝑏)

0.25
1 + 𝑖ω

.

(1𝑐)

For a perfectly grounded dipole Equation 1
yields 𝐾 ≡ 𝐾 ≡ 1, and the observed voltage
is related to the measured electrical fieldwith asimple expression:
(ω) =

(ω).

(2)

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Zorin, N. et al., 2022, Full correction of the electric field data biased by the ECR-effects

In a real grounded dipole, the voltage recordedby the receiver is always less than the expectedvalue (2), and the degree of bias is governed by thevalues of complex coefficients 𝐾
and 𝐾 .
CORRECTION OF BIASED DATA
A known way to deal with ECR distortions due
to voltage division at the receiver’s input is their
partial correction based on the estimates of=+values obtained in situ at eachsurvey site. It is especially important for receivers
with high input capacitance (and hence low input
impedance values at high frequencies). For this,many modern MT/AMT stations are capable toautonomously measure thevalue, which issaved in the device memory and then taken into
account at the stage of data processing by
⁄( +
)≈𝐾 .
normalizing the results by
A natural development of the method of partial
data correction described above is their fullcorrection according to Equation 1 with in situ
estimates of all necessary parameters –, ,and. Such estimates can be obtained using asimple portable LCR-meter or a specially designed
impedance meter using the “auxiliary grounding”
technique (Bursian, 1972). For this purpose, it isrequired to ground an auxiliary electrode A at a
sufficient distance from electrodes M and N andmeasure the impedance values, 𝐴 andof the three obtained dipoles at two (at least)𝐴
frequencies: ω1 and ω2 ≫ ω1 . Then, using thefollowing formulas, we estimate the impedancevalues of the half-dipoles M and N:(ω𝑖 ) =
(ω𝑖 ) =

(ω𝑖 ) +𝐴(ω𝑖 ) −𝐴(ω𝑖 ) −2(ω𝑖 ) +
2

𝐴

(ω𝑖 )

𝐴

(ω𝑖 )

,

.

(2а)

(2𝑏)

Then we solve with regard to the unknowns,,
andthe obtained determined (with𝑖 = 1,2 ) or over-determined (with 𝑖 = 1,2, )equation systems:(ω𝑖 ) =
(ω𝑖 ) =th

1 + 𝑖ω𝑖
1 + 𝑖ω𝑖

,

(3а)

.

(3𝑏)

Finally, for all operating frequencies, using
Equation 1 and known complex input impedance ofthe receiver we calculate the distortion coefficients
𝐾 and 𝐾 , and then divide by them the obtained
values of the electrical field after processing.
CONCLUSIONS
There are two principal ways of solving the
problem of biased data in EM prospecting: 1) – toeliminate/minimize the distortion value in the field,
and 2) – to correct the distorted data later at theprocessing/interpretation phase. In this paper weconsider the second approach, which involves thein situ estimation of one or more parameters of thereceiving dipole with further partial or full correctionof the biased data during processing.
The proposed approach of full data correction
may help to greatly expand the range of maximum
permissible values of electrode contact resistivities
for many EM methods with little additional effort.
Note that in the capacity of an auxiliary electrode A
one may use the central electrode of the receiver (ifany) or, in MT/AMT methods – any electrode of theorthogonal receiving dipole.
REFERENCES
Bursian VR (1972) Theory of electromagnetic fields
used in electrical exploration, 2nd ed., rev. andadd. Nedra, Leningrad [in Russian].
Vishnyakov AE, Vishnyakova KA (1974) Field
excitation and measurement in electricalprospection. Nedra, Leningrad [in Russian].
Zonge KL, Hughes LJ (1985) Effect of electrodecontact resistance on electric fieldmeasurements, Expanded Abstr. Technical
Programme of 55th Ann. Intern. SEG Meeting,contrib. MIN 1.5. Tulsa. OK. pp. 231–234.

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Figure 1. Classic receiving dipole (a) and its equivalent circuit (b).

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022th

Cloud connected low power, low noise systems for LMT & MT
1

2

1

V. Pronenko , K. Strack , and A. Prystai
LLC “LEMI”, Lviv, Ukraine, pron@isr.lviv.ua
2
KMS Technologies, Houston, USA, kurtstrack@me.com
1

SUMMARY
Over the past years, we developed and field tested two easy to operate low power, low-noise magnetotelluric
systems, LEMI-423 (wideband) and LEMI-424 (long period). Recently, both systems were adapted to beconnected via a noise-free web access box for continuous data streaming allowing near real-time remotereference application.
The key feature of both systems is the low noise amplifiers and the sensors. The LEMI-424 is used already inmany countries for low-frequency magnetotelluric work and past field experience has shown their high fieldreliability. The system is also used for magnetic studies of the magnetosphere. The sensor has a noise level of
10 pT/sqrt(Hz) at 1 Hz. Other sensors can also be connected. The entire system weighs less than 3 Kg (plusenvironment-friendly non-polarized electrodes). Its total power consumption is less than 1 W.
The sister system is the LEMI-423 which addresses the standard magnetotelluric band from 0.0001 Hz to1000 Hz. It can go up to 4 kHz sampling rate while still keeping the power consumption including sensors
under 1 W. The LEMI-423 uses the well-established LEMI-120 coils, used not only for magnetotellurics but
also for many space research projects. The system weights under 3 Kg (excluding cables) and the sensors
each under 6 kg.
The construction peculiarities and application examples are presented in the report.
Keywords: web access box, LMT & MT system, low-noise, low-power station.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

EM-ACROSS System: Installation at the Kusatsu-Shirane Volcano, Japan
Serita, S.1, Ogawa, Y.1,2, Ishizu, K. 2,3, Tseng, K.H.1, Kunitomo, T.4,
Minami, T.5, Ichihara, H4, Caldwell, T.G. 6, Heise, W. 6, Bertrand E.A.6
1Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Japan
2Volcanic Fluid Research Center, Tokyo Institute of Technology, Japan
3 Graduate School of Science, University of Hyogo, Japan
4 Earthquake and Volcano Research Center, Nagoya University, Japan
5 Department of Planetology, Kobe University, Japan
6 GNS Science, New Zealand

We have been working on establishing an electromagnetic routine monitoring system called EM-ACROSS.
ACROSS stands for Actively Controlled Routinely Operated Signal System. The source signal is composedof a set of sine waves with multiple line frequencies and is precisely controlled by the synchronization with a
10MHz GPS signal. This allows stacking receiver signals for a long time, even with a relatively small powertransmitter.
EM-ACROSS system was installed at Kusatsu-Shirane volcano, located 200km NW of Tokyo. The KusatsuShirane volcano is an andesitic-to-dacitic volcano known for its phreatic activity in historical times. Theinternal structure of the most active crater, Yugama, was modeled to the depth of 2km by magnetotelluric
soundings with 91 sites, and a three-dimensional resistivity structure was obtained (Tseng et al., 2020). We
now aim to image the temporal variation of the resistivity structure in four dimensions.
The transmitter system is composed of a GPS-controlled function generator that can output arbitrary signals
at two channels. For each channel, the signal time series consists of a set of 8 frequencies. The two sets
have slightly different frequencies, so two sets of independent signal time series can be recorded in thereceiver without interference. The amplitudes of the transmitter signals have a larger amplitude toward lower
frequencies to counteract the natural signals. The phases of the sine components are randomized to avoid
generating a peaking. We installed two current dipoles in NS and EW directions. The signals from the twochannels of the function generator were 80 times amplified by the two power amplifiers.
The receiver sites measure electric and magnetic signals using MT receivers. As we have two current
dipoles, we can obtain tensor responses simultaneously. In addition, as the transmitter signal consists of aset of specific line frequencies, we can also measure magnetotelluric signals without disturbances.

Keywords: controlled source, frequency domain, stacking, ACROSS

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Towards a AFMAG-capable airborne EM Sensor Platform - Identification of Noise
Sources
A. Thiede, M. Becken
Institute of Geophysics, University of Münster, Germany; athiede@uni-muenster.de

S UMMARY
Airborne electromagnetic (EM) measurements using natural signal provide a dense data coverage of large
areas in short time and with small effort. The audio-magnetotelluric signal is much weaker than the signal ofa nearby artificial transmitter. Hence, airborne systems require an extremely low noise level for purely passive
measurements.
The strongest noise source of airborne EM data is motion noise due to sensor movements in the Earth’s
magnetic field. As part of the DESMEX II project, we aim to optimize the DESMEX induction coil airborne
system to meet passive EM requirements and improve semi-airborne EM data quality aspiring to penetration
depths of > 1000 m. We use an Inertial Navigation System (INS) to measure sensor movements and predict
the signal input caused by motion. The INS samples with 400 Hz, thus allows for low frequency corrections.
However, the “cleaned” data recorded with undamped sensors are still afflicted with remaining motion noise
and the resulting signal-to-noise ratio is insufficient for natural signal analysis. To reduce the noise level, we
built a damped sensor platform using vibration-insulating foam material. Damping characteristics were testedon the ground and under flight conditions.
The vibration-insulated sensor platform reduces motion noise on all components with a factor of more than
10 at high frequencies (> 50 Hz). Passive airborne EM using the DESMEX induction coil system seems to
be feasible in a limited frequency range. Accessing lower frequencies is essential for greater penetration
depth hence further improvement is necessary. We identified internal EM and further motion noise sources
affecting airborne EM data quality. Ongoing work focuses on the removal of noise sources and the design ofan optimized sensor platform.
Keywords: Audio-frequency Magnetics; semi-airborne Electromagnetics; Airborne Sensor Platform; Motion
Noise

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

3D inversion of an integrated TEM survey
Longying Xiao1, Gianluca Fiandaca2, Pradip K. Maurya3, Anders Vest Christiansen4
1 Geological Survey of Finland (GTK), longying.xiao@gtk.fi
2 University of Milano, gianluca.fiandaca@unimi.it
3 Aarhus University, pradip.maurya@geo.au.dk
4 Aarhus University, anders.vest@geo.au.dk

SUMMARY
Recent instrument advancements in the field of the transient electromagnetic (TEM) method enableapplications adapted to different environments. To invert integrated datasets from ground-based andwaterborne TEM surveys under one model domain, the complexity increases for two factors: i) significant multidimensionality effects from settings with strong conductivity contrasts such as a coastline. ii) sensitivity
footprints vary depending on the systems. We address these challenges by utilizing a previously developed
3D octree-based inversion scheme, where the decoupling between forward and inversion mesh allows local
meshes for individual soundings, and a commonly shared model for the inversion domain. We demonstrate
the framework environments through synthetic and field case studies. These experiments show that: i) for such
surveys, the 3D inversion outperforms the 1D inversion in terms of a lower data misfit and more accurate
predicted model; ii) a careful forward mesh refinement is required to effectively explain the data collected at
the settings with thin and highly conductive top layers.
Keywords: Time-domain electromagnetic, 3D, Inversion, Borehole

INTRODUCTION
The transient electromagnetic method is an efficientand non-invasive geophysical tool for characterizing
the resistivity distribution of the subsurface.
Nowadays, many TEM systems are developed for
improved maneuverability and acquisition, allowing
for measurements in a variety of environments – e.g.
airborne, ground-based, or waterborne surveys. An
integrated survey, which consists of a mix of several
TEM systems, can be a helpful solution in somecircumstances, since different systems may offer
superior resolution by concentrating at different
sensitivity depths or surveying different types ofland/sea. Following TEM measurements, a 1D
inversion is routinely applied, assuming the
subsurface is quasi-1D. However, it is difficult toresolve the subsurface structures with little
ambiguity if major 2D or 3D effects are present(Rabinovich, 1995; Bauer-Gottwein et al., 2010),
such as salinity-related anomalies, even if there is astrong agreement between predicted and measured
data. Furthermore, the inevitable mix of system
configurations result in varied sensitivity footprints
horizontally and vertically, necessitating individually
tailored meshing for 3D modeling.

high salinity and freshwater lithologies results insignificant 2D and 3D effects in the data. As a result,a standard 1D inversion framework is unable toappropriately resolve subsurface structures
correctly, which could be crucial for effectively
conceptualizing seawater intrusion problems. To
address the issues, we use a developed 3D multimesh inversion scheme (Xiao et al., 2022): i) 3D
octree-based forward modeling is employed todescribe the multi-dimensional environment andsimulate the electromagnetic field diffusion. ii) a
decoupling between the forward and the inversion
mesh is utilized to provide the flexibility of modelingeachsounding
separately
tominimize
computational costs while accounting fordifferences in configuration and sensitivity. The
mesh decoupling further allows for a continuation of
the model-domain across the land-sea interface
during inversion, in comparison to an approach thatuses independent model domains for the landbased and waterborne sub-surveys. We investigate
the problem on a synthetic example anddemonstrate our solution on a field dataset collectedfrom a coastal area, where borehole data isavailable to verify the result.
METHODS

The outlined challenges are especially prominent incoastal surveys. First, both ground-based andwaterborne (or airborne) TEM systems are used toimage the resistivity distribution below land andseawater. Second, the strong conductivity contrast
between seawater and/or lithologies affected by

TEM Modeling and inversion

Assuming that the media is isotropic, nonmagnetizable and that electrical properties are
independent of time, the time-domain forward

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Xiao, L. et al., 2022, 3D inversion of an integrated TEM survey

problem is formulated as a diffusion equation interms of the electrical field 𝐞(𝐱, 𝑡):𝜕𝐞(𝐱, 𝑡)𝜕𝐣𝐬 (𝑡)
(1)
=−
𝜕𝑡
𝜕𝑡
where the electric field 𝐞(𝐱, 𝑡), is a function of space,𝐱(𝐱 ϵ 𝛺) and time, 𝑡 𝜖 (0, 𝑇) 𝜇 is the magnetic
permeability of free space, 𝜎 denotes the electric
conductivity, and 𝐣𝑠 denotes the current source.
The modeling and inversion used a previously
developed octree-based scheme (Xiao et al., 2022).
In particular, the multi-mesh approach (Zhang et al.,2021) is employed in the scheme, which isbeneficial in this dual-system ground-based andwaterborne TEM inversion. In the inversion, one
regular mesh is used for the full-scale model update.
The forward modeling and Jacobian calculation,
however, are performed on sounding-based, with alocal mesh description for the different systems.
∇ × ∇ × 𝐞(𝐱, 𝑡) + 𝜇𝜎(𝐱)To replicate a coastal environment (Figure 2-b)
similar to the field case scenario, we created athree-layer 3D synthetic model. The measurement
for the synthetic example (Figure 2-c) uses tTEM
(Auken et al., 2019) and FloaTEM (Maurya et al.,2021) systems to cover an area of 140mx70m witha 10 m sounding distance. We selected a profile
perpendicular to the driving direction consisting of 6
tTEM soundings and 7 FloaTEM soundings. We
compared the relative difference between the 1D
response from AarhusInv (Auken et al., 2015) andthe 3D response from the forward solution (Xiao etal., 2022).

Modeling mesh design

TEM techniques are highly sensitive to conductors.
When doing a survey in saline water, the eddy
currents diffuse horizontally in the water for a longtime rather than moving downwards, resulting in ashallow vertical resolution. For such cases where
conductive structures are present in the top
subsurface, additional mesh refinement is required
to attain acceptable numerical accuracy. First, themesh elements in the seawater layer must be
refined to depict rapid field variations near the TEM
system. At the same time, the refinement mustcover vast horizontal footprints to account for thelateral extension of the eddy currents with time.

Figure 1. Illustration of the octree meshrefinement for a three-layered (1/4/100 Ωm)model. a) shows a normal half-space meshing
while b) shows a refined mesh for a highly
conductive and thin top layer. c) shows a closeup of the mesh in b) at the end of the fine mesh(a1), laterally, and d) shows a similar close-up
just below the transmitter (a2).
RESULTS
Synthetic example

Figure 2. The 1D/3D forward and inversion result
on a coastal model: a) Forward response difference;b) Coastal model illustration, where the tTEM and
FloaTEM soundings are symbolized by black andgrey dots, respectively; c) 1D inversion section(starting model of 3D inversion); d) 3D inversion
section; e) 1D inversion misfit map; f) 3D inversion
misfit map. The dotted white lines outline thestructures of the true model. The dotted black lines
symbolize the coastline. The solid red line indicatesthe section location of inversion results in subfigure(c) and (d).
The modeling test has revealed a significant
difference between a 1D and a 3D response, in the
instance of strong 2D effects from a coastline(Figure 2-a). As a result, it has illustrated theimportance of using multi-dimensional simulation
instead of 1D modeling in such an area. To compare
the inversion performance of different dimensions,
we performed both 3D (Xiao et al., 2022) and 1D
inversions (Christensen et al. ,2017) using the samemodel space, i.e. a voxel mesh with the same

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spatial discretization. Although we started theinversion with correct parameters, the seawaterlayer in 1D inversion result is too shallow comparedto the true model; also, a conductive pant leg isreaching into the high-resistivity layer. The 3D
inversion starting model (i.e.,1D inversion result), on
the other hand, did not include a layer to represent
the proper water table, but it recovered the truemodel substantially. This includes a morereasonable resistivity model without the pant leg.
Field case

Himmark Beach is a contaminated area in southern
Denmark due to the deposition of chemical waste.
The local government aims to characterize thepollution pathways to the sea using the TEM method
and make a removal strategy for the environmental
problem. We use a small subset from the TEM
campaign to illustrate our solution to the inversion
problem, due to the large computational complexity
and the access to existing borehole information.
The presented dataset (Figure 3) consists of 18
tTEM soundings and 41 FloaTEM soundings. In
general, the distance between acquisition lines isaround 20 m and the distance between soundings
is 5 m.

Figure 3. Survey map. The solid
yellow line represents a profile
along the existing boreholes.
In 1D inversion result (Figure 4-a), what appears to
be a 2D pant leg effect, similar to the one seen in
the synthetic example inversion. For the 3D result(Figure 4-d), the top resistive till layer (~20 Ωm) is
only aligned with the borehole reported sand-clay
interface. We further calculate the formation factorof the 1D/3D inversion models within the depth
range of the saline-water-saturated sand layer
observed in the boreholes. Following Archie’s law(Archie, 1942), we find that the 1D result yields aformation factor of 20, whereas the 3D inversion
predicts a value of ~4-5. Based on published labmeasurements of saline sand formation properties(Frings et al., 2011; Kadhim et al., 2013), we learn
that the 3D result gives a more reasonableestimation of the resistivity in the sand layer.

Figure 4. The result from inversions. The whitedashed line represents the bathymetry based on
measurements at the borehole locations.
CONCLUSIONSIn this study, we investigated the 3D effects present
in integrated TEM surveys at coastal sites byapplying a previously developed 3D inversion
scheme, both with synthetic and field examples. In
the synthetic coastal model, we analyzed bothforward responses and inversion results using a 1D
and a 3D forward code. The forward response
difference increases significantly when thesoundings are closer to the coastline, which was up
to 400% on a sounding basis. As for the inversion,the 1D result did not recover the water depth
properly. Furthermore, a clear pant leg effectappeared in the onshore survey area. The 3D
inversion, however, reproduced the model fairly
accurately with no particular artifacts and a low data
misfit. In the inversions of the field data, many
results from the synthetic study reappear. Existing
borehole data verified that the 3D inversion
provided accurate characterization of a particularinterface between sand and clay, which was theprimary target of the investigation.
ACKNOWLEDGEMENTS
The authors would like to thank the region ofSouthern Denmark for allowing the data for
publication. We would like to thank Jesper
Pedersen for providing the background of the fielddata collection and Dr. Nikolaj Foged for extracting
borehole information from the Jupiter database. We
thank Dr. Paul McLachlan for the language polishing.
REFERENCES
Archie, G. E., 1942, The electrical resistivity log as
an aid in determining some reservoir
characteristics: Trans. AIME, 146, 54-62.
Auken, E., N. Foged, J. J. Larsen, K. V. T. Lassen,
P. K. Maurya, S. M. Dath, and T. T. Eiskjær,Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Xiao, L. et al., 2022, 3D inversion of an integrated TEM survey

2019, tTEM—a towed transient electromagnetic
system for detailed 3d imaging of the top 70 mof the subsurface: Geophysics, 84, no. 1, E13E22.

Zhang, B., K. W. Engebretsen, G. Fiandaca, H.
Cai, and E. Auken, 2021, 3d inversion of timedomain electromagnetic data using finite
elements and a triple mesh formulation:
Geophysics, 86, no. 3, E257-E267.

Auken, E., A. V. Christiansen, G. Fiandaca, C.
Schamper, A. A. Behroozmand, A. Binley, E.
Nielsen, F. Effersø, N. B. Christensen, K. I.
Sørensen, N. Foged, and G. Vignoli, 2015, An
overview of a highly versatile forward andstable inverse algorithm for airborne, groundbased and borehole electromagnetic andelectric data: Exploration Geophysics, 2015, no.
46, 223-235.
Bauer-Gottwein, P., B. N. Gondwe, L. Christiansen,
D. Herckenrath, L. Kgotlhang, and S.
Zimmermann, 2010, Hydrogeophysicalexploration of three-dimensional salinity
anomalies with the time-domain
electromagnetic method (tdem): Journal of
Hydrology, 380, no. 3-4, 318-329.
Christensen, N. K., T. P. A. Ferre, G. Fiandaca, and
S. Christensen, 2017, Voxel inversion of
airborne electromagnetic data for improved
groundwater model construction and prediction
accuracy: Hydrology and Earth System
Sciences, 21, no. 2, 1321-1337.
Frings, R. M., H. Schüttrumpf, and S. Vollmer,
2011, Verification of porosity predictors for
fluvial sand‐gravel deposits: Water Resources
Research, 47, no. 7.
Kadhim, F. S., A. Samsuri, and A. Kamal, 2013, A
review in correlation between cementation
factor and carbonate rock properties: Life Sci. J,
10, no. 4, 2451-2458.
Maurya, P. K., F. E. Christensen, M. A. Kass, J. B.
Pedersen, R. R. Frederiksen, N. Foged, A. V.
Christiansen, and E. Auken, 2021, Technicalnote: Efficient imaging of hydrological units
below lakes and fjords with a floating, transient
electromagnetic system (FloaTEM): Hydrol.
Earth Syst. Sci. Discuss., 2021, 1-21.
Rabinovich, M., 1995, Errors of 1-d interpretationof 3-d tdem data in the application of mapping
saltwater/freshwater contact: Journal of AppliedGeophysics, 34, no. 1, 23-34.
Xiao, L., G. Fiandaca, B. Zhang, E. Auken, and
A.V. Christiansen, 2022, Fast 2.5D and 3D
Inversion of transient electromagnetic surveys
using the octree-based finite element method:
Geophysics, 0: 1-53.
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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Joint Probabilistic Inversion of 3D Magnetotelluric and Seismic Data in Southeast
Australia

1

M.C. Manassero1 , J.C. Afonso2 , S. Özaydin 1 , A. Kirkby3 , I. Fomin1 A.G. Jones4 , 5, and K. Czarnota6
Macquarie University, Australia (constanza.manassero@hdr.mq.edu.au, sinan.ozaydin@hdr.mq.edu.au)
2
University of Twente, Netherlands (j.c.afonso@utwente.nl)
34Complete MT Solutions Inc., Canada
University of Western Australia, Australia
6
Geoscience Australia, Canberra, Australia
5

S UMMARY
Multi-observable inversions are gaining popularity for imaging the structure of the lithosphere (Afonso et al.,2016). Of particular interest is the joint inversion of magnetotelluric (MT) with seismic data as their complementary sensitivity to the thermal structure, hydrogen content and small volumes of fluid or melt offer a powerfulmeans to detect fluid pathways in the lithosphere including the locus of partial melting, ore deposits and hydrated lithologies. This unique potential has given impetus to the acquisition of collocated MT and seismic dataover large regions (e.g., AusLAMP/AusArray in Australia).
Probabilistic inversions provide complete information about the unknown parameters and their uncertainties
conditioned on the data and modelling assumptions. Joint probabilistic inversions of MT and seismic data
have been successfully implemented in the context of 1D MT data only. For the cases of 2D and 3D MT data,
however, the large computational cost of the MT forward problem has been the main impediment for pursuingprobabilistic inversions. To overcome this limitation, we have presented a novel strategy (Manassero et al.,2020, 2021) that reduces the computational cost of the 3D MT forward solver and allows us to perform full joint
probabilistic inversions of MT and other datasets for the 3D imaging of deep thermochemical anomalies andfluid pathways.
As part of the Exploring for the Future program, we present preliminary results of the first joint probabilistic
inversion of 3D MT in southeast Australia using the AusLAMP data and a seismic velocity model derived fromteleseismic tomography (Rawlinson et al.,2016). We also make interpretations of our conductivity models using
the code MATE (Özaydin and Selway, 2020). These results demonstrate the capabilities of our conceptual andnumerical framework for 3D joint probabilistic inversions of MT with other geophysical data sets and open up
exciting opportunities for elucidating the Earth’s interior in other regions.
Keywords: 3D Magnetotellurics, Probabilistic Inversion, Joint Inversion, Numerical modelling, Lithosphericstructure

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Petrophysical-based constrained and joint inversions of magnetotelluric (MT) andgravity data-sets on unstructured tetrahedral meshes

1M. Kangazian1 , C. Farquharson1
Memorial University of Newfoundland, St. John’s, NL, Canada, Department of Earth Sciences.
mkangazianka@mun.ca, cgfarquh@mun.ca

S UMMARY
Geophysical inverse problems can identify significant subsurface features by the means of measured physicalquantities such as gravity, magnetic, electric, and electromagnetic (EM) fields. The minimum-structure algorithm, or Occam’s style, is one of the most common inversion methods that has been extensively employed
for the inversion of geophysical data due to its robustness and reliability. This method can be adapted andextended in many ways. Perhaps the two most important of which are constrained and joint inversion methods,
and on which this study focusses. The geophysical Earth models constructed from multiple, complementarygeophysical survey types or constrained independent inversions are more likely to represent the true subsurface than a model constructed from an independent unconstrained inversion.
Joint inversion methods can be divided into two main types, structural-based and petrophysical-based joint
inversion methods, depending on the type of coupling measure used between physical property models.
This study develops a joint MT and gravity inversion methodology based on a petrophysical-based approach.
Petrophysical-based joint inversions result in a less challenging, although not easy, optimization problem compared with the structural-based approaches. The fuzzy c-means clustering technique (FCM) is the petrophysicalbased joint inversion method that this studying uses as a coupling measure. Also, independent constrained
MT and gravity inversions using the FCM method are compared with the joint inversion results.
The capability of the joint and constrained MT and gravity inversions are investigated using synthetic andreal-data examples. The conductivity and density models constructed by the clustering method represent a
reasonable definition of the anomaly’s boundaries and a reasonable range of the recovered physical property
values of the true model compared to the models constructed by the independent MT and gravity inversions.
Keywords: Constrained inversion, Gravity method, Joint inversion, Magnetotelluric method

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

True 3D Land CSAMT Modeling
W. Soyer1 and R.L. Mackie1
1CGG Multiphysics

Wolfgang.Soyer@cgg.com
Randall.Mackie@cgg.com

SUMMARY
Controlled source audio magnetotellurics “CSAMT” is a grounded electromagnetic imaging technique usedprimarily in the mining industry and is a closely related predecessor to the marine controlled source
electromagnetic method “CSEM” more familiar to the oil and gas community. Both methods involve injecting
alternating electric current into the Earth through long transmitter dipoles, and making measurements of the
Earth’s response at an array of EM receivers. Historically, CSAMT data have been interpreted using readily
available MT modeling algorithms, but this means using only the data considered far enough away from thedipole source to be interpretable using the MT-inherent assumption of a uniform, or “far-field”, source. In thiswork, we show the advantages of carrying out complete earth, three-dimensional modeling of all grounded
dipole EM fields, including those near the source, and avoiding the commonly employed, ad-hoc methods thatthrow away otherwise valuable “transition-zone” data that are unusable in MT-only modeling workflows. The
CSEM inversion modeling is applied to a synthetic and a real data example, and inversion of complex field
amplitudes is compared with impedance inversions.
Keywords: 3D inversion, true layout, CSEM, CSAMT, MT

INTRODUCTION
Controlled source, grounded electrode EM surveys
have a long and successful history in mineral
exploration geophysics, where the most commonly
deployed configuration is CSAMT, using single,
fixed, kilometer-long transmitter dipoles inbroadside configuration (e.g., Zonge and Hughes
1991). More recently, however, hybrid survey
configurations are gaining in popularity, including
surveys using multiple transmitter and receiver
dipole locations and orientations (e.g., Grayver et al.
2014, Darnet et al. 2018).
Despite continuous advancements in computing
power and algorithms, the analysis and modeling of
CSAMT data is still generally restricted to “far-field”
data, where the standard magnetotelluric (MT)uniform source field assumption is valid, and readily
available MT modeling tools are employed (e.g.,
Sasaki et al, 1992, Wannamaker, 1997). In general,
far-field conditions are met at higher frequencies
and longer source-receiver offsets. This restriction
not only limits the depth of exploration, but also
removes the potentially beneficial sensitivity
characteristics of EM fields in the transition zone
between near-field and far-field behavior, wherevertical current components are sensitive to thinresistive layers otherwise invisible to MT. While
three-dimensional inversion modeling of land
CSAMT is being developed (e.g., Lin et al 2018,

Wang et al 2018) it is not yet the industry standard
that 3D inversion modeling is for land and marine
MT, and marine CSEM.
3D CSEM IMPLEMENTATION
Here we discuss extensions of electromagnetic
modeling algorithms (Mackie et al, 2020) for
generic, land CSEM array data, including CSAMT
configurations. The objective is to provide accurate
and efficient modeling of the typically km-longgrounded transmitter dipoles, with rugged
topography and realistic geology at both receiverand transmitter array locations. We model EM fields
using a staggered grid, finite difference techniquewhich is a subset of the more general Finite
Integration Technique. In our modeling code, long
grounded dipoles are draped over topography. We
can model and invert far-field, transition zone, andnear-field data in a single workflow, as complete
controlled source EM data.
Code verification was carried out using a sequenceof 1D models of increasing complexity andcomparing 3D results to those obtained withanalytical 1D CSEM codes (not shown here). Thisconfirmed that with reasonable gridding we canobtain accuracies to better than 2%, in line withtypical finite difference numerical solutions.

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Soyer, W. and Mackie, R.L., 2022, True 3D Land CSAMT Modeling

SYNTHETIC EXAMPLE
We designed a test 3D model using real topography
with about 800m relief (Figure 1). The 3D model has
a background resistivity of 100 Ωm and hosts twopairs of 3200×2800×500 m anomalies of 10 Ωm and5000 Ωm, respectively, draped below 1000m depth(Figure 1). Two perpendicular transmitterorientations were modeled. Receivers were spaced
every 400 m, and offsets to the transmitter ranged
from 3 km to 12 km. We modeled CSEM forward
responses at 28 frequencies from 0.2 Hz to 500 Hz,
using all components.
Figure 2. 3D CSEM field inversion results. Data
input: top) broadside electric fields; middle)
broadside & inline electric fields; bottom) broadside& inline magnetic fields.

Figure 1. Setup of CSAMT simulated array overtopography. The transmitter location (red cross) is to
the west at 3 km from closest receivers. The fouranomalous 500m thick bodies inserted at 1km
below the surface are shown in orange and blue.
Figure 2 shows results from a number of 3D
inversions of complex field amplitudes, all of whichstarted from the background resistivity and used low
structural regularization. Prior to the inversions we
added 1.5% random Gaussian noise to the electricand magnetic fields of the CSEM data. Both theelectric broadside data (top row) and the combined
electric (scalar) broadside and inline data inversion(middle row) reveal the resistivity structure
reasonably well, however the inversion involving theinline component resolves the resistor closer to thetransmitter better due to the stronger vertical current
flow in this configuration (Figure 3). Magnetic field
inversions yield only a very smooth representationof the resistivity structure (Figure 2, bottom).
A question being discussed is whether field
amplitude inversions have an advantage over
impedance inversions (Routh and Oldenburg 2000).
Here we have carried out an impedance inversion
for the synthetic broadside setup, and theseresulted in higher structural detail than the inversionof the corresponding electric field amplitudes,
resulting in better impedance data fits (Figures 4
and 5).

Figure 3. 3D current density for the transmitter in
the inline configuration, at 0.2 Hz. Top: depth slice
at 1.25km below surface; middle and bottom: crosssections, vertical exaggeration is 2.5. Warm colors
and thicker lines mark higher current density.

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Figure 4. 3D CSEM electric field amplitude
inversion (top) compared to the sharper 3D CSEM
impedance inversion (bottom). Broadside data wereused in both cases.
Figure 6. Scalar, broadside CSAMT survey at
Silverbell. Transmitter-receiver distance is >4.7 km.
A 3D mesh was created with 20x60x10 m spacingin the topographic core, and a wider 100m crossline
spacing between the transmitter and the receivers(1.5 million cells total). Input data for 3D CSEM
inversions were scalar impedances at all 9
frequencies between 32 Hz and 8192 Hz. A
homogeneous 150 Ωm was used as starting model.
The 3D CSEM inversion gives a good data fit acrossthe full frequency range (Figure 7). Results areshown along line L28 in Figure 8 (bottom) incomparison with 1D and 2D MT inversions (TMmode data). The 3D inversion provides a smooth
image, without strong imprint from statics.
Figure 5. Impedance data fit at high angle fromtransmitter axis for electric field amplitude (Ex, left)
and impedance (Zxy, right) 3D CSEM inversion. The
impedance inversion achieves a better fit, as might
be expected, as magnetic fields are not inverted in
the Ex only inversion.
REAL DATA EXAMPLE - SILVERBELL
We applied our new inversion algorithm to a 1993
CSAMT legacy data set from the Silverbell mine in
Arizona. Line and site spacing is 240m and 60m,
respectively, and other geometric parameters areshown in Figure 6.

Figure 7. Data fit examples from 3D CSEM
inversion impedance inversion at Silverbell.

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Soyer, W. and Mackie, R.L., 2022, True 3D Land CSAMT Modeling

REFERENCES
Darnet M, Wawrzyniak P, Coppo N, Nielsson S,
Schill E, Fridleifsson GO (2018) Monitoring
geothermal reservoir developments with theControlled-Source Electro-Magnetic method – A
calibration study on the Reykjanes geothermal
field: Journal of Volcanology and Geothermal
Research: 391,
doi:10.1016/j.jvolgeores.2018.08.015.
Grayver AV, Streich R, Ritter O (2014) 3D inversion
and resolution analysis of land-based CSEM
data from the Ketzin CO2 storage formation:
Geophysics 79(2): E101–E114,
doi:10.1190/Geo2013-0184.1.
Figure 8. Comparison between 1D & 2D MT, and
3D CSEM impedance inversion, along line L28.
Input data to MT TM-mode inversions excluded thelowest three frequencies. 1D inversions are affectedby statics.
The 3D CSEM results also match well with earlier
published 2D results (comparison not shown here).
Note the Silverbell mine has evolved dramaticallysince the acquisition of this data set.
CONCLUSIONS
We have extended our land, marine and airborne
3D EM inversion modeling algorithms to include
generic land controlled-source EM modeling. While
we can now model any arbitrary set up of transmitterand receiver dipole orientations and locations, here
we have concentrated on the typical CSAMT field
deployment with one or two long groundedtransmitter and an array of receivers at a certain
offset. True controlled source inversion of the CSEM
data results in good recovery of resistivity
anomalies for the synthetic setup, especially when
inverting impedances or inverting electric field datafrom two polarizations. An additional benefit is theability to model all data from near-field through thetransition zone and into the far-field, eliminating ad
hoc methods to remove near-field and transitionzone data.
ACKNOWLEDGEMENTS
We would like to thank Scott Urquhart and Scott
McInnes from Zonge Intl. for providing the Silverbell
data. We highly appreciate the sharing of industry
insight by these and additionally Simon Mann
(Zonge Australia), Roger Sharpe (Quantec) andYann Avram (Phoenix).

Lin C, Zhong S, Auken E, Cai H, Tan H, Peng M,
Kong W (2018) The effects of 3D topography on
controlled-source audio-frequency
magnetotelluric responses. Geophysics 83(2):WB97–108.
Mackie RL, Meju MA, Miorelli F, Miller RV, Scholl
C, Shahir AS (2020), Seismic image-guided 3D
inversion of marine controlled-source
electromagnetic and magnetotelluric data.
Interpretation 8: SS1-SS13.
Routh PS, Oldenburg, DW (2000) Advantages offield component inversion of CSAMT data, SEG
Tech Program Expand Abstrdoi:10.1190/1.1815997.
Sasaki Y, Yoshihiro Y, Matsuo K (1992) Resistivity
imaging of controlled-source audiofrequency
magnetotelluric data. Geophysics 57: 952-955.
Wang T, Wang K, Tan H (2017) Forward modelingand inversion of tensor CSAMT in 3D
anisotropic media. Applied Geophysics 14:
590–605, doi:10.1007/s11770-017-0644-7.
Wannamaker P (1997) Tensor CSAMT survey over
the Sulphur Springs thermal area, Valles
Caldera, New Mexico, Part I: Implications forstructure of the western caldera / Part II:
Implications for CSAMT methodology:
Geophysics 62: 451-465 / 466-476.
Zonge KL, Hughes LJ (1991) Controlled source
audio-frequency magnetotellurics, in M. N.
Nabighian, ed., Electromagnetic Methods in
Applied Geophysics 2b: 713-809, Soc. Expl.
Geophys, Tulsa

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Using deep learning for model error estimation in 3D probabilistic inversion ofcontrolled-source electromagnetic data
Matías W. Elías1 , Marina Rosas-Carbajal2 and Fabio I. Zyserman1
CONICET - Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, Provincia
de Buenos Aires, Argentina, melias@fcaglp.unlp.edu.ar, zyserman@fcaglp.unlp.edu.ar
2
Université de Paris, Institut de physique du globe de Paris, CNRS, F-75005 Paris, France, rosas@ipgp.fr

1

S UMMARY
We propose a 3D Markov Chain Monte Carlo (MCMC) inversion of controlled-source electromagnetic (CSEM)
data to image oil reservoirs. Simulation of the CSEM response with complex 3D geological structures becomeschallenging when a large number of forward computations are required, which is the case when performinga probabilistic inversion. We use a parallel structure and an efficient nonconforming finite element algorithm
to calculate the electromagnetic response by discretizing the diffusive frequency-domain Maxwell’s equations.
We compute the differences between solutions calculated with the same forward solver but with two different
discretization sizes, and we use deep learning techniques to determine a low-dimensional probabilistic representation of this model discrepancies. We use this representation as prior information in the probabilistic
inversion, which is then set to characterize not only the subsurface model parameters, but also the model
errors caused by the use of the computationally-cheap coarse discretization.
Based on an array of receivers, we construct multichannel images of the field discrepancy between models
for different frequencies and field components. We train a spacial generative adversarial network (SGAN) withthose images, demonstrating that the network is able to capture the distribution function of the model error. The
trained generator is added to the MCMC inversion algorithm. Then, at each step of the MCMC computation
the forward response is corrected with a model error realization of the network.
We propose a synthetic marine CSEM experiment to test the MCMC recovery of model parameters and model
error. We evaluate the feasibility of the method and emphasize the advantages of estimating modeling error
and the use of a computationally-cheap forward model. This approach provides a complete probability distribution of the model parameters retrieved by the inverse problem. Assessing model uncertainty contributes toa more comprehensive interpretation of the geophysical measurements.
Keywords: Probability distributions, Neural networks, Controlled source electromagnetics

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Abstract, 25th EM Induction Workshop, Çeşmeı, Turkey, September 11-17, 2022

Surface geometry inversion of marine CSEM data

1

Xushan Lu1,3∗ , Chris Galley2 , Peter Lelièvre1 , and Colin Farquharson3
Department of Mathematics and Computer Science, Mount Allison University
2
Department of Earth and Environmental Sciences, University of Ottawa
3
Department of Earth Sciences, Memorial University of Newfoundland
xlu@mta.ca

S UMMARY
Traditional minimum-structure style inversion recovers models that are generally smooth due to the incorporation of the model roughness terms in the objective function being minimized. For certain models with alocalized anomaly that have a distinct physical property contrast with the surrounding host rocks, such as
seafloor massive sulphide (SMS) ore deposits, it is difficult to extract the boundary information of the ore bodyfrom the recovered models of minimum-structure inversion. We present a surface geometry inversion (SGI)
method which directly inverts for the geometrical information of the ore body’s boundary wireframe surface.
The objective function of our SGI algorithm only has one data misfit term per geophysical data type. Regularization can be applied but is not required. For this example, we use only a single data misfit term. The
minimization of the objective function is an over-determined problem, and we solve it using a global optimization algorithm, namely, the genetic algorithm. We present a synthetic study where we apply our SGI to theinversion of a marine controlled-source electromagnetic (CSEM) dataset. The synthetic model considers realistic bathymetry and the ore body has a complex shape based on a realistic SMS ore body. The surface ofthe ore body is constructed by connecting a small number of control nodes into triangular facets. We then use
a subdivision algorithm to further subdivide the connected facets, which produces a model that has a more
serpentine boundary surface although it is still characterized by the original set of control nodes. Constraints
can be easily incorporated into the model by fixing certain nodes during the inversion when drilling data are
available. In this study, we use our SGI algorithm to invert for the three-dimensional coordinates of these control nodes given a priori physical properties. However, it is possible to invert for physical properties of the ore
body simultaneously, although it would increase the nonuniqueness of the inversion significantly. The physicalproperty of the background model is always fixed during the inversion.
We discretize the entire model with unstructured tetrahedral meshes and then calculate the forward responseusing a finite-element solver. Our SGI algorithm iteratively searches for the best model that can fit the data.
We parallelized our SGI using a hybrid MPI+OpenMP technique to speed up the inversion as there could be
up to several hundreds of models required to be calculated in each iteration. Our synthetic inversion examples
show that the developed SGI could recover models whose shapes are close to those of the true model.
Keywords: Marine CSEM, Seafloor massive sulphide, surface geometry inversion

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Applying a multi-transmitter hybrid Conjugate Gradient-Occam algorithm to theinversion of 3D mCSEM data
W. Lima1, G. Egbert2 , N. Meqbel1, A. Benevides1, S. Fontes1, E. LaTerra1 and P. Werdt1
1Observatório Nacional Brazil, williamslima@on.br
2Oregon State University Corvallis USA, egbert@coas.oregonstate.edu

SUMMARY
We present the results of applying a hybrid conjugate gradient-Occam inversion algorithm to 3D marine CSEM
data. The Occam scheme is a very effective way of dealing with the particular problem of choosing a value forthe regularization parameter in an inverse problem. However, in the traditional implementation, the full
Jacobian matrix is required—something which is not feasible for realistic 3D problems. Thus, iterative searchalgorithms which do not require the full Jacobian, such as NLCG, LBFGS, or iterative solution of the GaussNewton (GN) normal equations, are most commonly used for solving 3D EM inverse problems. Hybridalgorithms offer another option. In the basic hybrid scheme, the dataspace GN normal equations are solved
iteratively (without computing the full Jacobian), but results of the forward and adjoint calculations required foreach iterative step are saved. These are then used to construct a low dimensional approximation to the full
Jacobian, and hence the GN equations. By solving the low-dimension approximate GN equations multiple
times with different regularization parameters, an approximate Occam scheme can be efficiently realized. In
addition to allowing use of GN methods such as Occam, generation of an approximate Jacobian through thishybrid approach will allow more complete and efficient exploration of trade-offs between fitting different datatypes (e.g., MT as well as CSEM), and provides an approximation to the linearized resolution operator.
For most EM inverse problems there are in fact multiple transmitters, characterized by source frequency and/or
location. Gradient based optimization schemes (such as NLCG, as used in ModEM) require solving adjoint
problems for every transmitter. Each of these is essentially the gradient (in the model space) of the data misfit
penalty, restricted to data from that transmitter. The standard schemes merge these into a single gradientvector. With the multi-transmitter hybrid scheme we save all of this information about penalty functional
gradients, and then uses this to form an improved approximation to the full Jacobian. We can anticipate thatsaving all of this information will be especially useful for mCSEM, and joint mCSEM/MT inversions – primarily
because the transmitters (different dipole locations for mCSEM; plane wave sources for MT) are all quite
different, so the corresponding gradient components will be more nearly independent. We are currently testing
this idea using a mixture of Matlab and Fortran—the inversion algorithms being tested are coded in Matlab,
and forward and sensitivity calculations are implemented through calls to ModEM. Communication between
the two codes is through disk I/O. Although there is some loss of efficiency with such an approach,development and testing new algorithmic concepts is much faster in Matlab (especially since we already havea substantial Matlab code base). In our presentation we will present details on the new algorithms, andcompare performance against other algorithms (e.g., NLCG) in terms of memory, number of iterations requiredfor convergence, and total computing time.
Keywords: mCSEM inversion, Occam, hybrid, Conjugate Gradient

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Real-time simulation of the electromagnetic field spatiotemporal evolution due to
geomagnetic disturbances

1

M. Kruglyakov1 , A. Kuvshinov2 and E. Marshalko3
University of Otago, New Zealand, mikhail.kruglyakov@otago.ac.nz
2
ETH Zurich, Switzerland, kuvshinov@erdw.ethz.ch
3Finnish Meteorological Institute, Finland, elena.marshalko@fmi.fi

S UMMARY
We present a methodology for real-time simulation of the electromagnetic (EM) field spatiotemporal dynamics in a given 3-D conductivity model of the Earth and continuously augmented inducing source data. The
formalism relies on the factorization of the source by spatial modes and time series of respective expansion
coefficients, works both on a global and regional scale (i.e. in spherical and Cartesian geometries) and allows
researchers to compute high-resolution EM field in a fraction of seconds.
To validate the formalism, we invoke a 3-D conductivity model of Fennoscandia and consider a realistic source
built using the Spherical Elementary Current Systems (SECS) method as applied to magnetic field data fromthe IMAGE network of observations. The factorization of the SECS-recovered source is then performed using
the principal component analysis. Taking the 7–8 September 2017 geomagnetic storm as a space weather
event, we show that real-time high-resolution 3-D modelling of EM field is feasible, requiring less than 0.025
seconds to compute the field at a given time instant on a 512 × 512 lateral grid.
We also discuss an application of the presented technique to global-scale simulations of magnetic and electricfields.
Keywords: Numerical methods, EM induction, 3-D modelling, Now-casting

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

HIP-FEM: A Hierarchical, Induced Polarization Finite Element Method
for analysis of thin, dispersive, geoelectric features
C.J. Weiss1,2 , G.D. Beskardes1, and A. Darrh1,3
1 Geophysics Department, Sandia National Laboratories, Albuquerque NM, USA, cjweiss@sandia.gov

2 Earth and Planetary Science Department, University of New Mexico, Albuquerque NM, USA

3 Geophysics Department, Colorado School of Mines, Golden CO, USA

SUMMARY

One of the persistent challenges in computational geophysical electromagnetics is balancing the tension
between the high computational cost of maximizing geologic realism and the necessity of rapid throughput ofsimulation results, whether for inversion, “real-time” decision making, experiment design, or construction ofmachine learning training sets. To this end, considerable effort has been spent over the decades on various
effective medium methodologies, exotic reformulations of Maxwell’s equations in terms of non-local (fractional)
operators, and a growing expanse reduced order model approaches. Here we expand on the latter byfocusing on the hierarchical finite element approach (Weiss, 2017; Beskardes et al., 2021) and its extension
to the physics of induced polarization through review of the relevant mathematics and analysis of model studies
inspired by relevant and challenging mineral exploration scenarios. The occurrence of alteration halosaround massive mineral bodies or mineral alteration along the complex fluid migration pathways in a leach pileor fault zone represent instances where the conductive feature of interest is intrinsically “thin” when comparedto the surrounding geologic structures. Such conductive features – often with frequency-dependent electricalproperties – discretize naturally in the hierarchical finite element framework which decomposes the electricalmodel into bulk contributions from volumetric elements, supplemented by infinitesimally thin contributions
associated with the elements’ facets and edges where needed.
This avoids the costly volumetricdiscretization of the latter and lies at the heart of the method’s overwhelming efficiency. The application andextension of the method we describe here represents a novel contribution toward further tilting the balance ofgeologic realism in the analyst’s favor while addressing the future of mineral exploration in developing tools tomeet the demands of an emergent and global, green energy economy.

Keywords: mining geophysics, induced polarization, fractures, finite element modeling

ACKNOWLEDGEMENTS
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology
and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for
the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.
Finite-element meshes used for this analysis were generated by Cubit, available at http://cubit.sandia.gov.
Funding support was provided by the Sandia National Laboratories LDRD program.

REFERENCES

Beskardes, G.D., Weiss, C.J., Um, E., Wilt, M. and MacLennan, K., 2021. The effects of well damage andcompletion designs on geoelectrical responses in mature wellbore environments. Geophysics, 86, no. 6, pp.
E355–E366.

Weiss, C.J., 2017. Finite-element analysis for model parameters distributed on a hierarchy of geometricsimplices. Geophysics, 82, no. 4, pp. E155–E167.

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3-D radio-frequency CSEM at the Weidenpesch waste site in Cologne
M. Smirnova1, B. Tezkan1, A. Shlykov2, S. Fadavi1, A. Saraev2, P. Yogeshwar1
1University of Cologne, maria.smirnova@uni-koeln.de
2Saint-Petersburg State University

SUMMARY

A classical radio-magnetotelluric (RMT) method is routinely applied to various environmental, engineering, andexploration problems. The technique uses the radio-transmitters broadcasting electromagnetic fields at
frequencies above 10 kHz. A significant disadvantage of RMT is a lack of robust radio transmitters in remoteareas and a limited depth of investigation. To overcome these problems, the controlled sources can be used
as an active source – the CSRMT method. Such modification allows measurements in remote areas and theextended frequency range (from 1 kHz to 1 MHz). However, with controlled sources, maintaining the plainwave assumption at all frequencies and throughout the survey area is often problematic. In this contribution,
we present an implementation of the CSRMT method with sources installed in the survey area (near-to-farfield zones). This is, in fact, a CSEM method working in the radio-frequency band. However, we consider
impedance tensor and tipper vector transfer functions, which require measurement and modelling of twosource polarisations. Here, we present the results of the radio CSEM measurements collected at the
Weidenpesch waste site in Cologne. In the obtained 3-D model, the waste site is well resolved. The model
agrees with the boreholes' interpretation and the area's known geology.

Keywords: CSRMT, CSEM, 3-D inversion, Weidenpesch waste site

INTRODUCTION

The concept of using controlled sources (CS) to
improve the data quality and depth of investigationof AMT and RMT methods has earlier shown its
potential (Hughes&Carlson 1987; Bastani 2001).
There, sources were installed in the far-field to fulfil
the plane-wave assumption. One of thedisadvantages of such far-field CSAMT/CSRMT(Saraev et al 2017) is that there are still somesource effects in the data (Li&Pedersen 1991), or if
the source is too far, the signal-to-noise ratio
decreases substantially. Here, we present the
CSRMT method, with sources installed in the survey
area (measurements in near-to-far-field zones),
which is, in fact, a CSEM method working in theradio-frequency band. In this case, the source
geometry must be considered during modelling. We
have implemented the modelling and inversion forthe 3-D case using our finite-difference code in
Matlab (Egbert et al., 2017, Cherevatova et al.,2018). We consider two perpendicular sources(horizontal electric dipole (HED) lines) to estimate
the impedance tensor and tipper vector transfer
functions. Such an approach avoids measurementsof the current strength time series, which is difficultat high frequencies.

Several CSRMT field surveys have been conducted
to verify the method. Here, we present the results of
the CSRMT survey over the Weidenpesch waste

site in Cologne, Germany. This area was selected
because the conductive waste site is buried in acontrasting, more resistive host rock. The geologyof the sub-surface, composition and history of thewaste site is well known. The waste site is about 400
m by 90 m in lateral size and about 10 m thick. It
comprises various building debris, household
wastes, dust, grinding, and stone materials and issurrounded by relatively resistive gravel and sand.
The site is closed and covered with a thin layer offine silty sand (0.5-2.5 m thick). In addition to
CSRMT measurements, ERT, magnetic and TEM
data were acquired and will be used later forcomparison.

METHODSThe 3-D radio-frequency CSEM modelling andinversion was implemented within the MR3DMod
framework – an object-oriented code in Matlab. The
code was initially created as a prototype of MT
ModEM software (Egbert&Kelbert 2012, Egbert etal. 2017), later extended for other methods, e.g.,
CSEM, TEM, DC, gravity and magnetics. Thus, as
in ModEM, the modelling is performed using finitedifference approximation on a staggered grid. For
CSEM, we followed the scattered-field approach(Newman&Alumbaugh 1995), where the total field(we solve for 𝐄 field) is split into the backgroundand anomalous (scattered) parts: 𝐄 = 𝐄! + 𝐄" . The
background EM field is calculated using 1-D quasi1 / 4

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Smirnova, M. et al., 2022, 3-D radio CSEM at Weidenpesch

analytical code of Key (2009). The anomalous fieldis calculated by solving a system of partial
equations:∇ × ∇ × 𝐄" + 𝑖𝜔𝜇# 𝜎𝐄" = −𝑖𝜔𝜇# (𝜎 − 𝜎! )𝐄" ,(1)

where the right-hand side is an external source term,
𝜇# is the magnetic permeability of free space, 𝜔 isthe angular frequency, and 𝜎! is the background
conductivity (1-D half-space). In this study, we
neglect the displacement currents as the subsurface’s resistivity is below 1000 Ωm, and there isno evidence of the strong polarisation effects in thefield example discussed later. Once the solution for
the anomalous electric field is obtained, theanomalous magnetic field is computed as$

𝐇" = %&' ∇ × 𝐄" .
!

(2)

The total magnetic field is then calculated as asuperposition of the background field andanomalous field: 𝐇 = 𝐇! + 𝐇" . Equations 1 and 2
are calculated twice, once for each source
polarisation. Then, EM fields are interpolated fromgrid edges/faces to the site locations, andimpedance tensor or tipper vector transfer functions
are estimated. The subsequent procedure forcalculating the Jacobian is the same as inEgbert&Kelbert 2012 with a non-zero source(CSEM and adjoint cases). For inversion, we usedthe data space-Occam method (Siripunvarapon etal. 2004).

Figure 1. Survey plan for CSRMT measurements inthe transition zone at Weidenpesch, Cologne. Red
circles mark the location of the receivers; magentalines are source locations. The waste site is outlinedwith a dashed grey line. Local coordinate system xy
is shown on the map.
Figure 2 shows a pseudo-section of the yx
component of the apparent resistivity and
impedance phase for profile 12 (Figure 1). The
apparent resistivity ranges from around 1 Ωm to1000 Ωm. There is a clear boundary between the
conductive waste site in the NW of the profile andthe resistive background in the SE. The data areconsistent from site to site and with a period,
indicating good data quality.

RESULTS

The Weidenpesch survey was conducted in twophases. The first phase was CSRMT/RMT far-fieldmeasurements. The second phase, discussed inthis contribution, was the transition zone CSRMT(Figure 1). Two perpendicular HED sources (Tx1 –215 m, Tx2 – 465 m) were installed in the survey
area. Source current was variable between 1 A and7.5 A. Measurements were performed along a set ofprofiles (red circles in Figure 1) crossing the waste
site (dashed grey line in Figure 1). The distance
between profiles is about 30 m and between sites
about 10 m. At each site, six recordings were done,
three for each source at base frequencies of 0.5, 5,
and 50 kHz, to cover the complete frequency range
from 1 kHz to 1 MHz. The data were processedusing the KMSProMT software (Smirnov 2003),
which was adopted for CSEM processing by adding
the discrete frequency selection (Smirnova et al.
2019).

Figure 2. Pseudo-section of the yx apparent
resistivity (top) and impedance phase (bottom) at
profile 12 (see location in Figure 1).

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In Figure 3, an example of a typical sounding curveat the waste site is shown (site 16 along profile 12
in Figure 1).

CONCLUSIONSWe presented a radio-frequency CSEM method(CSRMT in the transition zone) in this contribution.
We developed and tested an algorithm for CSRMT
3-D modelling and inversion for the case when data
are acquired in the vicinity of the source. Such an
approach allows us to extend the measurement
frequency range, depth of investigation and improve
the signal-to-noise ratio. Additionally, it simplifies thesurvey planning and logistics, as sources can beinstalled directly in the survey area. We presented areal-field case study for the Weidenpesch waste sitein Cologne. The 3-D model clearly outlines thewaste site's location and shape and agrees withborehole data and geology.

ACKNOWLEDGEMENTS

The project was funded by the German Research
Foundation (DFG) under grant number TE 170/211 and by the Russian Science Foundation, project
number 21-47-04401.

REFERENCES
Figure 3. Apparent resistivity and impedance phaseof the off-diagonal components of the impedance
tensor at the selected site (see location in Figure 1).

Bastani M (2001) EnviroMT: A new controlled
source/radio magnetotelluric system. PhD thesis,
Uppsala University, Uppsala

In Figure 3, the xy slices from the 3-D model are
shown at different depths (1 m, 2.1 m, 3.3 m, and
9.5 m). The model was obtained after inverting theimpedance tensor at 114 sites in the frequency
range from 1 kHz to 524 kHz. Error levels were set
at 5% 2|𝑍() 𝑍)( | for all impedance components.
The coordinate system was rotated by 20° to
reduce the mesh size. The modelling domain wasthen discretised into 96x96x50 cells with a minimum
11 m cell size in the x-direction, 6 m in the ydirection and 1 m in the z-direction. A half-space of
50 Ωm was used as the initial model. The inversion
converged to an RMS of 2.5.

Cherevatova M, Egbert GD, Smirnov MY (2018) A
multi-resolution approach to electromagnetic
modelling. Geophysical Journal International 214:
656—671

DISCUSSION

Hughes L, Carlson N (1987) Structure mapping at
Trap Spring Oil eld, Nevada, using controlledsource magnetotellurics. First Break 5(11)The highly conductive (1-10 Ωm) waste site is wellresolved in the 3-D model (see slice at a depth of
3.3 m in Figure 4). The top of the anomaly is at a
depth of around 2 m, and its thickness is about 7 m.
The waste is covered by a resistive layer of fine silty
sand (up to 1000 Ωm and about 1 m thick). The
waste is underlain by a more resistive layer of graveland sand (around 100 Ωm). This layering structureagrees with borehole interpretations, suggesting the
top 2 m layer of fluviatile clays, underlain by a 20 m
thick Pleistocene gravel and sand layer.

Egbert GD, Kelbert A (2012) Computational recipes
for electromagnetic inverse problems. GeophysicalJournal International 189: 251—267
Egbert GD, Smirnov MY, Cherevatova M (2017)
Development of Flexible Object-Oriented MATLAB
Codes for 3D EM Modeling. 6th International
SymposiumonThree-Dimensional
Electromagnetics, Expand Abstr.

Key K (2009) 1D inversion of multicomponent,
multifrequency marine CSEM data: Methodology
and synthetic studies for resolving thin resistive
layers. Geophysics 74: F9—F20
Li X, Pedersen LB (1991) Controlled source tensor
magnetotellurics. Geophysics 56: 1456–1461

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Smirnova, M. et al., 2022, 3-D radio CSEM at Weidenpesch

Newman GA, Alumbaugh DL (1995) Frequencydomain modelling of airborne electromagnetic
responses using staggered finite differences.
Geophysical Prospecting 43: 1021—1042

Smirnov MY (2003) Magnetotelluric data processing
with a robust statistical procedure having a highbreakdown point. Geophysical Journal International
152: 1—7

Saraev A, Simakov A, Shlykov A, Tezkan B (2017)
Controlled source radiomagnetotellurics: A tool fornear surface investigations in remote regions.
Journal of Applied Geophysics 146: 228—237

Smirnova M, Becken M, Nittinger C, Yogeshwar P,
Moerbe W, Rochlitz R, Steuer A, Costabel S,
Smirnov MY, DESMEX Working Group (2019)
Three-dimensional inversion of semi-airborne datafrom the flight experiment over an ancient mining
area near Schleiz, Germany. Geophysics 84:
E281—E292

Siripunvaraporn W, Uyeshima M, Egbert G (2004)
Three-dimensional
inversionfor
networkmagnetotelluric data. Earth, Planets and Space 56:
893—902

Figure 4. 3-D Weidenpesch waste site model. Horizontal xy slices at different depths. Black lines indicate thelocation of the source, and white triangles – represent sites.

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Hybrid GPU solution to regularized divergence-free curl-curl equations forelectromagnetic inversion problems
1,2

3

4

5

Hao Dong *, Kai Sun , Gary Egbert , Anna Kelbert and Naser Meqbel

61

China University of Geosciences, Beijing,
State Key Laboratory of Geological Process and Mineral Resources, Beijing
3
Nvidia Corporation, Beijing
4
Oregon State University, Corvallis
5
United States Geological Survey, Denver
6
3D Consulting-GEO GmbH, Berlin*donghao@cugb.edu.cn

2

SUMMARY
The Curl-Curl equation is the key to the time-harmonic electromagnetic (EM) problems in geophysics. The
efficiency of its solution is decisive to the performance of EM simulations, which account for over 90% of thecomputation cost in inversions like Magnetotellurics or controlled source EM problems. However, mostpublished EM computation codes are still CPU-based and cannot utilize the recent computation techniqueswith GPUs. Based on the previously proposed divergence-free algorithm developed on CPUs, this study
demonstrates the current limits of the CPU-based inversion procedure. To exploit the high throughput
computational ability of GPUs, the study proposes a hybrid CPU-GPU framework to solve the forward andadjoint problems of the EM inversions. The large sparse linear systems arise from the staggered-grid finite
difference approximation of curl-curl problems are solved with a new mixed-precision Krylov subspace solver.
The algorithm is implemented with the ModEM modular inversion package and with both synthetic andreal-world magnetotelluric examples. The results show a promising 15-30x speed-up for the solution stage ofthe curl-curl equations over single-CPU calculations. On real-world inversion test cases, the overall
performance of a GPU-attached computation node with the new hybrid framework is comparable to that offour CPU-only nodes with conventional ModEM implementation. This would make the large-scale frequency
domain EM inversions possible on smaller modern GPU platforms with reduced carbon footprint.
Keywords: divergence-free, curl-curl equation, GPU, magnetotellurics, inversion

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Speeding up the inversion of the 3D MT problem
D. Varılsüha1
1Istanbul Technical University, deniz.varilsuha@itu.edu.tr

SUMMARY
In this study, I have investigated different algorithms that can greatly reduce the inversion time of a 3D
Magnetotelluric problem. These algorithms include the right choice of the partial differential equations to solve
an EM problem, a hybrid finite-difference and finite-elements numerical technique, employment of multiple
GPUs for the calculations, the mesh decoupling technique, and other good implementation practices. When
all of them are applied in conjunction, the inversion time for a medium-sized 3D problem can be completed inaround an hour.
Keywords: 3D, Magnetotellurics, GPU computing, Hybrid Numerical Technique.
Introduction
Any 3D interpretation or inversion of geophysicaldata is computationally challenging. Researchers
who develop such software have to deal with longcompute times that can vary from hours to days toobtain reasonable 3D models. In reality, an
inversion tool should be rerun several times withdifferent parameters to be sure of the final results.
This may even lead to greater compute time andalso it limits the possibility of one experimenting withthe existing code. In this study, the 3D MT inversion
problem is investigated and different algorithms areapplied to the forward and inverse parts of the
problem to speed up the inversion.
Methods
For the MT problem, either second-order E field or
H fields obtained from Maxwell equations are solvedin quasi-static approximation for the forward
problem. The resulting linear system can be solved
either by direct methods such as LU decomposition
or iterative methods such as conjugate gradients(CG). The latter approach is better and more
efficient for larger systems. However, the iterative
solution’s convergence might be affected by roundoff errors and it may need an additional routine
called divergence correction of E fields for low
frequencies as the linear system gets more andmore ill-conditioned when the frequency value getscloser to zero. For that reason, there are manydifferent partial differential equations (PDE) to solve
for various reasons to obtain the same fields.
Varılsüha and Candansayar (2018) investigatedthese PDEs that can be solved iteratively in the least
amount of time and the following is found to be thefastest when compared to others.
∇𝑥∇𝑥𝐀 + μ0 σ(iω𝐀 + ∇ψ) = 0μμ0 ∇. σ𝐀 + 0 ∇. σ∇ψ = 0.

(1)
(2)

E-fields are calculated via
𝐄 = −iω𝐀 − ∇ψ

(3)

iω

The iterative solution of the coupled system of (1)and (2) reduce the number of iterations required byhalf on average when compared to the classical Efield formulation.
The most common numerical techniques to solve a
given problem are finite-differences (FD) and finiteelements (FE) approaches. When a model has a flat
surface, it is a common approach to use simple
blocks and employ the FD technique due to its
simplicity. The FE technique is necessary when
distorted hexahedral elements or tetrahedral
elements are present. Implementation-wise, it canbe a good approach to start with a structured andnon-distorted mesh. Later topographic features
such as hills and valleys can be added by distorting
the mesh. This will result in a partially distorted
mesh and the suggested technique by Varılsüha(2019) can be used to take advantage of both FE
and FD techniques by employing both of them in asingle mesh.

Figure 1. A depiction of the hybrid numerical
technique is given for a 2D mesh. While thedistorted FE region is solved with finite elements,
the undistorted blocks are solved with finitedifference methods
The main advantage of this hybrid technique is its
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computational cost. The linear systems resulting
from the pure FD technique can be solved with halfof the time required by the FE technique. It also
requires one-third of the memory required by the FE
method. The meshes formed and used in this study
have only 5 to 20 percent of their cells distorted andthus require the FE method. So, this ultimatelymeans that the hybrid method is almost as efficientas the FD approach while being able to represent
the topographic features for the model used in thisstudy. It is also worthy to state it reduces therequired memory space which could be very
important when GPUs are used to solve theequations and considering their limited amount ofmemory space.
It is the most straightforward approach to use asingle mesh for all data, frequencies, and receivers.
It is also a common approach to use multiple smaller
meshes for different receivers to reduce
computational overburden, especially for airborne
EM and time domain EM studies. It can be possible
to employ this technique for the frequency domain
MT problem. Varılsüha (2020) showed that different
meshes can be created for different frequency
groups since higher and lower frequencies havedifferent penetration depths. In this study, the same
technique is employed and smaller meshes are later
mapped to the parameter mesh with a mapping
matrix. Figure 2 depicts a picture of a parameter
mesh and two local meshes for a high-frequency
group and a low-frequency group. By using different
smaller meshes for different frequencies, as
opposed to using a single large mesh, it waspossible to at least halve the total number of cells
for the forward modeling meshes used in this study.
This ultimately means halving the time for theforward modeling and thus inversion time.
High performance and parallel computing can also
reduce computational time. It is common to use
multi-core CPUs or multiple CPUs in a workstation
to divide up the work using libraries such as MPI or
OpenMP. In the last decade, GPU computing has
become popular and the platform itself has shownto have the potential to speed up numericalcomputations. It is also possible to plug in multiple
GPUs to a single computer to speed-up calculations
even more. This could be also an important feature
because one doesn’t need to deal with large andcomplicated shared or distributed memory clusters
since it is easy to plug in more GPUs to a single
computer and it is easy to configure the new
hardware. For these reasons, in this work, I haveused 8 GPUs (Nvidia RTX 3090) along with a 10core CPU (Intel 10900K) to do the numericalcrunching using Matlab. The experimentation
showed that using 8-GPUs in parallel speed-up thecomputations by 8-fold when compared with parallel
computing using the multi-core CPU.

Figure 2. a) The parameter mesh and the forward
modeling mesh without the mesh decouplingtechnique. b) mesh for the highest frequency groupand c) mesh for the lowest frequency group whenthe mesh decoupling technique is employed. The
dashed line shows the barrier between FE and FD
regions for the hybrid numerical technique.
A regular consumer-grade CPU could have a core
count between 1 and 64. On the other hand, aconsumer-grade GPU might have a core count of
10000. In addition to that, GPUs have their
dedicated memory and the memory bandwidth on
the GPU can exceed the bandwidth between CPU
and RAM by 10 to 20 times. This is an importantdetail because the linear algebra for sparse
matrices is often memory-bounded as opposed tobeing compute-intensive. This means higher
memory bandwidth of a system can greatly reducethe computation times. For that reason, multiple
GPUs are employed to solve the linear system
iteratively using a Conjugate gradient (CG) methodin this study to achieve significant speed-ups.
However, to make the algorithm converge to asolution in a fast manner, the CG system must be
preconditioned using a preconditioner matrix.
Usually, the preconditioner matrix is decomposed
into two triangular matrices using the incomplete
lower-upper algorithm (ILU(0)) and it is applied to agiven vector in every step of the iterative solver by
forward and backward sweeping. This operation
can be challenging for GPUs because sometimes it
can be difficult to find parallelism for a given
preconditioner matrix. My experimentation also

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confirmed that 80 to 90 percent of GPU time is spent
doing the triangular matrix solutions. I also noticed
that solving larger linear systems makes the GPU
more efficient, however, this ultimately means larger
models and longer inversion times. To make thecomputations more efficient, I came up with an
implementation detail that resulted in a 15-20
percent decrease in forward modeling time. For the
3D MT problem, a linear system such as 𝐴𝑥 = 𝑏 issolved for two polarizations. Instead of solving it
twice for different RHS vectors, I’ve found thatsolving the following is more efficient,
𝐀
[
0

b
0 x1
] [ ] = [ 1]b2
𝐀 x2

and vectors respectively. In this study, they are
estimated as a parameter to obtain much correct
inversion.

(4)

where 𝑥1 and 𝑥2 are the solution for two
polarizations. This technique is implemented in the
inversion algorithm fully.
To test all the techniques presented in this work all
at once, a previously published dataset that would
require a medium-sized model is chosen. Thisdataset is also inverted by other people so it waspossible to compare the inversion results to test theaccuracy of this algorithm. The dataset named two
mountain model’s data is first created and used by
Usui (2015) and I obtained it by contacting theauthor. It has 40 receiver points and 16 frequencies.
Every receiver has 4 components of the impedance
tensor and 2 components of Magnetic Transfer
Function (MTF) for all frequencies. The data also
has additional noise and real-valued distortion
components to make it harder to invert. In this study,
I inverted the data to obtain the original model andthe distortion tensors as parameters. In Figure 3 thetrue model that the data is obtained from is given.
The inversion is carried out by minimizing thefollowing objective functional by the L-BFGS
algorithm.
ϕ(𝐦𝛔 , 𝐦𝐂 ) = ϕd (𝐦𝛔 , 𝐦𝐂 ) + λϕm (𝐦𝛔 ) + κϕC (𝐦𝐂 )(5)where ϕd , ϕm and ϕC are data misfit, modelroughness, and distortion intensity. λ and κ aretrade-off parameters that would be adjusted as theinversion is carried out. 𝐦𝛔 and 𝐦𝐂 are model
parameters for the conductivity and the distortion
tensor that are being obtained in inversion.
In Figure 4, it can be seen the inversion results are
side by side with the true model. In this inversion,
the impedance tensor (Z) and the MTF vector (W)are used as data. In addition to those, the distortion
tensor for both Z and W is estimated as a parameterto obtain better models. Distorted tensors could be
expressed as
𝐙D = 𝐂h 𝐙(6)
𝐖D = (𝐖 + 𝐂z 𝐙)(7)

𝐙D and 𝐖D are the distorted forms of data. 𝐂h and𝐂z are the horizontal and vertical distortion tensor

Figure 3. The figure is taken from the work by Usui,2015. a) and b) shows the surface topography indetail. c) and d) show the exact spot and sizes ofconductive and resistive anomalies. In this study, x
and y directions are reversed and named y and x
directions respectively.
Results
It took 45 inversion steps to obtain the results given
in Figure 4. The location of both conductive andresistive anomalies are found correctly however the
resistivity value of the resistive structure isestimated at 300 Ω𝑚 which is far away from its true
value. However, considering the nature of the MT
method and its insensitivity to resistive structures,
these findings can be considered normal. The same
inversion is also carried out by using only the
impedance data (while estimating the distortion
tensor also) and a very similar model is obtained
from it. However, it took 52 steps to complete theinversion and this ultimately means that the joint
inversion solves the problem in a lesser amount oftime. It should be noted that the joint inversion ofdifferent data types can potentially reduce thecomputation times significantly.
The model given in Figure 4 had 116K parametersand 50 31 and 75 cells in x, y, and z directions
respectively. Using all techniques described in thisstudy helped to reduce the total inversion time to anhour. Even though the inversion code requires
special hardware such as GPUs to run, thealgorithms described here can be coded for any
architecture and run on any hardware. These

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results also prove that the same techniques could
be applied to other EM problems or other elliptic
problems to achieve similar results.

mean that a coarser mesh can be adapted and thesame accuracy of a single mesh could be achieved.
The important detail about the Lebedev grids is thatthey will generate 4 independent meshes for theisotropic case instead of one. When coupled with
the idea given in Equation 4, it will make the forward
solution on a GPU even more efficient. I haveexperimented with this idea and a model with no
topography, and I’ve seen a further increase inefficiency. However, due to the topography of themodel used in this study, it is not considered here.
Conclusions
There is no single answer when it comes tospeeding up a geophysical inverse problem. In thisstudy, I show that various techniques must be
implemented together to achieve significant results.
Simple parallelization techniques or better/faster
hardware alone may not be enough. A correct
choice of the PDE, hybrid numerical techniques, themesh decoupling approaches also play a great roleto achieve exceptional timings because every
technique shown in this study has the potential toreduce the computational time significantly however
when all of them are employed together the final
speed-up is the multiplication of the speed-ups of allof them. This study concludes that these techniques
can reduce the total inversion time by almost 2
orders of magnitude.
References

Figure 4. The true model and the model obtainedby the joint inversion of impedance tensor and MTF
vector. The dashed lines indicate the position of true
anomalies. The inversion is performed by jointly
inverting the impedance (Z) and MTF (W) data while
estimating the distortion tensor (DT)Discussion
When dealing with anisotropic forward modeling for
an EM problem, the Lebedev grids can be employedespecially when an FD grid is in mind with E fields
defined on the edges. This not only helps with thenumerical discretization of the anisotropic PDE but
also helps to reduce the size of the mesh. The
Lebedev grid technique requires 3 more meshes
shifted to the original mesh so the total
computational time seems to get quadrupledhowever at the same time it significantly increases
the accuracy of the forward solution. This can also

Usui Y (2015) 3-D inversion of magnetotelluric datausing unstructured tetrahedral elements:
applicability to data affected by topography,
Geophysical Journal International 202, 828-849.
Varılsüha D (2019) Fast Three Dimensional
Inversion of Magnetotelluric data based on Finite
Difference and Finite Element Hybrid Forward
Modeling, Ph.D. thesis, Ankara University,
Ankara.
Varılsüha D (2020) 3D inversion of magnetotelluric
data by using a hybrid forward-modelingapproach and mesh decoupling, Geophysics 85,
E191-E205.
Varılsüha D, Candansayar ME (2018) 3D
magnetotelluric modeling by using finitedifference method: Comparison study of different
forward modeling approaches, Geophysics 83,
WB51-WB60.

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Open-source 3D inversion of semi-airborne electromagnetic data
R. Rochlitz1 , T. Günther1 and M. Becken2
Leibniz Institute for Applied Geophysics, Hanover, Germany, raphael.rochlitz@leibniz-liag.de
2
Department of physics, University of Münster, Germany, michael.becken@uni-muenster.de

1

S UMMARY
We present a new 3D inverse modeling scheme for semi-airborne controlled-source electromagnetic databuilding upon the open-source modeling software custEM and the open-source inversion framework pyGIMLi.
The underlying geometries include arbitrarily shaped transmitters on the surface with topography and airborne
receivers in the air or on the ground. The topography prohibits the exploitation of secondary-field formulations
with semi-analytic primary-field solutions, such as commonly used in marine or airborne environments. We
are able to account for realistic survey geometries with an irregular tetrahedral discretization of the modelingdomain. We use a finite-element electric-field approach with a total-field formulation and first- or (preferably) second-order polynomial basis functions as implemented in custEM. Solving the resulting linear systemsof equations with a direct solver enables us calculating explicit sensitivities with comparatively cheap backsubstitutions for hundreds of airborne or ground receiver stations in multiple flight areas with several transmitters. The pyGIMLi framework provides a fast-converging Gauss-Newton minimization scheme for the inversion
process. We apply our developed tools on a synthetic model and a semi-airborne data set of combined flightareas with 4 transmitters. The data with 12 frequencies in the range of 7 to 1000 Hz cover a total area ofapproximately 50 km2 and provide a penetration depth of up to 1000 meters. Our final 3D resistivity model
matches well the available geological information. Reference inversion results from the analysis of 2D ERT
and EM data on a central profile are overall consistent with the 3D inversion results but provide also insights
about the limitations of 2D methods in a 3D geological environment. The developed procedure is not only
applicable for semi-airborne data sets, but also capable of handling other controlled-source electromagnetic
survey geometries. The presented results and tools are freely available as the underlying software.
Keywords: numerical solutions, electromagnetic theory, inversion, finite element method, controlled-source
electromagnetics

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Abstract, 25th EM Induction Workshop, Çeşmeı, Turkey, September 11-17, 2022

Application of the total and scattered field decomposition and perfectly-matchedlayers to improve the accuracy in electromagnetic modelling
L. M. Buntin1 , T. Kalscheuer2 , G. Kreiss3 and Z. Ren4
Dept. of Earth Sciences, Uppsala University, Uppsala, Sweden, laura.buntin@geo.uu.se
2
Dept. of Earth Sciences, Uppsala University, Uppsala, Sweden, thomas.kalscheuer@geo.uu.se
3
Dept. of Information Technology, Uppsala University, Uppsala, Sweden, gunilla.kreiss@it.uu.se
4
School of Geo-science and Info-Physics, Central South University, Changsha, China,renzhengyong@csu.edu.cn
1

S UMMARY
Inhomogeneous Dirichlet boundary conditions are routinely applied in 2D magnetotelluric forward modelling.
However, the finite size of the modelling domain results in false reflections of the anomalous field at the boundaries, because the prescribed boundary fields are normal fields. Hence, the anomalous field is not accounted
for, and the solution accuracy may be significantly reduced. To eliminate such false reflections, we introduce
the total and scattered field decomposition (TSFD) to geophysical modelling. This makes it possible to apply
fully absorbing boundary conditions to the scattered (often anomalous) field, and we apply perfectly-matchedlayers. In the TSFD, the modelling domain is split into two regions: in the total-field region, encompassing
receiver sites and geological targets, the unknowns are total-field values, and in the scattered-field region,
consisting of boundary regions, the unknowns are scattered-field values. At the TSFD interface, that separates
these regions, the incident plane-wave field is impressed as a source. At the outer boundaries of the scatteredfield region, perfectly-matched layers attenuate the outward propagating scattered field to zero. We develop
TSFD setups for two types of models. The horizontal TSFD divides the domain along a horizontal TSFD interface in air, with perfectly-matched layers at the top and Dirichlet boundary conditions at the other boundaries.
It is designed for models with different layerings at the left and right boundaries. In such cases the total fieldat the top boundary cannot be correctly computed, which always leads to reflections using standard Dirichletboundary conditions. The horizontal TSFD solution is shown to be significantly more accurate. The surrounding TSFD is designed for horizontally layered background models. Here, the scattered field region comprises
all boundaries, which can be placed closer to the area of interest without diminishing solution accuracy byusing perfectly-matched layers. This results in significantly diminished computational problem size.
Keywords: total and scattered field decomposition, perfectly-matched layers, finite-element modelling, computational accuracy

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

An efficient 3D EM modeling scheme based on a radiation boundary approach

1

Rahul Dehiya1 and Arun Singh2
Indian Institute of Science Education and Research, Pune, rahul.dehiya@iiserpune.ac.in
2
Indian Institute of Technology (Indian School of Mines), Dhanbad, aruns@iitism.ac.in

A BSTRACTWe present the development of an efficient 3D forward modeling algorithm of electromagnetic data based ona radiation boundary scheme. The proposed scheme computes the response at the desired resolution in two
steps. The first step involves coarse-grid finite-difference modeling and the computation of a radiation boundary
field vector at the boundaries of a relatively fine mesh. In the second step, modeling is performed for the finemesh with boundary conditions modified using the computed radiation boundary vector. An initial solution
derived from coarse-grid modeling is also used for fine-grid modeling. The fine-mesh discretization includes
partial or no air medium and does not contain the stretched grid. The boundary of the finer mesh can have an
arbitrary shape that depends on the sensors’ positions. The ability to have an arbitrarily shaped boundary of
the computational domain optimizes the computation in forward modeling and reduces the degree of freedom
when the algorithm is used in an inversion. The proposed algorithm derives computational efficiency froma stretch-free discretization, air-free computational domain, and a better initial guess for an iterative solver.
The proposed scheme is based on the finite-difference method; however, the concept of radiation boundary
conditions can be employed along with other numerical techniques as well. The robustness and versatility of
the algorithm are illustrated for both controlled-source electromagnetic and magnetotelluric data. Numericalexperiments demonstrate that the developed algorithm is one order faster than the finite-difference modelingalgorithm in most of the cases presented. The scheme is particularly suitable for data analysis of large models
resulting due to the fine discretization or the large survey area such as USArray and AusLAMP.

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Electromagnetic imaging using high-order FE and goal-oriented meshing
O. Castillo-Reyes1 , P. Rulff2 and E. S. Um3
Barcelona Supercomputing Center (BSC), octavio.castillo@bsc.es
2
Department of Earth Sciences, Uppsala University, paula.rulff@geo.uu.se
3
Lawrence Berkeley National Laboratory, Earth and Environmental Sciences, evanum@gmail.com
1

S UMMARYElectromagnetic (EM) modeling routines development has increased in the last decade. Given the recent
advances in numerical methods and increased computing power, there are several options to solve the forward
EM problem today. However, regardless of the computational meshes used, most of these modeling tools do
not support high-order polynomial discretization methods and adaptive meshing.
In this work, we present numerical experiments about goal-oriented adaptive mesh refinement and the highorder edge finite element method (HEFEM) for the solution of challenging 3D controlled-source electromagnetic(CSEM) surveys. Each test case presents a particular modeling challenge (e.g., topography, presence of
metallic boreholes), suitable for studying the numerical schemes capacities in realistic setups. We use two
3D EM modeling routines to compute synthetic EM responses: elfe3D and PETGEM. We investigate theperformance of the goal-oriented meshes and HEFEM discretizations in terms of accuracy and computational
cost (e.g., run-time, memory consumption).
Our assessment provides relevant information for an in-depth understanding of the pros and cons of employing HEFEM and goal-oriented meshes. The numerical experiments show that high-order polynomial basis
functions in conjunction with goal-oriented meshes can obtain synthetic EM responses in agreement with thereference. This conclusion is valid for all the test cases under consideration. However, the code performance
depends on the input model: frequency, resistivity, scale variations, mesh quality, source discretization, andcomputational architecture. Therefore, an aware meshing scheme that considers the polynomial basis order isrequired to obtain competitive performance ratios. We believe that our numerical experiments will prove usefulfor the EM community.
Keywords: Geo-electromagnetics, numerical solutions, goal-oriented refinement meshing, high-order finite
element, parallel computing

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96

Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

2D Electrical Resistivity Modelling on highly distorted, non-smooth, rough grids

1

Deepak Suryavanshi1 , Rahul Dehiya1
IISER Pune, deepak.suryavanshi@students.iiserpune.ac.in
2
IISER Pune, rahul.dehiya@iiserpune.ac.in

S UMMARY
DC Resistivity method is a geophysical technique that has been widely used in subsurface investigations
such as groundwater mapping, critical zone studies, and mineral exploration. In this study, we present a 2Dmodeling of the DC Resistivity data, keeping in mind that the constructive approach should be based on thevery fundamentals of the governing physics and should be able to extend its realms for the complicated cases
as well. We have developed a C++ code that aims at solving the 2D DC resistivity problem.
The novelty of our development lies in the construction of the proposed finite difference scheme (FDs) thatis based on the Mimetic finite difference methods (MFDM). MFDMs are a class of numerical methods that
attempts to mimic the fundamental properties of the governing physics like the conservation laws, symmetry
properties, and discontinuity of the coefficients. It further enables the development of modeling algorithms
based on non-orthogonal grids required to account for irregular topography. It is difficult for the FDs to accommodate non-orthogonal grids and, hence, varying topography. The finite elements (FEs) methods are generally
used for modeling variable topography models. However, FDs have advantages over FEs in inverse modelingwhere dual discretization is not required in FDs, as is the case with FEs. The MFDM meets both of theseobjectives, making them a suitable scheme for DC modeling. Hence, we implemented MFDM to develop an
algorithm that solved the DC resistivity problem.
A dyke model is utilized to verify the accuracy of the developed algorithm by comparing the numericallysimulated responses to the analytical solution. The benchmarking exercise is done on varying conductivity
contrasts for the dyke models to establish the algorithm’s accuracy. Further, the developed algorithm is tested
for different 2D models by comparing its responses with a 3D modeling algorithm. The proposed algorithm iscapable of handling anisotropy. Hence, the developed algorithm is suitable for analyzing DC data of anisotropy
and variable topography subsurface. We have carried out a series of experiments to establish the robustness of
our code when the grids become very irregular, non-smooth, and the orthogonality is violated. This is achievedby distorting the orthogonal girds using pseudo-random numbers, which follow a uniform distribution. The
angles for all the grid nodes are calculated, and the ones having an angle outside 20 to 160-degree intervals
are classified as highly distorted. The numerical tests are conducted on highly distorted cells ranging from 1%
to 10% of the total cells. The error analysis suggests that the algorithm performs very well for non-orthogonal,
non-smooth, highly distorted rough grids and the observed errors were appreciably low.
Keywords: 2D DC Resistivity Modelling, MFDM, Non-orthogonal rough grids.

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Abstract, 25th EM Induction Workshop, Kuşadası, Turkey, September 11-17, 2022

Regularization of VLF inversion using rank order smoothingGökhan Karcıoğlu1, Anisya B.Tekkeli1, Ümit Avşar2, Mehmet Ali Üge1,3, Mehmet Safa Arslan1

1 Istanbul University-Cerrahpasa, Engineering Faculty, Department of Geophysical Engineering. Istanbul

34500, Turkey, gkarci@istanbul.edu.tr
2 Istanbul Technical University, Mining Faculty, Geophysical Engineering Department, Ayazağa, Turkey,

avsaru@itu.edu.tr
3 Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Showa-machi 3173-25, Kanazawa-ku, Yokohama 236-0001, JapanSUMMARY

Least-squares inversions are generally applied to recover resistivity distribution models from electromagnetic
induction data. Since the defined inversion problems are underdetermined and ill-posed, various operators are
implemented to regularize the process. Smoothing operators, which are simple finite difference matrices
penalizing the resistivity differences between neighboring cells, are implemented in the most applications andaims to result with smooth structure boundaries. These smooth models often have over-smoothing structure
boundaries which are rather difficult to interpret. Considering this, we have defined a smoothing operator basedon rank order filtering. These non-linear filters are known with their ability to eliminate low-frequency changes
while keeping boundary information. In image processing, rank order filters are implemented by determining
ranks of pixels within a given filter window and assigning the value of the pixel corresponding to the desiredrank, defined by the user, to the central pixel. Filtering is completed by repeating the process for all pixels.
Correspondingly, we have penalized the difference of each cell from its neighboring cell corresponding to thedefined rank value. Contributions from other cells are also calculated and damped with a Gaussian curve
according to their difference from the defined rank. In our implementations we have used median (50%) as thedesired rank value since higher or lower values were biasing the inversion to minimize resistors or conductors.
In the trials with synthetic data, inversion with the rank order smoothing is observed to be able to recoverboundaries successfully while minimizing misfit. For the field implementation, we used the VLF-R
measurements collected over the rupture area of the North Anatolian Fault Zone. The data are modeled using
both rank order smoothing and traditional smoothing operator for comparison. The recovered models showthat the rank order smoothing resulted with models with more evident boundaries, revealing the known faults
better.

Keywords: VLF-R, North Anatolian Fault Zone, 2D Inversion

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Gradient and roughness regularization operators for geophysical inversion on
unstructured meshes using `2 and `1 norms

1

M. Kangazian1 , C. Farquharson1
Memorial University of Newfoundland, St. John’s, NL, Canada, Department of Earth Sciences.
mkangazianka@mun.ca, cgfarquh@mun.ca

S UMMARY
Minimum-structure, or “Occam’s”, style of inversion requires proper regularization strategies to gain sensible
geophysical models. Designing the spatial matrix operators required for the regularization term for unstructured meshes is not as straightforward as for structured meshes because of the complex geometric and spatial
properties of unstructured meshes. Perhaps the simplest method of calculating the spatial gradient operators
for unstructured tetrahedral meshes is the one that calculates the physical property differences across all internal mesh faces. However, this type of regularization is not able to incorporate structural orientation informationsuch as strike, dip, and tilt angles into an inversion, which can be particularly important information for survey
methods with limited depth resolution such as gravity and magnetics, as well as for electrical and electromagnetic methods such as magnetotellurics. Hence, other methods have been proposed for calculating thegradient operators for unstructured tetrahedral meshes that allow one to incorporate the structural orientation
information of geological features into the inversion framework and obtain more sensible geophysical models.
The majority of these methods consider a cell along with the nearest neighbours of that particular cell as a
package to prevent a patchwork pattern being created, and commonly an `2 norm is used for the measure of
the regularization term. These methods work well and allow the orientation information of a geological structure
to be incorporated into the inversion to give desirable methods. However, the models constructed using theseregularization methods are not as sharp as hoped for when the regularization function uses `1 -norm measuresinstead of an `2 norm due to the package issue.
In this study, the effect of the package issue is investigated for the scenarios in which an `1 -norm measure isemployed in the regularization term. Also, the method that calculates the gradient operators across the interior
faces of the mesh is extended so that geological orientation information of a body can be incorporated into an
inversion. It shall be shown that the models constructed by this method have sharper boundaries compared tothe models constructed using methods that consider each cell as a package with its neighbours.
Keywords: Inversion, Regularization, Unstructured tetrahedral meshes

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1D and 3D Inversion and Modelling of Airborne TransientElectromagnetic and Magnetic Data From Over a Potential Volcanogenic
Massive Sulphide Deposit, Cripple Creek, Newfoundland
Alican Demirbas1, Colin G. Farquharson2
1General Directorate of Mineral Research and Exploration, Ankara, Turkey, alican.demirbas@mta.gov.tr
2Memorial University of Newfoundland, Department of Earth Sciences, St John’s, Canada,

cgfarquh@mun.ca

SUMMARYElectromagnetic (EM) methods, including transient electromagnetics (TEM), and magnetic methods arecommonly used in mineral exploration. In this study, airborne TEM, ground-based frequency-domain EM andmagnetic data are used to investigate possible mineralization zones suggested by the airborne TEM data on
the Cripple Creek Property, in the Gander area, Newfoundland, Canada. This study’s main goal is to invert the
airborne TEM data in 1D to recover the conductive structures that are possible mineralization zones. Airborne
magnetic and ground-based frequency-domain EM data are also considered. The ground-based frequencydomain EM data, which are available for just a limited area, and the TEM responses are compared and theirrespective responses shown to be mostly consistent. A 3D inversion of the magnetic data not only helps better
define the physical properties of the targets identified from the EM data-sets but also helps define thebackground regional geology. The geometry of a mineralized zone suggested by the TEM inversion is thenrefined by further forward modelling (using the software “Maxwell”). The resulting models are compared anddiscussed in terms of reliability of the inversion. The results show that the 3D models of the conductivity
generated from the stitched together 1D TEM inversion results recover the near-surface structures quite well
but become inaccurate at depth. It is shown that the models resulting from the 3D magnetic inversion are
partially consistent with the TEM results. This is possible because of the physical property of the structure ofinterest, which means that the area of interest does not show the same magnetic properties everywhere. Bothmagnetic and conductive features are only coincident on one of the flight lines. However, when the magnetic
inversion result is considered separately, it helps to reveal the magnetic properties reasonably well around thestructure of interest.

Keywords: Airborne Transient EM, Magnetic, Inversion

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Abstract, 25th EM Induction Workshop, Çeşme,Turkey,September 11-17, 2022

2D U-Net convolutional networks for 1D inversion of magnetotelluric data
Mehdi Rahmani Jevinani1, Banafsheh Habibian Dehkordi2, Mohammad Hossein Rohban3 and Ian J.
Ferguson4
1
Institute of Geophysics, University of Tehran, Tehran, Iran, mehdirahmanijevinani@yahoo.com
2
Institute of Geophysics, University of Tehran, Tehran, Iran, bhabibian@ut.ac.ir
3
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran, rohban@sharif.edu
4
Dept. of Geological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada,
Ij.Ferguson@umanitoba.ca

SUMMARY
Deep convolutional networks as one of the main developments in deep learning approaches have been
applied increasingly in geophysical inverse problems in recent years. The capabilities of 2D U-Net
convolutional network for 1D inversion of magnetotelluric data are examined in this study. We apply an
innovative method to generate the large number of sample data that are generally required by deeplearning-based approaches. Layers with fixed and variable thicknesses and (apparent resistivity) data withand without phases are considered. From the total of 1 million data samples, 95% of them are used astraining and validation sets and 5% as test data. Input data are scaled for training and rescaled to form output
data or predictions.
The original 2D U-Net is used for segmentation problems. To 1D inverse modeling of magnetotelluric data, a
2D U-Net with 36 layers is designed including two main modifications: First, at the beginning and end of thecompression and decompression path, filter 32 is replaced by filter 64. Second, instead of tow, fourconvolutional layers are used for each filter. The kernel size is 3ൈ2 and batch normalization and ReLU
activation functions are considered. Dropout ratios of 0.5, 0.3 and 0.1 are also added to prevent overfitting.
Finally, the Adam algorithm with learning rate of 0.0001 and Huber loss are used as optimization algorithm
and loss function, respectively.
In the random design of one-dimensional models, there is a possibility of creating layers that due to the valuesof their electrical conductance cannot be easily identified in magnetotellurics. Therefore, subsurface resistivity
values as outputs of the deep learning model are also recovered with similar limitations in resolution.
Keywords: Magnetotellurics, Inverse Modelling, Convolutional Neural Networks

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Abstract, 25th EM Induction Workshop,Çeşme, Turkey, September 11-17, 2022

3D Minimum-structure Inversion for CSEM Problems Using Potentials andUnstructured Tetrahedral Grids
Kadir B. Kara1,2 , Colin G. Farquharson1
Memorial University of Newfoundland, Department of Earth Sciences, St. John’s, Newfoundland A1B 3X5,
Canada.
2
Kocaeli University, Faculty of Engineering, Department of Geophysical Engineering, Kocaeli 41380, Turkey.
E-mail(s): kbkara@mun.ca; cgfarquh@mun.ca.
1

S UMMARYElectromagnetic (EM) methods, including profiling and sounding techniques, are used to determine the changes
in the electrical conductivity of the earth with both depth and laterally. Frequency-domain EM measurements
are conducted at a number of frequencies at fixed source and receiver locations, with the strength of themeasured EM fields depending on the earth’s conductivity.
Many methods have been used for the numerical modelling of controlled-source electromagnetic methods(CSEM) as a result of developments in computer science. In this study, the finite-element (FE) method inwhich differential equations are weighted, or a functional minimized whose minimum occurs at the solution
to the differential equations is used. CSEM modelling can be done by using either direct E-field methods or
potential methods. A potential formulation, specifically the decomposition of the electric field into vector andscalar potentials for the Helmholtz and the conservation of charge equations, namely an A-φ decomposition,
is used. Vector and scalar basis functions are used for the potentials. The equations are discretized using theweighted residual method, which results in a sparse linear system. The resulting linear system is solved bya sparse direct solver with LU factorization. Modelling domains are subdivided into unstructured tetrahedral
grids which are suitable for real geological features such as contacts between rock units and for topography.
Inversion methods can be mainly separated into local (gradient-based) and global methods. Global methods
have the ability to reach the global minimum and use less memory. These methods, however, have to call theforward-modelling operator too many times, and hence, gradient-based methods have become more attractive
for higher-dimensional inversions. A minimum-structure inversion procedure based on the Gauss-Newton(GN) method which is one of the gradient-based methods is used for the inversion. The procedure aims tofind the simplest and most robust model that reproduces the observed data by dividing the problem region
into many fine cells. Therefore, the inversion procedure is a highly under-determined inverse problem that
achieves the goal by iteratively minimizing an objective function that includes data misfit and model structureterms. Iterative preconditioned conjugate gradient (CG) and nonpreconditioned generalized minimal residual(GMRES) methods are used to solve the linear systems of equations for the model updates. These solvers do
not request explicit calculations of the matrices; therefore, this significantly reduces memory demand.
The aim of this study is to develop a powerful and flexible interpretation tool for CSEM problems. The new algorithm was tested with three synthetic examples. One of the three examples was a realistic model that includes
an amorphous body and topography. The EM fields (e.g., Ex and Hz ) were used as data, and Gaussian random noise was added before inversions. The inversions of models were completed with a common-or-garden
laptop and in reasonable time e.g., 85 hr. Both iterative solvers could reduce the residual norms down to thedesired level. The recovered models were almost the same for both iterative solvers, and the buried bodies of
the three examples were fully recovered.
Keywords: Inversion, Potentials, CSEM, Unstructured Grids

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

ModEM software:
An update on the improvements, availability, and performance metrics
Kelbert, A.1,2, Egbert, G.2, Dong, H.3, Meqbel, N.4, Zhongyin, L.5
1 Geomagnetism Program, US Geological Survey, Golden, CO, USA
2 Oregon State University, Corvallis, OR, USA

3 China University of Geosciences, Beijing, China
4 3D Consulting-Geo GmbH, Berlin, Germany

5 Institute of Geology, China Earthquake Administration, Beijing, China

SUMMARYModEM 3D is a parallelized Fortran magnetotelluric (MT) modeling and inversion code [Egbert and Kelbert,
2012; Kelbert et al., 2014] that is freely available for academic use, and has been widely used for 3D MT
modeling and inversion by the global MT community. Since the code was initially released, multiple extensions
and improvements have been implemented. An upcoming release of an updated version of ModEM allows forflexible air layers and boundary conditions, flexible data orientations, and improved memory usage. It also
includes an efficient sparse-matrix solver, a variety of inversion algorithms, and provides (with some limitations)for MT modeling in spherical coordinates. This updated version of ModEM 3D will be better suited for themodeling of electromagnetic induction in the Earth in the context of modern large-scale MT and space weather
applications. We will report on the timelines and procedures for availability of these improvements and therelated performance metrics, and on the budding efforts to share and distribute auxiliary toolboxes that provide
assistance to scientists in setting up ModEM software and in the analysis of results.
Keywords: Magnetotelluric Inversion, Numerical Modeling, ModEM Community of Practice

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

A MATLAB FE Library for the Simulation and Inversion of EM Problems
Jan Blechta1 , Ralph-Uwe Börner2 , Oliver Ernst1 , Mathias Scheunert2 and Klaus Spitzer2
1
Faculty of Mathematics, TU Chemnitz, Germany, jan.blechta@math.tu-chemnitz.de,
oliver.ernst@math.tu-chemnitz.de2
Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Germany,
rub@geophysik.tu-freiberg.de, mathias.scheunert@geophysik.tu-freiberg.de,klaus.spitzer@geophysik.tu-freiberg.de

S UMMARY
The electromagnetics working group of the Freiberg Institute of Geophysics and Geoinformatics looks backon a long history of research in the field of simulation and inversion of electromagnetic problems. Together
with the Faculties of Mathematics of the Universities of Chemnitz and Freiberg, a wide variety of individual
software solutions have been developed over the years. A collaborative software project over the last three
to four years has enabled the pooling, unification, and re-implementation of all the acquired knowledge into asingle Matlab finite element software library, which is currently being tested and is on its way to becoming an
open source application for the EM community. In our poster, we present the key features of this library whichallow for the implementation of the forward and inverse problem of any type of time- or frequency dependent
geophysical electromagnetic application in 2D and 3D. For this purpose, case studies for magnetotellurics(MT) and geoelectrics (DC) as well as examples for controlled source electromagnetics (CSEM) and induced
polarization (IP) are shown. We would like to invite all interested parties to discuss with us possible applications
as well as technical or theoretical aspects of electromagnetics and its numerical implementation.
Keywords: Finite elements, unstructured grids, simulation, inversion, Matlab, library

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

3D modeling of CSEM data in the radio frequency band with different sources
S. Schöttle1∗ , M. Smirnova1 , B. Tezkan1 , P. Yogeshwar1 and M. Smirnov2
1
University of Cologne2Luleå University of Technology∗
stefan.schoettle@uni-koeln.de

A BSTRACTRadio-magnetotellurics (RMT) is a passive electromagnetic (EM) technique in geophysics often used for shallow environmental and geotechnical applications. The method uses remote radio antennas broadcasting in arange of around 10-1000 kHz as transmitters. Due to the natural limitations of RMT in its depth of investigationand its dependency on remote radio transmitters, the technique has been extended to use control source inwide frequency range in the past decade. Here, we term it as CS/RMT measurements, as they combine theplane wave RMT range and the source intermediate zone (CSEM). Therefore, the approach can be thought,
especially in terms of 3D inversion, as a combined CSEM and RMT method. The CS/RMT measurement frequency range extends from 1-1000 kHz.
Considering the source intermediate zone has several advantages over the previous approach of considering
only the far-field: (i) easier logistics, as we do not need to place the source far enough to maintain far-field
conditions; (ii) better signal-to-noise ratio; (iii) combined resolution of CSEM and RMT, among others. Such
implementation requires modelling of the EM field considering the source. Here, we study the resolution capabilities of different source types in the radio-frequency band over the 3D sub-surface. For this we develop ourmodelling software − an object-oriented code in Python − MR3DMod.py.
To calculate the background 1D solution for different source types, general 1D field formulations were implemented, following the computational recipes of the well established 3Dinv. Moreover, we combine the fast
Hankel transform (different filters) with direct quadrature via optimised Python routines to derive the field components.
We will show the advantages and disadvantages of the various sources at high frequencies for the selectedsynthetic models and give recommendations for the CS/RMT survey planning.
Keywords: forward modelling, CSEM, RMT, CS/RMT

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th

Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

CRT3DMT: A three-dimensional magnetotelluric inversion package with adaptively
refined unstructured inversion grid and an application to lithospheric conductivity
structure beneath North China
Huang Chen, Zhengyong Ren and Jingtian Tang
School of Geosciences and Info-Physics, Central South University, 410083, Changsha, China,
csuchenhuang@csu.edu.cn

SUMMARY
Currently, three-dimensional magnetotelluric inversion often uses a fixed inversion grid. Too sparse inversion
grid cannot accurately describe the distribution of the complex subsurface conductivities, and may also leadto inversion results failing to converge. While, too inversion grids will increase not only the computational cost,
but also the non-uniqueness of the inversion. Aiming to solve the above problems brought by using fixed
inversion grid, we have developed a three-dimensional magnetotelluric adaptive inversion algorithm andpackage with adaptively refined unstructured inversion grid. Firstly, based on the unstructured finite element
method, the highly accurate forward responses of the three-dimensional geo-electric model with arbitrarycomplex topography and conductivity distributions can be obtained. Then, the nested forward and inversion
tetrahedral grids are utilized to satisfy their different requirements on grid density. The Thikhonov
regularization objective function with smooth constraint is established and minimized by using limited memory
BFGS algorithm with inexact line search procedure, where the gradient of the objective function is solved byusing the adjoint principle. Most importantly, an inversion grid refinement strategy commonly driven by thedata fit gradient and the model parameter gradient is proposed, which is used to guide the automaticrefinement of inversion grid in the optimization process. Successively, several synthetic models are used tovalidate and test the performance of the developed adaptive inversion algorithm and package. The results
show that the developed algorithm and package can significantly improve the accuracy and convergence rateof three-dimensional magnetotelluric inversion with less inversion knowns. Finally, a geologically significant
three-dimensional lithospheric conductivity structure beneath North China is obtained by using the developed
package.

Keywords: Magnetotellurics; Finite element method; Unstructured grids; Adaptive inversion; L-BFGS

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UHOMT: A novel 3D finite element Magnetotelluric forward modeling code with
unstructured meshes.
Ruiz-Aguilar, D., Gallardo-Romero, E.U.

We introduce to the EM community a 3D magnetotelluric forward modeling program which
computes the distribution of the electric field on an unstructured mesh. We employ highorder edge elements and implement the Edge Finite Element method to accurately
simulate magnetotelluric data in the form of impedance tensor. The program is open-source
and optimized for both high-performance computing (HPC) architectures and non-HPC
architectures. At first, the implemented methodology and code work-flow are shown. Then,
we evaluate the accuracy of our algorithm with various synthetic test models and compare
our results with other Finite Element (FE) and Finite Difference (FD) algorithms. The
experiments show that the implemented high order elements are effective in providing
highly accurate solutions for multiple frequencies with few elements independently of themodel’s geometry. Hence, leading to reduced computing times. Finally, we compare results
using FD and FE codes considering real topographic variations.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

3D Inversion of Controlled-Source Electromagnetic Data
using Non-linear Conjugate Gradients
P. Rulff1, T. Kalscheuer2
1Uppsala University, Department of Earth Sciences, Uppsala, Sweden, paula.rulff@geo.uu.se
2Uppsala University, Department of Earth Sciences, Uppsala, Sweden, thomas.kalscheuer@geo.uu.se

SUMMARY
We present first results of our 3D inversion software developed to image the resistivity distribution of thesubsurface with frequency-domain controlled-source electromagnetic data.
Our 3D finite-element forward solver was implemented into an inversion framework using non-linear conjugate
gradients. The software can handle CSEM setups with multiple source locations. Impedance tensor elements
generated by a set of two coincident perpendicularly oriented horizontal electric or horizontal magnetic dipole
sources serve as input data for the inversion. Using tensor measurements instead of scalar or vector
measurements or single field components brings two advantages: First, complex 3D structures can be better
resolved. Second, the source current strength, which is not always recorded alongside the field data, does nothave to be known for the inversion as the amplitude and phase of the source current cancel out in the tensor
formulation.
In our contribution, we investigate the influence of different regularisation schemes and structural weights on
the inversion of synthetic datasets. The regularisation on unstructured meshes is a non-trivial part in the inverse
computation, especially the regions around the sources must be handled with caution. To prevent large model
updates near sources and receivers, we designed a depth weighting function. A similar depth weighting has
been successfully applied to 3D magnetic and gravity inverse problems in order to distribute the model update
more evenly in the domain.
Keywords: Three-dimensional inversion, controlled-source, frequency-domain, regularisation, conjugategradients

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

2D and 3D Forward modeling of electromagnetic fields in the time domain using
Discontinuous Galerkin Method and Spectral Element Method.
Beatriz Valdés-Moreno1 , Marco A. Pérez-Flores2 , and Jonás D. De Basabe3
1
Department of Applied Geophysics, CICESE, valdesb@cicese.edu.mx
2
Department of Applied Geophysics, CICESE, mperez@cicese.mx
3
Department of Seismology, CICESE, jonas@cicese.mx

S UMMARYElectromagnetic methods in the time domain have been widely used in geophysics exploration, and theirapplication in complex media increases the necessity to get better forward and inverse modeling. With theincrease of computational power, we can create more extensive and complex earth models and use moresophisticated numerical algorithms.
The boundary conditions of the electromagnetic problem dictate that the tangential electric field must be continuous, and the normal electric field is discontinuous. Thus, the finite element method based on nodal elements
is unsuitable for this problem. An alternative is to use the Discontinuous Galerkin method, where the discontinuity of the normal electric field is preserved, and the jump in the tangential electric field is penalized forcontinuity.
This ongoing research project explores the spectral element method and the interior-penalty discontinuous
Galerkin method (IP-DGM) to model transient electromagnetic fields in two and three dimensions in anisotropic
media. IP-DGM allows us to have better control over the boundary conditions of the electromagnetic fields;
furthermore, it is a highly parallelizable method and allows adaptation to geometries and complex boundary
conditions. Finally, we tested explicit schemes such as finite differences, the Lax-Wendroff method, and the
Backward Euler implicit method for time stepping.
Keywords: Discontinuous Galerkin Method, Time-domain, Electromagnetic fields.

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Time-dependent adaptive mesh refinement for 3D forward modelling of transient
electromagnetic fields in volcanic environments including topography

1

C. Schneider1 , K. Spitzer1 and M. Hort2
TU Bergakademie Freiberg, Germany, carolin.schneider@geophysik.tu-freiberg.de,klaus.spitzer@geophysik.tu-freiberg.de
2
Universität Hamburg, matthias.hort@uni-hamburg.de

S UMMARY
Numerical forward modelling of transient electromagnetic (TEM) fields in topographically demanding terrain
is a rather challenging task but inevitable to identify reasonable measurement configurations and to correctlyinterpret acquired field data when investigating mountainous regions. Since the propagating electromagnetic
fields change with time and space, an adaptively refined grid would ideally be time-dependent, too. Generating large unstructured tetrahedral grids tailored manually to each specific time-step is, however, very time
consuming. Moreover, the system matrix also changes with time causing additional numerical work in the solution process. We tackle both problems by using a very efficient Krylov-subspace method which projects thesystem matrix onto a low-dimensional space and, therefore, enables us to evaluate the solution for any given
time. Additionally, we propose an adaptive approach including hanging edges within our modelling domain.
With this, an initial mesh is refined according to the requirements of the propagating electromagnetic field. The
development is driven by the application of TEM at volcanic sites. There is an enormous volcanological interestin magmatic pathways and hydrothermal systems within volcanic structures aiming to understand processes
occurring prior to a volcanic eruption. Therefore, we apply our TEM simulation routine to a model of Stromboli
volcano, Italy, created from a digital elevation model (DEM).
Keywords: modelling, transient electromagnetics, Krylov subspace, adaptive mesh refinement

I NTRODUCTION

Numerical modelling using finite elements (FE) is
a common tool in geoelectromagnetics. Its accuracy is strongly affected by the underlying meshas has been vividly demonstrated by the work of
Schwarzbach (2009). Several approaches exist
to generate an appropriate mesh: Usually, an experienced user sets up an a-priori refined meshwith high resolution in the vicinity of the receiverand transmitter locations. On the other hand, aposteriori error estimators are used to quantify thediscretization error associated with a specific mesh,which has been proposed by, e.g., Ren et al (2013).
In frequency-domain modelling the fineness of themesh is also related to the frequency. Consequently,
the mesh has to be a function of time in time-domain
modelling. We therefore suggest to create a seriesof meshes adapted to the individual time step. Thisis usually prohibitively expensive but becomes feasible with the Krylov subspace technique we are using
here. In order to tailor the mesh to specific times in
the TEM forward problem, we use an extension of
the hanging node approach on Nédélec elements.
The refinement routine guided by an error indicator
can be applied to a DEM derived topographic model.
Here, we apply our technique to a model of Stromboli volcano, Italy.

M ATHEMATICAL BACKGROUNDTEM utilizes the turn-off of a DC current in a usually square loop on the Earth’s surface to induce
eddy currents in the underground decaying in intensity and expanding in space. They can be modeledby solving the field equation for the electric field E
derived from Maxwell’s equations. The TEM mea1/4

114Schneider, C. et al., 2020, Time-dependent AMR for 3D TEM forward modellingsurand, a normalized temporal variation of the magnetic flux density at the Earth’s surface, is then extracted using Ampère’s law ∂t B = ∇ × E.

Electric field equation
We state the time domain formulation of transient
electromagnetic induction in terms of the electricfield E as an initial-boundary value problem (e.g.
Afanasjew et al, 2013; Börner et al, 2008, 2015)

are indicated in black, while the vertices constructedwithin the refinement process are denoted by red
numbers. When refining adjacent tetrahedra, newdegrees of freedom are generated. However, if at
least one adjacent tetrahedron is not refined, thiscauses hanging nodes and, consequently, hanging
edges. Therefore, hanging edges only appear in theoutskirts of a refined area, adjoint to the original (i.e.
unrefined) mesh.
4∇ × (µ−1 ∇ × E)+σ∂t E = 0 on Ω × R+ , (1a)E|t=0 = σ−1

j on Ω,

n × E = 0 on ∂Ω × R+ ,

7

(1b)
(1c)

6

1

where µ = µ0 represents the magnetic permeabilityof free space, σ = σ(x) the spatially varying electrical conductivity, and j the source current density
associated with a current shut-off at time t0 .
The spatial discretization on an unstructured tetrahedral grid using curl-conforming Nédélec elements
yields an ODE initial value problem reading∂t u(t) + Au(t) = 0,u(0) = b,t ∈ R+ ,

(2a)
(2b)

where u is the coefficient vector of the FE approximation of E with respect to the Nédélec basis attimes t > 0 and b denotes the vector of initial values.
The matrix A includes both the spatial approximation of the curl-curl operator and the spatially varying conductivity σ(x).
Now, we find a solution to Equation 2 using a matrix
exponential function
u(t) = e−tA b,(3)which can be evaluated by developing a rational
Arnoldi approximation at any given time t > 0(Afanasjew et al, 2008; Börner et al, 2015).

Hanging edge approach
Adaptive mesh refinement using hanging nodes
for octree finite volume discretizations has alreadybeen described by, e.g., Haber et al (2007). However, records are scarce for extending the approachto FE simulations based on Nédélec elements.
In Figure 1, a uniformly refined tetrahedron is depicted, where the original vertices enumerated 1-4

10

3
5

9

8

2

Figure 1: Uniformly refined tetrahedron, black digits enumerate the original vertices of the unrefined tetrahedron, red digits indicate vertices
emerging from the refinement process.

Uniform refinement results in bisecting every single edge. Consequently, a set of geometrical constraints can be derived from the original, large
edges (”parents”) on the newly created, small edges(”children”), where edge ⃗eij denotes the edge linking vertices i and j (cf. Figure 1). The constraints
derived from edge ⃗e12 read⃗e15 = 1/2 ⃗e12and ⃗e25 = −1/2 ⃗e12 .

(4)Furthermore, the new edges emerging on the front
facet of the depicted tetrahedron can be constrainedusing⃗e57 = 1/2 ⃗e24 , ⃗e59 = 1/2 ⃗e14 and ⃗e79 = 1/2 ⃗e12 . (5)In such a manner, each new edge can be constrained, except for an edge created inside the tetrahedron, such as edge ⃗e78 in Figure 1. Since the latter edge is embedded in the surrounding tetrahedra,
it is a complete edge in the mathematical sense.
The stated constraints are mapped to the FE degrees of freedom on each refined tetrahedron andincorporated into the system matrix using the workof Abel and Shephard (1979).
Note, that the constrained degrees of freedom are alinearly interpolated and, consequently, do not yieldadditional information. The advantage of the hanging edge approach is the ability to refine a meshstraightforwardly without the need to treat hanging
nodes at the level of grid generation.

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In order to demonstrate the use of adaptive mesh refinement including hanging edges, we consider two
applications common in 3D modelling: firstly, automatic refinement around the transmitter locationand secondly, time-dependent tracking of the electric field in the subsurface.

σair = 10−9 S/m and σEarth = 10−3 S/m based on
the solution Ex at time t = 10−6 s shown in Figure
2. In the uppermost panel, one can clearly observehanging nodes where the refined mesh (red colour)
borders the original mesh (black colour). As can beseen in the central and lowermost subplot, respectively, the solution on the fine mesh (lowermost figure) exhibits a smoother Ex distribution in contrast
to the rather coarse solution on the original mesh(central figure). Due to the higher resolution in theinitial step, the accuracy of the solution at later times
is also raised.

Figure 2: Unrefined (black) and refined (red) gridat the transmitter location (top) and Ex at time
t1 = 10−6 s in the unrefined (central) and refined mesh (bottom).

Figure 3: Tracking Ex in the underground using
adaptive mesh refinement at times : t1 =10−6 s, t2 = 10−5 s, t3 = 10−4 s (from top tobottom).

Homogeneous Halfspace

Application to a DEM

When modelling in the time domain, the fineness of
the initial value condition severely affects the solution. Therefore, we automatically refine the region
around the transmitter location within a homogeneous halfspace model with electrical conductivities

As a second application for automatic adaptive refinement, we consider a homogeneous model ofStromboli volcano, Italy, derived from a DEM. Dueto the steep topography, the electrical conductivities
are set to σair = 10−6 S/m and σEarth = 10−3 S/m

R ESULTS

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Schneider, C. et al., 2020, Time-dependent AMR for 3D TEM forward modellingin order to maintain numerical stability at the Earthair interface. An experienced user would generatea mesh with appropriate fineness in the vicinity of
the receiver location, the transmitter location and at
the Earth-air interface mapping the topography to acertain level of detailedness. However, additional
refinement in the underground is necessary in thevicinity of conductivity anomalies and for accurately
resolving the temporal behaviour of the electric field.
In Figure 3, the tracking of Ex using the subset Tref
with
Tref = { T ⊂ Ω|Ex > 1/2 max(Ex ) ∨Ex < 1/2 min(Ex ) }is visualized at times t1 = 10−6 s, t2 = 10−5 s andt3 = 10−4 s. Note that, due to the nature of unstructured meshes, the interface of the clipped model
may contain a variety of slices through tetrahedra
which need not necessarily be triangular. The timedependency of the underlying mesh is clearly visible, the fine regions follow the extrema of Ex . Apparently, the region, in which Ex is greater or lessthan half of its maximum/minimum value, respectively, widens with increasing time. This is in accordance with the diffussive nature of electromagnetic
fields.

C ONCLUSIONS
3D simulations of electromagnetic fields are highly
useful to image electromagnetic fields in the underground. However, the accuracy of the results depends on the underlying mesh. In order to enhancethe distribution of degrees of freedom we propose
a strategy of time-dependent adaptive mesh refinement using hanging edges based on the extremaof the electric field. Future investigations will focus
on the quantitative description of the refinement process.

ACKNOWLEDGMENTS
The SRTMGL1 V003 data product was retrieved inJuly 2017 from the online Data Pool, courtesy of theNASA Land Processes Distributed Active Archive
Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls,

South Dakota,
https://lpdaac.usgs.gov/data access/data pool.

R EFERENCES
Abel JF, Shephard MS (1979) An algorithm
for multipoint constraints in finite element
analysis. International Journal for Numerical
Methods in Engineering 14(3):464–467, DOI
10.1002/nme.1620140312
Afanasjew M, Eiermann M, Ernst OG, Güttel S(2008) Implementation of a restarted Krylov
subspace method for the evaluation of matrix
functions . Linear Algebra and its Applications
429:2293–2314
Afanasjew M, Börner RU, Eiermann M, Ernst
OG, Spitzer K (2013) Efficient Three-Dimensional
Time Domain TEM Simulation Using Finite Elements, a Nonlocal Boundary Condition, Multigrid,and Rational Krylov Subspace Methods. 5th International Symposium on Three-Dimensional Electromagnetics, May 7 - 9, 2013, Sapporo, Japanp 4p
Börner RU, Ernst OG, Spitzer K (2008) Fast 3d simulation of transient electromagnetic fields
by model reduction in the frequency domainusing krylov subspace projection. Geophysical Journal International 173(3):766–780, DOI
10.1111/j.1365-246X.2008.03750.x
Börner RU, Ernst O, Güttel S (2015) ThreeDimensional Transient Electromagnetic Modeling
Using Rational Krylov Methods. Geophys J Int
202(3)
Haber E, Heldmann S, Ascher U (2007) Adaptive finite volume method for distributed
non-smooth parameter identification. Inverse
Problems 23(4):1659–1676, DOI 10.1088/02665611/23/4/017
Ren Z, Kalscheuer T, Greenhalgh S, Maurer H(2013) A goal-oriented adaptive finite-element
approach for plane wave 3-d electromagnetic
modelling. Geophysical Journal International
194(2):700–718, DOI 10.1093/gji/ggt154
Schwarzbach C (2009) Stability of finite element solutions to Maxwell’s equations in frequency domain. PhD thesis, TU Bergakademie Freiberg

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

A parallel adaptive finite-element method for 3-D large-scale controlled-source
electromagnetic forward modelling with hierarchical tetrahedral grids
Zhengguang Liu12, Zhengyong Ren1, Hongbo Yao1, Jingtian Tang1, Xushan Lu2, Colin Farquharson2
1 School of Geosciences and Info-Physics, Central South University
2 Department of Earth Sciences, Memorial University of Newfoundland
Email: liuzhengguang@csu.edu.cn

SUMMARY
Three-dimensional forward modeling software that can quickly solve large-scale problems, provide precise
electromagnetic responses for complicated geo-electrical models, and can be easily incorporated into
inversion algorithms are required to effectively and efficiently model and interpret controlled-source
electromagnetic (CSEM) data acquired in areas with the kinds of arbitrary topography and complex geologicalenvironments that are typical of real-life situations. We have developed a parallel, adaptive, finite-element
approach for frequency-domain 3-D CSEM forward modeling. Our algorithm is capable of using hierarchicaltetrahedral grids with an adaptive mesh refinement (AMR) technique, which yields more accurate
electromagnetic responses for large-scale complex models. Our algorithm solves the total electric field vector
equation. The geo-electrical model is discretized by unstructured tetrahedral grids which can deal with complex
underground geological models with arbitrary surface topography. Unlike previous adaptive finite-element
software working on unstructured tetrahedral grids, we implemented a novel mesh refinement technique
named the longest edge bisection method to generate hierarchically refined grids. New cells obtained from therefinement method are all part of the original cells in the coarser grid. Therefore, a one-to-one mapping
relationship between the newly refined cells and the original cells could be efficiently and precisely obtainedsuch that the conductivity model represented by the refined mesh stays the same as that represented by theoriginal mesh. This means there will be no inconsistency in the conductivity model in the inversion whiletransitioning from early-stage coarser meshes to late-stage refined meshes to obtain better recovered models.
In addition, we employ a parallel domain-decomposition technique to accelerate the computational speed of
our forward modeling algorithm. The flexible generalized minimum residual (FGMRES) iterative solver with an
auxiliary-space Maxwell preconditioner is used to solve the final large-scale linear system of equations. We
validate the performance of the proposed scheme using one synthetic model and one realistic model. We
demonstrate that accurate electromagnetic fields can be obtained by comparison with the analytic solutions
and that the code is highly scalable for large-scale problems with millions or even hundreds of millions of
unknowns. For the realistic model with complex geometry, our solutions match well with the results calculatedby the existing 3D CSEM forward modelling code. Both synthetic and realistic examples demonstrate that our
newly developed code is an effective, efficient forward modeling engine for interpreting CSEM field dataacquired in areas of complex geology and topography.
Keywords: Numerical simulation; Large-scale; Goal-oriented adaptive mesh refinement; Longest edge
bisection method; Parallel iterative solver

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3D EM modeling and inversion with a mixed finite-element and finite-difference
approach to handle high topography and bathymetry variations
S. Védrine1, R. Rochlitz2 and F. Bretaudeau1
1BRGM, 3 Av. Claude Guillemin, Orléans 45060, France, s.vedrine@brgm.fr
2Leibniz Institute for Applied Geophysics, Hanover, Germany, raphael.rochlitz@leibniz-liag.de
1BRGM, 3 Av. Claude Guillemin, Orléans 45060, France, f.bretaudeau@brgm.fr

SUMMARY
3D CSEM modeling is usually performed by finite-difference (FD) discretization, but new players have entered
the scientific community with finite-element (FE) modeling codes. FDs are suitable for simple cases, but are
unsuitable for complex geometries, while FEs are very effective in the presence of high topography and/or
bathymetry variations. The open-source 3D FE modeling code custEM has demonstrated its efficiency andreliability and is freely available to the scientific community. The recent work done by its development teamhas made it possible to integrate the inversion part with the Guauss Newton minimization algorithm, but there
is not yet enough user feedback and tools available on this new module to be operational for the general public.
On the other hand, POLYEM3D is a 3D FD modeling and inversion code that has a wide range of inversion
algorithms and reparametrization and regularization tools but faces numerical errors in presence of hightopography or bathymetry variations such as in coastal areas or volcanic environments due to the sea/landinterface and large variations in near-shore bathymetry and topography. We tested a mixed FE and FD
approach that aims to reduce the modeling errors inherent in the FD scheme in such complex problems. A
secondary field formulation is available in POLYEM3D and instead of classically using a semi-analytical 1D
code to compute the primary field, we use 3D fields computed in a simple isotropic homogeneous model withcustEM incorporating topographic and bathymetric variations. The accuracy of the mixed approach wasevaluated by modeling with a simple synthetic model incorporating topographic and bathymetric variations,and comparing the responses of the FE and mixed FE/FD codes. In addition, an inversion on a complex CSEM
case demonstrated the good behavior of the mixed approach in inversion thanks to the panel of
reparametrization and regularization tools offered by POLYEM3D.
Keywords: 3D CSEM modeling, topography, bathymetry, Volcanic islands, Separated field, Finite-element,
Finite-difference

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Multi-scale conductivity model of the contiguous US from inversion of the MT USArray
Federico Munch1, Alexander Grayver2
1Berkeley Seismological Laboratory, University of California, Berkeley, United States,

fmunch@seismo.berkeley.edu
2Institute of Geophysics, ETH Zürich, agrayver@ethz.ch

SUMMARY

The MT component of the USArray consists of a high quality data set of magnetotelluric
measurements that addresses both of these problems. Covering ~70% of the contiguous UnitedStates on a quasi-regular 70 km spaced grid, this unique publicly available data led to thedevelopment of several regional 3-D electrical conductivity models. However, an inversion of theentire data set demands novel multi-scale imaging approaches that can handle and take advantage ofa large range of spatial scales contained in the data. We here present a 3-D electrical conductivity
model of the contiguous United States derived from the inversion of the 1100 highest quality
USArray magnetotelluric stations. The use of state-of-the-art modeling techniques based on highorder finite-element methods allows us to take into account complex coastline and reconstruct
Earth’s conductivity across many scales. The retrieved electrical conductivity variations are
consistent with well-known continental structures such as the active tectonic processes within thewestern United States (e.g., Yellowstone hotspot, Basin and Range extension, and subduction of theJuan de Fuca slab) as well as the presence of deep roots (~250 km) beneath cratons. Furthermore,
we interpret the conductivity variations in terms of upper mantle water content by coupling
electrical conductivity with constrains on mantle thermochemical structure derived from theanalysis of seismic data. Our results suggest the existence of a relatively upper mantle beneath theUnited States, with slight lateral variations. In particular, we find an increase in upper mantle watercontent from west to east, with largest values underneath the Midcontinent Rift System and theAppalachians.
Keywords: global induction, mantle conductivity, inversion, water content

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Joint inversion of magnetotelluric data and receiver functions using Pareto-basedswarm intelligence algorithm
E. Büyük1 and E. Zor2
1Department of Geophysical Engineering, Faculty of Engineering and Natural Science, Gümüşhane

University, Gümüşhane, Turkey,
2TÜBİTAK Marmara Research Center, Kocaeli, Turkey, ekrem.zor@tubitak.gov.tr

SUMMARY
The increasing popularity in joint inversion could be attributed to the successful determination of structuresthat are difficult to detect by restricting the models using priori information in conventional modelling. In recent
years, joint inversion of magneto-telluric data and receiver functions also provides valuable information on
structures extending from the upper crust to the lower crust such as detached and folded metamorphic complex
structures, weakly layered crust, etc. Another well-known disadvantages of the derivative-based conventionalmodeling algorithms used to determine these structures is that the solution has a dependency to an initialmodel and the possibility of trapping in a local minimum.
The seismic velocity and electrical conductivity parameters solved with the inversion of receiver function andmagnetotelluric data are not well correlated physically, and jointly inverting them in the scientific literature is
generally used in two ways: 1) to obtain a solution with the empirical relationships extracted from the laboratory
experiments, the so-called petrophysical approach, 2) the cross-gradient approach, which aims to obtain asolution using structural constraints. Since the relationship between seismic velocities and conductivity islargely unknown in the petrophysical approach, a weak coupling between them increases the resolution but
can also lead to misleading and incorrect results. These physical parameters can have different responses tothe inputs such as porosity, permeability, temperature. As a commonly accepted approach, the cross-gradientmethod looks for the solution having structural similarities between the two models. However, the two different
datasets does not always respond to the structural similarities equally, thus obtaining more convincing solution
may involve the consideration of different structural couplings with a regularization term added to the objectivefunction as a multiplier or a weighted sum.
In this study, Particle Swarm Optimization integrated with the Pareto optimality approach (Pareto-PSO) applied
to the receiver function and magnetotelluric data to jointly invert them. PSO is a metaheuristic global
optimization algorithm inspired by the social behavior of flocks of birds or fish, and is an excellent alternative
to traditional geophysical modeling techniques that suffer from initial model dependence, linearization
problems, and trapping at a local minimum. Pareto-PSO also provides the opportunity to model in a joint
system without reducing the resolution of individual datasets that have different physical sensitivities. Similar
to the cross-gradient approach in modeling receiver functions and magnetotelluric data that provide onedimensional model responses, this study examines the gradient approach in the vertical direction only. By
using the advantage of the Pareto approach, apart from the classical weighted sum approach in the objectivefunction, the structural differences have been added to the objective function space as a third axis alongside
the misfit of the receiver function and magnetotelluric data to search for the solution without the need forweighting and combining in the objective function.
We tested a series of synthetic models containing noise-free and noisy data of the magnetotelluric and receiver
functions by using Pareto-PSO. Since the seismic velocity and electrical conductivity parameters may indicate
different responses to the same physical phenomena, the variation of the parameters was applied in the same
direction but with different percentages. Our modeling approach is successfully solved the structures by using
the Pareto front, which presents the joint and individual solutions for the two datasets, also allows to analyze
the non-uniqueness of the solution from the Pareto-front solutions.

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Development of an efficient 3D inversion algorithm for large-scale MT data
Arun Singh1 and Rahul Dehiya2
Indian Institute of Technology (Indian School of Mines), Dhanbad, aruns@iitism.ac.in
2
Indian Institute of Science Education and Research, Pune, rahul.dehiya@iiserpune.ac.in
1

A BSTRACTWe developed an efficient 3D magnetotelluric inversion algorithm employing a forward modeling scheme basedon the radiation boundary method. The radiation boundary vector is estimated using coarse-mesh modeling for
the user-defined inversion domain. Therefore, the proposed algorithm allows an arbitrarily shaped boundary of
the inversion domain. Consequently, it minimizes the degree of freedom in an inversion process by reducing thenumber of cells for which the conductivity ought to be estimated. The developed algorithm is helpful for large
inversion domains that arise due to the vast survey area or high-resolution imaging. The algorithm is created byaugmenting these features into the AP3DMT algorithm. The versatility and efficiency of the inversion scheme
are illustrated for synthetic and real field data. We considered the Rubik model for the synthetic experiment
where data is simulated on two profiles intersecting at a right angle. The proposed algorithm estimates theresistivity of conductive blocks better than the inversion scheme based on the conventional modeling approach.
At the same time, the computation time in this experiment reduces by more than six times. We also invert the
SAMTEX MT data using the developed algorithm to illustrate the robustness of the proposed algorithm. The
inverted model is in overall agreement with well-known structures (e.g., Kaapvaal and Zimbabwe cratons,
Ghanzi-Chobe Belt).

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3-D Modeling of Airborne and Land-based Controlled-Source Electromagnetic Data:
Comparison on CPU and GPU Platformİsmail Demirci1
1Ankara University, Faculty of Engineering, Geophysical Engineering Department, Geophysical ModellingGroup, Ankara, TURKEY. E-mail: idemirci@eng.ankara.edu.tr
SUMMARY
In last decades, thanks to the developed semi-airborne/airborne geophysical measurement methods and dataacquisition systems, it is aimed to make the data acquisition possible and increase the data acquisition speed
in areas where working condition is difficult. Airborne geophysical studies have generally focused on Magnetic
methods, transient Electromagnetic methods and Very Low Frequency Electromagnetic methods. However,
due to the weakness of the methods in terms of depth information, the use of airborne-based controlled-source
electromagnetic methods has gained importance in recent years. The realization of geophysical studies inforested and mountainous areas depends not only on the development of measurement acquisition systems,
but also on the development of software to be used in the evaluation processes of the measurements taken.
In this study, 3D forward solution algorithm is developed to use for inversion stage of natural and controlled
source electromagnetic data collected on ground and airborne studies. In addition, direct and iterative solution
methods are used in the software developed during the study, and their performance is tested in CPU-GPU
platforms and the results are discussed.
Keywords: Airborne Electromagnetic, Modeling, Three-Dimensional, Graphical Processing Unit
INTRODUCTION
In recent years, studies have done on aerial
measurement methods for many geophysicalmethods(Radiometric,
Magnetic
andElectromagnetic methods, etc.). Thanks to thedeveloped
airborne-based
geophysicalmeasurement methods and measurement taking
systems, it is possible to work in topographic
conditions where it is not possible or difficult to takemeasurements from the land, covered with dense
vegetation and where it is not possible to carry theequipment for measurement taking. However, due
to the weakness of methods such as Radiometricand Magnetic methods in terms of depth information,
the use of electromagnetic methods measured fromthe air vehicle has gained importance. Airborne EM
methods are used in many subjects such asenvironmental problems (Doll et al., 2001),
infrastructure surveys (Pfaffhuber et al. 2010),
groundwater and pollution (Gunnink et al., 2012),
geological mapping (Steuer et al., 2009) andmineral surveys (Wolfgram and Golden, 2001).
In parallel with the developments in themeasurement methods and applications of airborne
EM methods, the development of 3-D modeling andinversion algorithms used in the evaluation of data
has accelerated. 3-D forward solution algorithms
are the most important part of inversion algorithms
used to obtain reliable 3-D underground models.
Therefore, the development of stable and fast 3-D
forward solution algorithms is vital for obtaining
more reliable underground models.

In recent years, there are many studies on 3-D
forward solution in frequency domain EM methods.
Finite Differences (Newman and Alumbaugh, 1995,
2002; Streich, 2009), Finite Elements (Badea et al.,
2001; Mitsuhata and Uchida, 2004; da Silva et al.,2012), Finite Volume (Mackie et al., 1994; Haber
and Ascher, 2001; Constable and Weiss, 2006) and
Integral Equation (Wannamaker, 1991; Avdeev etal., 2002) methods are generally used for forward
solution methods. Finite element method is the mostflexible method in terms of defining model geometry(Avdeev, 2005, Erdoğan et al. 2008, Demirci et al.,2012). Although integral equation methods are very
useful for simple models, there are difficulties incomputation for complex models (Mackie et al.
1993). For this reason, the Finite Difference method
and the closely related Finite Volume method are
preferred because of their ease of calculation andapplication and solution stability. In addition, there
are a limited number of studies on modeling 3-D
airborne-based EM methods (Newman and
Alumbaugh, 1995; Avdeev, 2005; Cox et al., 2010;
Liu and Yin, 2014), and the finite difference method
was generally preferred in the studies. During thisstudy, airborne and land-based CSEM 3-D forward
modeling algorithm was developed by using Finite
Difference method.
METHODIn EM methods, to calculate model responses, wesolve the vector Helmholtz equation for thesecondary electric field ES    Es  i0 *Es  i 0 J

(1)
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where J  ( *   P* )E P define source term, ωdenotesangularfrequency,
thecomplexconductivity σ* = σ + jωε includes conductivity σ
and permittivity ε, μ0 denotes free-space magnetic
permeability and σP* is the conductivity of a layered
background model. If the source term on the righthand side of the equation is used, the methods aredefined as controlled source, otherwise natural
source methods. The primary electric field EP iscomputed for conductivity σP* using quasi-analytic
expressions for 1D media (Streich & Becken 2011).
The Helmholtz equation given above cannot be
solved analytically for complex models. For thisreason, one of the numerical solution methods
should be used in the solution of the equation. Finite
Difference method (Newman and Alumbaugh,
1995; Alumbaugh et al., 1996; Champagne II et al.,
2001; Weiss and Newman, 2002, Streich, 2009) is
one of the most preferred methods due to its easeof application and speed of solution. In this study,
Finite Difference method is preferred in the solutionof the Helmholtz equation.
The Finite Difference expression of Eq.1 is obtainedusing Yee(1966)'s staggered grid approach, scaled
symmetrically, and Dirichlet boundary conditions(usually Dirichlet boundary conditions have beenused in previous studies so that the resulting
equation is symmetrical, see Newman and
Alumbaugh, 1995; Streich, 2009 ) is used, a systemof linear equations is obtained in the form given
below.

KE S  S(2)

where K defines a hermitian and sparse matrix withat most 13 nonzero elements in each row, and S
defines the source term. E field values are obtainedby solving the system of equations, and H fields canbe derived from electric fields by using auxiliary
equations. In order to solve the system of equations,
it is necessary to invert the matrix K (direct methods)
or to solve the system of equations with Krylov
space solvers (iterative methods).
Recently, direct solvers have used for relatively
small modeling meshes. Since the inverse of thematrix is taken with direct solvers, there is no needto solve the equation again for each source andpolarization, and the solution speed increases.
Depending on the developments in computer
technology, the use of direct solution methods has
increased in the last decades and the use ofMultifrontal methods in the CPU environment has
become widespread (Streich, 2009; da Silva et al.,2012; Kordy et al., 2015; Puzyrev et al., 2016;
Mütschard et al., 2017 ; Liu et al., 2018). Although
the RAM usage of direct solvers are reduced with
Multifrontal methods, it is not preferred for large
model meshes. During the study, Multifrontal

methods are used to compare solution speeds forsmall model meshes.
The number of rows or columns of the K matrix inthe equation system to be solved can be expressedin hundreds of thousands or even millions,depending on the number of elements in thedesigned 3-D model mesh. For this reason,
stationary and fast solvers used in the solution of
the system of equations directly affect the speed ofthe method. Krylov space solvers are often
preferred because RAM usage is much lower thandirect solvers. In the 3-D EM method, the main
Krylov space solvers used in the forward solution
are CG (Zhdanov et al., 2000; Haber, 2004;
Zhdanov et al., 2011), BICG (Sasaki et al., 2010;
Farquharson ve Miensopust, 2011; Sasaki, 2012),
BICGSTAB (Xiao et al., 2018; Singh et al., 2017;
Plessix ve Mulder, 2008), QMR (Kelbert et al., 2014;
Tang et al.,2015; Wang ve Tan, 2017) and GMRES(Cox et al., 2010; Grayver, 2015; Grayver and Kolev,
2015). Hursan and Zhdanov (2002) compared themethods and said that BICGSTAB, QMR and
GMRES methods are the most effective solvers intheir study. These three methods are often preferredin the solution of the equation.
These solvers are used in the developed algorithm
and CPU performances are tested in the solution of
frequency domain CSEM 3-D modeling algorithm.
Using the results obtained, it is preferred to use
BICGSTAB in GPU. The results of the algorithm andcomparison of the solvers are discussed and theirresults presented in the following section.
MODEL STUDY
The initial results of the developed algorithm has
been tested for the resistivity model given in Table1 with using 1-D semi-analytical straight solution
algorithm (Streich and Becken, 2011) and thepreviously developed 3D CSEM forward modelingalgorithm (CUSTEM- Rochlitz et al., 2019). The E
and H field amplitudes of the developed algorithm
were compared with the CUSTEM and semianalytical solution algorithm for the 1Hz frequency
on each measurement point, which were obtained
as a result of the resistivity model given in table-1(Figure 1). The source used in the study is locatedat y=-3km and parallel to the x-axis. The dipole
length of the source is used as 1 Km. The result ofdeveloped algorithm coincides with the results
obtained with both the analytical solution and the
CUSTEM algorithm. The performances of thesolvers are tested on the CPU and it is seen that the
BICGSTAB solver was the fastest and most stable
solver (Figure 2). However, it is seen that direct
solvers reach the solution in the most effective
solution time when more than one source is used.
In Figure 2, the results are given according to the

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use of a single source. In iterative solvers, it isnecessary to recalculate for each source, while indirect solvers, there is no significant increase incomputation time when more than one source isused. Therefore, as a result of the study, it isconcluded that using direct solvers would be
efficient in case of using multiple sources for small
model meshes. When the CPU and GPU
performances of the BICGSTAB algorithm, which isthe chosen iterative solver, are examined, it isobserved that the acceleration in the solver is 2,5times higher, especially at low frequencies, and thealgorithm developed on the GPU platform is more
efficient (Figure 3).

solvers in the developed algorithm chosed as hybrid
in the light of the information obtained, which will
increase the solution speed. For this purpose, inorder to optimize the solution speed, the use of
BICGSTAB at high frequencies and the multifrontal
method at low frequencies is recommended incases where the number of sources is more thanone.
ACKNOWLEDGEMENTS

Table 1. 1-D resistivity model used in the test studyz (m)Resistivity (Ωm)
B1
0 to 300100
B2
300 to 700
10000
B3
700 to infinity
1000

Avdeev DB, Kuvshinov AV, Pankratov OV, Newman GA(2002) Three-dimensional induction logging problems,Part I: An integral equation solution and modelcomparisons. Geophysics, 67(2), 413-426.
Badea EA, Everett ME, Newman GA, Biro O (2001)
Finite-element
analysisofcontrolled-source
electromagnetic induction using Coulomb-gauged
potentials. Geophysics, 66(3), 786-799.
Constable S, Weiss CJ (2006) Mapping thin resistors andhydrocarbons with marine EM methods, Part II—
Modeling and analysis in 3D. Geophysics, 71(6),G321-G332.
da Silva NV, Morgan JV, MacGregor L, Warner M (2012)
A finite element multifrontal method for 3D CSEM
modeling in the frequency domain. Geophysics, 77(2),E101-E115.
Demirci I, Erdoğan E, Candansayar ME (2012) Twodimensional inversion of direct current resistivity data
incorporating topography by using finite differencetechniques with triangle cells: Investigation of Kerafault zone in western Crete. Geophysics, 77(1), E67E75.
Doll WE, Nyquist JE, Beard LP, Gamey TJ (2000)
Airborne geophysical surveying for hazardous waste
site characterization on the Oak Ridge Reservation,
Tennessee. Geophysics, 65(5), 1372-1387.
Erdoğan E, Demirci I, Candansayar ME (2008)
Incorporating topography into 2D resistivity modelingusing
finite-element
andfinite-difference
approaches. Geophysics, 73(3), F135-F142.
Gunnink JL, Bosch JHA, Siemon B, Roth B, Auken E(2012) Combining ground-based and airborne EM
through Artificial Neural Networks for modelling glacial
till under saline groundwater conditions. Hydrology
and Earth System Sciences, 16(8), 3061.
Haber E, Ascher UM (2001) Fast finite volume simulationof 3D electromagnetic problems with highly
discontinuous coefficients. SIAM Journal on Scientific
Computing, 22(6), 1943-1961.
Mackie RL, Smith JT, Madden TR (1994) Three‐dimensional electromagnetic modeling using finite
difference
equations:The
magnetotelluric
example. Radio Science, 29(4), 923-935.
Mitsuhata Y, Uchida T (2004) 3D magnetotelluric
modeling
using
the
T-Ω
finite-element
method. Geophysics, 69(1), 108-119.
Newman GA, Alumbaugh DL (1995) Frequency-domain
modelling of airborne electromagnetic responses
using staggered finite differences. Geophysical
Prospecting, 43(8), 1021-1042.

Figure 3. CPU and GPU performances and relative
acceleration graphs of the selected iterative solver(BICGSTAB)
CONCLUSIONS
The findings obtained on the CPU revealed that the
BICGSTAB algorithm is an efficient algorithm for 3D
CSEM forward modeling. Therefore, it was also
coded in the GPU environment. In this way, it has
been observed that the algorithms coded on the
GPU reach a solution 2.5 times faster, especially at
low frequencies. In the case of multiple sources,
multifrontal methods is converging to the solution
faster for high frequencies and for small model
networks. It is thought that the selection of theThis work was supported within the scope ofTÜBİTAK BİDEB with grant no 1059B191800610.
REFERENCES

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Demirci, İ. 2022, Airborne and Land-Based Modeling of 3-D CSEM data

Newman GA, Alumbaugh DL (2002) Three-dimensional
induction logging problems, Part 2: A finite-difference
solution. Geophysics, 67(2), 484-491.
Pfaffhuber AA, Grimstad E, Domaas U, Auken E, Foged
N, Halkjaer M (2010) Airborne EM mapping ofrockslides and tunneling hazards. The Leading
Edge, 29(8), 956-959.
Rochlitz R, Skibbe N, Günther T (2019) custEM:
Customizable finite-element simulation of complex
controlled-source
electromagnetic
data. Geophysics, 84(2), F17-F33.
Steuer A, Siemon B, Auken E (2009) A comparison ofhelicopter-borne electromagnetics in frequency-and
time-domain at the Cuxhaven valley in Northern
Germany. Journal of Applied Geophysics, 67(3), 194205

Streich R (2009) 3D finite-difference frequency-domain
modeling of controlled-source electromagnetic data:
Direct solution andoptimization for
highaccuracy. Geophysics, 74(5), F95-F105.
Streich R, Becken M (2011) Electromagnetic fields
generated by finite‐length wire sources: comparison
with
point
dipolesolutions. Geophysical
Prospecting, 59(2), 361-374.
Wannamaker PE (1991) Advances in three-dimensional
magnetotelluric
modeling
using
integralequations. Geophysics, 56(11), 1716-1728.
Wolfgram P, Golden H (2001) Airborne EM applied tosulphide nickel-examples and analysis. Exploration
Geophysics, 32(3/4), 136-140.

Figure 1. Comparison of our algorithm electric and magnetic field results (Ex, Ey, Hx, Hy, Hz) with CUSTEM
and Semi-analytic solution for 1 Hz

Figure 2. Comparison of iterative solvers in terms of iteration number, computation time, relative error andspeed-up relative to direct solvers
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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

POLYEM3D : A flexible 3D CSEM and MT modeling and inversion code
F. Bretaudeau 1,∗, F. Dubois 1, J. Porté 1,2,3, S. Védrine 1,4
1BRGM (French Geological Survey) f.bretaudeau@brgm.fr
2 Strasbourg University, ITES-CNRS, UMR7063, France
3 Formerly 1 & 2, now at SINTEF Industry, Norway
4 University of Western Britany - EO-OCEAN, UMR6538, France

SUMMARY
We developed the massively parallel 3D finite-volume modeling and inversion software POLYEM3D, dedicatedto the modeling and inversion of various type of EM data. POLYEM3D relies on a separated primary/secondary
fields approach and can therefore handle both MT, and CSEM with very flexible acquisition configurations forsources and receivers: far or near field, marine, land, coastal, dipoles, long wires, loops and interactions withboreholes and metallic pipes. Coupling with other modeling codes such as FE to improve
topography/bathymetry modeling or interactions with complex structures such as casings are possible.
The algorithm includes various solvers (MUMPS, WSMP) and can support both gradient-based (l-BFGS) and
Gauss-Newton optimization to adapt to the data and computation constraints.
Many tools are available to tune the inversion of real world data, such as flexible parameterization with arbitrary
1D/2D/3D splines of several orders, automatic and user-defined inversion preconditioners, data reformulations
and weighting, various L2 and robust norms, joint MT and CSEM, fast check 1D modeling/inversion, as wellas including prior and constrains from borehole or AEM.
POLYEM3D was designed to be operational and efficient with real EM data, but also as an open research tool.
In particular, it made it possible to explore new possibilities for EM such as 3D CSEM complex resistivity
inversion (Porté et al. 2021), CSEM/MT inversion in a coastal context (Védrine et al. 2022, Védrine et al.
EMIW22), joint inversion of several EM methods (Védrine et al. EMIW22), CSEM time-lapse differential
inversion (Bretaudeau et al. 2021), CSEM deep imaging for geothermal energy in urbanized areas andsedimentary environments (Bretaudeau et al. 2022, Darnet et al. 2022), or CO2 storage and monitoring, orother shallow land CSEM configurations.
Keywords: 3D modeling, 3D inversion, MT, Controlled Source EM, direct solver, flexible, land, marine, coastal,joint inversion, IP inversion, topography, bathymetry, geothermal exploration

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Magnetotelluric system NORD
D. Epishkin1*, A. Yakovlev1,2, D. Yakovlev1, N. Zorin1,2,
1Nord-West Ltd, Moscow, Russia
2Moscow State University, Moscow, Russia*corresponding author: dmitri_epishkin@mail.ru

SUMMARY
This work presents the testing results of a new magnetotelluric system NORD. The key features of the system
are: wireless operation with options of real-time data processing and quality assessment, measuring complex
impedance of electric lines, low self-noise, low power consumption, light weight, compactness, reliability,
robust data processing algorithms. The receiver has a built-in calibration system as well as telemetry thatallows to monitor the status of the device and identify common problems. At the moment, the NORD allows to
obtain high-quality MT data in the range from 50 000 seconds up to 20 000 Hz. We carried out a large numberof field tests and comparisons with equipment from leading manufacturers, which showed excellent
performance of the system. Also in recent years, a few large-scale production works have been carried out
using this system.
Keywords: instrumentation, self-noise, testing, data processing

INTRODUCTION
The use of high-quality modern equipment is one of
the important components of the successful
implementation of magnetotelluric (MT) work. A
number of review papers and textbooks discuss
land MT instrumentation (Kaufman and Keller 1981;
Simpson and Bahr 2005; Ferguson et al. 2012).
Other reviews are focused on magnetic (Poliakov etal. 2017) and electric (e.g. Perrier et al. 1997; Lu etal. 1998) field sensors.
At present, a number of requirements are imposedon magnetotelluric systems. The system shouldhave low level of self-noise, wide frequency range,
the receiver input impedance should be sufficiently
high to minimize the distortions of electric signal
associated with poor grounding conditions. The
equipment should be reliable, robust and able tooperate in various climatic conditions; it also should
be as simple as possible to use. We focused onthese requirements when developing the station.
The NORD magnetotelluric system consists of 3
main parts: 1) 5-channel receiver 2) magnetic andelectric field sensors 3) software for communication
with the station and for data processing. This work
describes the distinctive features of the NORD
system and presents the results of testing.

it has the ability to measure the overall complex
impedance of the grounded dipoles, which include
the capacitive effects in the wires. This allows taking
less biased electric field measurements in theregions with poor grounding conditions.
The station has the ability to process data in realtime without stopping the record. It is also possible
to view telemetry, time series, spectra andcoherences in real-time, which allows you to quickly
estimate the quality of the data.
All interaction with the station is carried out via WiFi, which increases the reliability of the device in thefield. Operation of the system is carried out by any
Android / iOS / Windows / Linux – based device witha Wi-Fi module and an Internet browser.
The small dimensions and weight of the receiver
make it possible to adapt it for marine
measurements (Epishkin et al, 2018).
For advanced processing (Epishkin 2016) of raw
time series acquired by NORD system one shoulduse EPI-KIT — a modern and universal software
solution for processing of MT and CSEM data. This
software can process data received by equipmentfrom a number of manufacturers, which allows you
to combine different types of equipment in one
project.

MAIN FEATURES OF THE SYSTEM
Receiver
The NORD system has a number of features that
distinguish it from other magnetotelluric systems.
An innovative feature of the NORD receiver is that

The first component of the NORD system is amodern digital receiving unit (Figure 1), speciallydesigned to register the EM field signals in wide

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frequency range. The receiver has 2 electric and 3
magnetic channels, each one equipped with aseparate 32-bit ADC with a base sampling rate of2400 Hz and 24-bit ADC with a base sampling rateof 312.5 kHz for radio-magnetotellurics (RMT) or
audio-magnetotellurics (AMT). It is possible to savethe time series simultaneously at 5 sampling rates:
15 Hz, 150 Hz, 2400 Hz, 78.125 kHz, 312.5 kHz.
The sampling rate schedule is easily configurable,
and allows to control the final size of raw data files.
High accuracy of the acquired MT or CSEM data isensured by low internal noise level (~ 3.5 nV/√Hz for
AMT frequency range) and modern ADCs of NORD
receiver, combined with using robust dataprocessing algorithm implemented in the EPI-KIT
software. The main characteristics of the receiverare given in Table 1.

Electric field sensors
Electric field measurement in the NORD system isperformed with NW-4 sensors, which is a special
type of compact, environmentally sound andrelatively inexpensive non-polarizable graphite
electrodes with low self-noise level.
Magnetic field sensors
NORD system set includes induction coils of the
IMS series – low-noise magnetic field sensors withsmooth and stable frequency characteristics. There
are three types of sensors: IMS-5 – high-frequency
sensors for AMT (0.1 Hz – 20 kHz), IMS-10 – classic
MT band sensors (0.00002 Hz – 1000 Hz), IMS-15– broadband sensors which allows obtaining asingle AMT+MT (BBMT) curve without changing
different types of sensors (0.00002 Hz – 10 kHz).
The main characteristics of the IMS sensors areshown in Table 2. The amplitude-frequency
characteristics of the sensors are given in Figure 3.
Parameter
Methods

Figure 1. Receiver NORD
Parameter
Number of channels
Frequency range
ADC per channel
Sampling
frequencies
Input resistance
Input capacitanceMax. input voltage
Self-noise level
Data interface
Time synchronization
Integralsolid-state drive
Power consumption
Power supply voltage
WeightDimensions
Temperature range

Value
2 electric + 3 magnetic
DC – 35 kHz
32 bit + 24 bit
312.5 kHz, 78.125 kHz,
2.4 kHz, 150 Hz, 15 Hz
10 MΩ
< 0.5 nF
E channels: ± 600 mV
H channels: ± 2500 mV~ 3.5 nV/√Hz
Wi-Fi 802.11n± 30 ns (RMS)
32 GB Industrial Grade(optionally up to 256
GB)
6 – 8.5 W
12 V
1.8 kg20x10x13 cm-40 … +85 °C

Operation
frequency
range, HzPower
consumption,
W
Dimensions:length, cm
Dimensions:diameter, cm
Weight, kg

IMS-5
AMT,
CSEM

IMS-10MT,
CSEM

IMS-15
AMT+MT,
CSEM

0.1 –
20000

0.00002- 10000.00002 10000

0.96

1.2

1.7

55

105

105

6

7.6

6

3

7

5.5

Table 2. Technical characteristics of the induction
magnetic sensors (IMS series) used in the NORD
systemFigure 3. Magnetic sensors IMS-5, IMS-10, IMS-15
amplitude-frequency characteristics

Table 1. Technical characteristics of the NORD
receiver

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Receiver self-noise
The measured internal noise level of the receiver isshown in Figure 2 in orange.

Synchronization testing
One of the important requirements for MT units is
accurate time synchronization. To check the NORD
receivers’ synchronization accuracy we developed
a special test. The same sinusoidal signal with afrequency 𝑓 of several hundred Hz was applied
simultaneously to several receivers, and for each
moment of time the phase difference in degrees (Δφ)
between the registered signals at the given
frequency was calculated. Next, according toEquation 1, the time shift (Δt) was calculated:∆𝑡 =Figure 2. Self-noise of NORD receiver compared totypical electric line signals. There are different noise
floors for high- and low-frequency receiving circuits.

∆𝜑 1
360 𝑓

(1)

Figure 5 shows an example of a two-hourobservation of timing accuracy between two NORD
receivers. During the entire observation period, thesynchronization accuracy between instruments was
better than 60 ns, which is sufficient for synchronous
processing of time series at frequencies up to 100kHz.

Magnetic sensors self-noise testing
For all types of inductive magnetic sensors, we
carried out numerous parallel tests, on the basis ofwhich the self-noise of each sensor was calculated.
Also, for some sensors self-noise measurements
were carried out in a shielded camera. The noiselevels obtained by both methods agree with eachother. Tests have shown that the sensors have astable and predictable noise level. Figure 4 shows
results of self-noise measurements for different
types of IMS sensors.

Figure 5. Time shifts (Δt) between signals from two
NORD stations synchronized by GPS
Field testing
NORD system is being actively tested for several
years, and dozens of pilot instruments have been
already used in commercial MT and CSEM projects
in Ural Mountains, Taymyr Peninsula, Siberia andKazakhstan. To date, thousands of measurements
have been carried out using the NORD system invarious climatic conditions.
As a demonstration of the system's capabilities,
Figures 6 and 7 show 2 measurement results.
The first result was obtained in Kamchatka (easternpart of Russia). The measurements were carried out
in the MT mode. For 3 days of measurements, it waspossible to obtain high-quality curves in the range
from 0.001 seconds to 30 000 seconds.

Figure 4. Self-noise of IMS sensors compared to atypical magnetic field

The second result was obtained in the central partof the East European Plain. In this example, therecording was carried out in the BBMT mode. With
12 hours of data, high-quality results were obtainedin the range from 0.0001 seconds to 1000 seconds.

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CONCLUSIONSThe conducted tests have shown that the receiverand field sensors work at a high level and meet themost modern requirements for accuracy andreliability. The new solutions applied in the Nord
complex (measurement and accounting of thecomplex resistance of electrical lines, processing
and quality control in real time, control through any
device with a browser) improve the quality of thereceived MT and CSEM data and increase work
productivity.
REFERENCES
Epishkin DV (2016) Improving magnetotelluric dataprocessing methods. Moscow Univ. Geol. Bul.
71(5):347–354.
Epishkin D, Palshin N, Yakovlev A, Yakovlev D(2018) Technology of marine magnetotelluric
sounding in transition zone. In: Paper presented
at the 24th EM Induction Workshop, Helsingor,
Denmark,
13-20
August2018.
https://doi.org/10.13140/rg.2.2.33684.60808
Figure 6. Processing result of 3-day record at
Kamchatka region (East of Russia) with the IMS-10
magnetic coils in the MT frequency range

Ferguson IJ, Chave AD, Jones AG, Mackie R, Rodi
W (2012) Instrumentation and field procedures.
In:
Magnetotelluric
method.
Cambridge
University Press, pp 421–479
Kaufman AA, Keller G (1981) The magnetotelluric
Sounding Method. Elsevier, Amsterdam
Lu K, Macnae J (1998) The international campaign
on intercomparison between electrodes forgeoelectrical measurements. Explor. Geoph. 29:
484-488.
Perrier FE et al. (1997) A 1-year systematic study ofelectrodes for long period measurements of theelectric field in geophysical environments. J.
Geomagn. Geoelectr. 49: 1677-1696
Poliakov SV, Reznikov BI, Shchennikov AV,
Kopytenko EA & Samsonov BV (2017) The
range of induction-coil magnetic field sensors forgeophysical explorations. Seismic Instruments
53: 1-18
Simpson F & Bahr K (2005) Practical
Magnetotellurics. Cambridge University Press,
Cambridge

Figure 7. Processing result of 12-hours record at
the East European Plain (Russia) with the IMS-15
magnetic coils in the BBMT frequency range

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Well integrity monitoring with electric fields by using hierarchical geo-electric models
G.D. Beskardes1 and C.J. Weiss1
1 Geophysics Department, Sandia National Laboratories, Albuquerque NM, USA, gdbeska@sandia.gov

SUMMARY

Failure of wellbore integrity is one of the key concerns in operating oil and gas fields as well as abandoned
mature fields that are typically considered for long-term CO2 storage, and may cause dramatic negativeenvironmental impacts. Here, we present a numerical study on the well integrity monitoring by using electric(E) field measurements. The survey setting includes a steel-cased well whose condition is unknown (i.e., intact
or damaged) and a surface profile along where the E field is measured once the casing is energized at thewell head (i.e., the top-casing method; Wilt, 2016; MacLennan et al., 2018). The changes in the surface E field
can be used to detect and constrain the location of well damage.

Here, we obtain the E field responses of the steel-cased wells from the simulated electrical potentials in theDC limit by utilizing the hierarchical finite element method (Hi-FEM; Weiss, 2017). The method allows us torepresent the electrical conductivity not only on volume elements but also on lower dimensional elements suchas facets (2D) and edges (1D) in the unstructured finite element mesh. Since the well casing is represented
by a subset of connected edges within the 3D tetrahedral finite element mesh, the surface E field data can be
simulated without the need of extensive mesh refinement and high computational cost. Our results support thefindings of the previous studies that well breakage results in an anomalous increase in the amplitude of thesurface E field inversely proportional to the length of the path from the wellhead. Moreover, our analysis ofsurface E field data obtained from an energized, damaged well also shows that regardless of the amount, type,and location of well damage, surface E field measurements can identify the presence of damage and provide
a reasonable estimate for the compromised portion of the well. In this study, we present various model
scenarios to investigate the feasibility of an automated well integrity monitoring with the surface EM data.

Keywords: DC resistivity, electric fields, well integrity monitoring, finite element modeling

ACKNOWLEDGEMENTS
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology
and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for
the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.
Finite-element meshes used for this analysis were generated by Cubit, available at http://cubit.sandia.gov.
Funding support was provided by the Sandia National Laboratories LDRD program.

REFERENCES

Beskardes, G.D., Weiss, C.J., Um, E., Wilt, M. and MacLennan, K., 2021. The effects of well damage andcompletion designs on geoelectrical responses in mature wellbore environments. Geophysics, 86, no. 6, pp.
E355–E366.

MacLennan, K., G. Nieuwenhuis, M. Wilt, E. Um, and J. M. Pendleton, 2018, Evaluating well casing integrity
with non-invasive electromagnetic methods: Presented at the Annual Technical Conference and Exhibition,
SPE.

Weiss, C.J., 2017. Finite-element analysis for model parameters distributed on a hierarchy of geometricsimplices. Geophysics, 82, no. 4, pp. E155–E167.

Wilt, M., 2016, Wellbore integrity mapping using well-casing electrodes and surface based electrical fields:
Final Report for SBIR phase 1 Grant DE-SC0015166.

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Magnetotelluric imaging of the Mitidja Basin structure, North of AlgeriaNaïla. Kerbadj1,2, Abderrezak. Bouzid1 and Ahmed Seddik. Kasdi1,2
Centre de Recherche Astronomie, Astrophysique et géophysique CRAAG, 16340, BP 63 route del'Observatoire Bouzaréah, Algiers, Algeria.
2
Université des sciences et de la technologie Houari Boumediene, BP 32 El Alia Bab-Ezzouar, 16111, Algiers,
Algeria.
1

SUMMARY
The Mitidja basin is located in central northern Algeria, thus it is an active seismic zone. Studies have shown
destructive earthquakes along the southern and northern sides of the area. It is associated with Quaternarycompressive deformation revealed by thrust focal mechanisms (Maouche et al. 2011; Meghraoui 1991). Dueto a lack of deep geophysical data, evidences about the lithospheric structures related to the formation of the
Mitidja basin are limited. Magnetotelluric data from 10 stations on an 11 km N-S profile were acquired andinverted to derive an electrical resistivity model. Phase tensor analysis indicates a 2D resistivity structure withpresence of 3D local features. The 2D inversion model illustrates electrically conductive basin with depth of ~3.5 km. The lack of seismic profiles and deep wells throughout the basin makes it impossible to know its depth,
exact development time, geometry and dip of surrounding faults. Only modeling of recent gravity data
highlights the direction of deep and steep north-dipping NE-SW tectonic contact at the northern basinboundary (Hamai 2011). The subject of this work is to image the underlying geoelectric structures beneath
Mitidja basin.

Keywords: Mitidja Basin, Tipasa, Magnetotellutic, inversion, Algeria.

INTRODUCTION
Geological setting
The north of Algeria is one of the most active
seismic area in northern Africa. The Mitidja basin is
located within the central northern Algeria. Thisbasin is marked by E-W to NE-SW trending foldstructures and related reverse and thrust faults,
accommodating 2 to 3 mm/yr shortening across theTell Atlas (Guemache 2010; Maouche et al. 2011;
Meghraoui 1991).
In order to obtain a geoelectrical model of the
Mitidja basin, a magnetotelluric survey was
performed. Basically it consisted of an 11 km N-S
profil in Sidi-Rached, near Tipasa town in thewestern part of Mitidja basin. MT method uses
natural origin electric and magnetic fields (Electricalthunderstorms and ionosphere-magnetosphereinteractions). It is the most effective method tocharacterize the electrical resistivity of the subsoil(Aymé et al. 1954). A frequency dependent
impedance tensor (Z) is obtained from the time
variations of the horizontal electric and magnetic
fields measured at the surface.
This paper presents a 2D inversion results obtained
from minimizing the misfit of calculated model
responses and observed data. This work is madeto image the underlying geoelectric structures
beneath Mitidja basin which is the focus of interestof the scientists working on seismic hazards andrisk.

The Mitidja basin is situated in the central Tell Atlasin the north of Algeria. It is interpreted as a syncline
bounded by compressive structures. It ischaracterized by E-W to NE-SW trending foldstructures, over 150 km, being more or less parallelto the coastline (Maouche et al. 2011; Meghraoui1991).
The most important active structure of Algiers area
form the southern and northern edges of the Mitidja
Quaternary basin, are the Blida thrust and foldsystem and the Sahel anticline respectively. The
main part of the Mitidja basin is filled by sediments
and volcanic rocks. NW-SE to NNW-SSE
compressions formed the actual frame of the
Mitidja region, between lower Pliocene andQuaternary (Derder et al. 2018; Guemache 2010;
Maouche et al. 2011; Meghraoui 1991).
To the north of the basin, a 70 km long Sahelasymmetrical anticline shows a blind reverse fault(Guemache 2010; Maouche et al. 2011; Meghraoui1991) which is probably at the origin of several
earthquakes in the North of Algeria. The Mitidjabasin is an active structure; many seismologicalstudies have shown the existence of destructive
earthquakes along both the southern and northernsides of the basin (Heddar et al. 2013; Maouche etal. 2018).

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Data acquisition and processing

Dimensionality and strike analysis

The Mitidja broadband MT sites were collected
between 2008 and 2022, where the data was
acquired on an 11 km profile of 10 stations, withsites spacing between 0.6 and 3 km.
Sites MTJ02, MTJ03, MTJ06 and MTJ07 arelocated over the Sahel fold. MTJ09 was locatedabove the limit between the Sahel fold and theMitidja basin. The rest of sites were installed on topof the Quaternary deposits of the Mitidja basin. The
sites are distributed along N-S profile, that is,orthogonal to the geological structures direction. At
each sounding the electrical (Ex , Ey) and magnetic
components (Hx , Hy Hz) are recorded using
MTU-5A of Phonix Geophysics. The AMT
recordings were performed during approximately 1hour, whereas the MT ones were carried out over atime range of 3 to 24 hours.
The observed time series were processed using
SSMT2000 software, yielding impedance tensors Z
and Tipper TZ within the period rang 10-4 – 103s.

The MT data were edited to remove estimations
that were clearly contaminated by noise. A
dimensionality analysis using phase tensor was
carried out. This approach provides also estimatesof regional strike starting from the analysis of themagnetotelluric impedance tensor (Caldwell et al.
2004).
To measure the structural dimensionality (Caldwell
et al. 2004), a skew angle β was calculated fromphase tensor. When |𝛽| < 3 , a 2D structure isinterpreted while for |𝛽| > 3 a 3D structure should
be considered. The phase tensor can be displayedby an ellipse. The shape of the ellipse represents adimensionality of the resistivity responses: Circular
for 1D and elliptical for 2D or 3D.
Figure 3 shows that for most of the periods β values
are below the threshold (|𝛽| < 3). Some large β
values appear at the longest period ( |𝛽| > 3 ).
Accordingly, the subsurface structure may be
regarded as 2D, in general.
In this study, induction vectors were useless due tothe high level noises contaminated the measured
vertical magnetic field.
Figure 4 shows the rose diagram values for all
periods at each station. The results suggest a strikeangle of N80°E which coincides well with theregional geology. The rotated xy and yx curves wereassigned to the TE and TM mode, respectively.
Two dimensional resistivity modeling

Figure 1. Simplified geological map of Mitidja basin
modified from (Maouche et al. 2018). The red line
indicates the position of MT profile.

The MT data was inverted using the finite element
inversion algorithm MARE2DEM (Key 2016) toderive a 2D smoothest resistivity model that fits thedata. In order to find the best inversion strategy,
several tests were carried out.
Both transverse electric (TE) and transverse
magnetic (TM) modes were inverted jointly using
50 Ωm homogenous half space as a starting model.
An error floor of 10% was applied to the apparent
resistivity and 2.87° in the phases. Figure 5 shows
the model obtained finally. This model achieved an
RMS misfit of 2.03. In order to check theconsistency of the result, sensitivity tests werecarried out. The latter have shown that there are no
inversion artifacts.

Figure 2. MT sites distribution plotted in red circle
along a N-S profile (Google earth image)

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Kerbadj, N. et al., 2022, Magnetotelluric imaging of the Mitidja basin structure, North of Algeria.

being resolved properly.
CONCLUSIONS

Figure 3. Skew angle β plot along the profile

Magnetotelluric profile was carried out in the central
part of northern Algeria, into the east of Mitidjabasin in order to reveal its structure at depth.
The phase tensor dimensionality analysis
suggested that the data are generally consistent
with 2D assumptions. The finite elements code
MARE2DEM was used to resolve the 2D inverse
problem. Sensitivity tests were carried out toconfirm that resistivity structures are robust. The
obtained model reveals structures that correlatewith geological information and complementing
them despite the noisy data.
ACKNOWLEDGEMENTS

Figure 4. Rose diagram of strike angles obtainedby the phase tensor method for all sites and all
periods.
Results and discussion
The subsurface of the Mitidja basin appears mainly
as conductive intersected by various conductors’
bodies (Kerbadj et al. 2022).
From N to S, the model (figure 5) shows in theupper part a conductive zone UC1 (0.1 ~ 1Ωm) a
resistive zone UR1 (150 Ωm), a second conductorzone UC2 (10 Ωm), a second very resistive zone
(1000 Ωm) and an alternation of conductors andresistors UC-R (10 – 150 Ωm). A very conductive
zones C1 and C2 (0.3 – 1 Ωm) are identified in
depth that extends to 3 km. This high conductivity
might be related to fluid content within the marine
Pliocene formation.
A number of resistive anomalies shown in theobtained model (figure 5) could be related to active
deformation causing juxtaposition of older
geological formation with the younger basin
sediments.
The model illustrate well the Sahel anticline
Neogene structure, this anticline shows thrust andflexural faulting affecting the vertical bedding of
Quaternary units of the Mitidja basin. Between theupper Miocene and the Quaternary, NW-SE to
NNW-SSE compressions forms the syncline of the
Mitidja basin.
Minimum depth extent of the shown model can be
determined as approximately deeper than 3.5 km
due to the highly intense effect of the conductive
structures which prevent the deep structures fromThe equipment for the field work was supplied by
the research center of Astronomy, Astrophysics andGeophysics (CRAAG) and Houari Boumediene
university (USTHB Algeria). This project has
required intensive field work which would have notbeen possible without the effort from researchersand students from the research center of
Astronomy, Astrophysics and Geophysics (CRAAG,
Algeria) and Houari Boumediene university(USTHB
Algeria)which
wegratefully
acknowledge.
REFERENCES
Aymé A, Aymé J-M, Magne J (1954) Etude desterrains
Néogènes
de
la
cluse
du
Mazafran Sahel d’Alger). Bulletin n°1, Fascicle
11,129-150
Caldwell T.G, Bibby H.M, Brown C (2004) The
magnetotelluric phase tensor. Geophys. J. Int.,
158, 457-469
Derder M.EM, Djellit H, Henry B, Maouche S,
Amenna M, Bestandji R, Ymel H, Gharbi S,
Abtout A, Dorbath C (2018) Strong Neotectonic
block-rotation, related to the Africa-Eurasia
convergence in northern Algeria: Paleomagnetic
evidence from the Mitidja basin. Tectonics
10.1029/2018TC005394
Guemache M-A, Djellit H, Ymmel H, Gharbi S,
Dorbath C (2010) La faille post-Astienne deBouinan-Soumâa (région de Blida, bordure sud
du bassin de la Mitidja, Algérie) : Expression
néotectonique et implication dans l’évaluation del’aléa sismique. Bulletin du service géologiquenational Vol.21, n°1, PP. 75-94,11 fig.

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Kerbadj, N. et al., 2022, Magnetotelluric imaging of the Mitidja basin structure, North of Algeria.

Hamai L, (2011) Etude gravimétrique de la Mitidja
occidentale
Heddar A, Authemayou C, Djellit H, Yelles A-K,
Déverchère J, Gharbi S, Boudiaf A, Van Vliet
Lanoe B (2013) Preliminary results of apaleoseismological analysis along the Sahelfault (Algeria): New evidence for historicalseismic events. Quaternary International, 302,
210-223
Kerbadj N, Abderrezak B, Boukhlouf W, BougchicheS, Deramchi A, Guemache M-A, Djellit H, Abtout
A (2022) Underlying geological structure of thenorthern edge of the Mitidja basin and the Sahelfold near Tipasa town (Algiers, Algeria) using
magnetotellurics: First result. CAJG 2019 pp
489–491

review.
Euro-Mediterranean
Environmental Integration, 3:21

Journalfor

Maouche S, Meghraoui M, Morhange C, Belabbes
S, Bouhadad Y, Hddoum H (2011) Active
coastal thrusting and folding, and uplift rate of
the Sahel Anticline and Zemmouri earthquake
area (Tell Atlas, Algeria). 2nd edn. Publisher,
Location (1999). Tectonophysics, 509, 69-80
Meghraoui M (1991) Blind reverse faulting systemassociated with the Mont Chenoua-Tipaza
earthquake of 29 october 1989 (north-central
Algeria). Terra Nova, 3, 84-93

Kerry K (2016) MARE2DEM: A 2-D inversion codefor controlled-source electromagnetic andmagnetotelluric data. Geophys. J. Int 207,
571–588
Maouche S, Harbi A (2018) The active faults of the
Mitidja basin (North Central Algeria): what does
the seismic history of the region tell us? A

Sahel anticline

Mitidja basin
S

N

Figure 5. 2D Inversion results model obtained with RMS of 2.03

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Sensitivity of phase tensors to absolute resistivities in a 3-D world
L. Dambly1a, F. Samrock1, A. V. Grayver2 and M.O. Saar1
1ETH Zürich, Institute of Geophysics, Geothermal Energy and Geofluids Group, aluise.dambly@erdw.ethz.ch

2ETH Zürich, Institute of Geophysics, Earth and Planetary Magnetism

SUMMARY

Galvanic distortions represent one of the greatest challenges when dealing with magnetotelluric datasets.
Several methods exist that try to mitigate the effect of distortions on inversion. One solution to avoid this major
issue was provided with the introduction of the phase tensor, a transfer function that is free of galvanic distortion.
However, the nature of the phase tensor makes it predominantly sensitive to relative changes of resistivity andits ability to constrain absolute resistivities is still under debate. So far, case studies have shown that phasetensor inversion works effectively if (i) the subsurface resistivity distribution has significant 3-D heterogeneities,(ii) MT site spacing is sufficiently dense, and (iii) data-constrained starting models are employed. There ispresently a lack of systematic investigations to what extent phase tensors constrain absolute resistivity valuesin 3-D subsurface models.
To shed light on this discussion we systematically analyse to what degree variations of absolute resistivities
influence the fit of phase tensor data. We use a dataset with measurements from 127 MT stations in an areaof about 340 km2 covering a volcanic geothermal field in the Main Ethiopian Rift. To ensure the eligibility of thedataset for 3-D phase tensor inversion we introduce a quantitative criterion for “sufficiently” dense station
spacing. Using a homogenous starting model derived from the average apparent resistivity we retrieve a modelwith a good data fit, that acts as a reference model.
In a first test the fit of the reference model is compared to models obtained from inversion using arbitrary (i.e.
not data-constrained) starting models consisting of homogenous halfspace models of 20, 50, 100 and 1000
Wm. This results in poorer data fit and less geologically plausible models, thereby demonstrating theimportance of using starting models that are constrained by data. In a second test we use the reference modeland vary the resistivity values using algorithms from image processing such as contrast adjustment and linear
stretching. Detailed analysis of the modelled responses and residuals reveals significant changes in the misfit
relative to the reference model.
We conclude that in a realistic 3-D settings with sufficiently dense MT site spacing phase tensor inversion canbe considered as a reliable method to recover true subsurface resistivities. If performed properly, phase tensor
inversion is an elegant and straight-forward method to obtain models not affected by galvanic distortion.

Keywords: Magnetotellurics, Phase tensor, 3-D inversion, Galvanic distortion

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Conductivity structure beneath Australia constrained by 3-D inversion of tippers in
spherical geometry
F. Cicchetti1, A. Grayver1, R. Rigaud1, A. Kuvshinov1 and A. Yoshikawa2
1Institut für Geophysik - ETH Zürich, agrayver@erdw.ethz.ch
2 Department of Earth and Planetary Sciences - Kyushu University,

yoshikawa.akimasa.254@m.kyushu-u.ac.jp

SUMMARY
We present a new 3-D conductivity model of the Australian continent for part of the crust and upper mantle.
The model was derived by inverting vertical magnetic field transfer functions (tippers), estimated from a
combined data set at periods between 300s and 10000s. Specifically, we used data from the Australian Wide
Array of Geomagnetic Stations (AWAGS) project and complemented them with geomagnetic observatory datafrom the MAGnetic Data Acquisition System (MAGDAS) and the British Geological Survey (BGS) data sets.
The challenging task of inverting tippers at a continental scale was addressed by performing inversion in a
spherical frame and using a multi-scale approach with adaptively refined meshes to accurately account for theocean effect and decrease of resolution with depth. This was made possible by a new formalism that allows
modelling tippers on a sphere within the inversion code GoFEM. Furthermore, a spherical geometry set-up
eliminates potential distortions caused by cartographic projections and paves the way to a natural integrationof tippers with other longer-period electromagnetic (EM) responses from global sources.
Despite the limitations posed by the data set and survey layout, obtained conductivity model successfully
images conductivity variations that correspond to major geologic features and boundaries, thereby providing
new insights into the electrical structure beneath Australian continents and potentially serving as an initial
guess for more detailed studies.
We consider this work as the first step in a broader study aiming at jointly inverting tippers and longer-period
EM responses to obtain a 3-D conductivity model beneath Australia down to lower mantle depths.
Keywords: EM Induction – Magnetotellurics – Tippers – Electrical Conductivity – Australia

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Constraining the 1-D electrical conductivity of the crust and mantle beneathcontinents by the joint inversion of multi-source electromagnetic transfer functions
R. Rigaud1, A. Kuvshinov1, A. Grayver1, F. Perrier2 and M. Kruglyakov3
1 ETH Zürich, rafael.rigaud@erdw.ethz.ch

kuvshinov@erdw.ethz.ch
agrayver@erdw.ethz.ch
2 Institute de Physique du Globe de Paris, perrier@ipgp.fr
3 University of Otago, mikhail.kruglyakov@otago.ac.nz

SUMMARY
In this study, we present an approach that enables probing the Earth’s conductivity structure from the crust to
the lower mantle beneath geomagnetic observatories equipped with electric field measurements. Thisapproach is based on a joint inversion of multi-source electromagnetic (EM) responses, namely,
magnetotelluric impedances and longer-period global-to-local (G2L) transfer functions (TFs) originating fromsignals due to ionospheric and magnetospheric sources. Using the G2L TFs paradigm instead of theconventionally used C-responses allows us to account for the spatial complexity of the external sources. We
validate the approach by inverting the responses estimated from the magnetic and electric data collected at
the Chambon-le-Foret (CLF) geomagnetic observatory in France. We discuss the incurred challenges whenworking with CLF data, which arise due to the contamination of daily variations of ionospheric origin with thesignals induced by the oceanic tides. We mitigate this problem through a high-resolution 3-D modelling of tidal
signals and their subtraction from the data.
Keywords: EM Induction – Magnetotellurics – Geomagnetic Depth Sounding – Electrical Conductivity –Chambon-le-Foret

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Dual L-shape model: a possible cause of anomalous magnetotelluric phase incentral India
Khasi Raju & Prasanta K Patro
Magnetotelluric division
CSIR-National Geophysical Research Institute, Hyderabad, India
Summary
The anomalous magnetotelluric phase is a distinct characteristic of magnetotelluric (MT) data,
which can be represented as a distortion in the MT data or anisotropy in the subsurface formations.
In the present study, we notice an anomalous phase at several stations along the Narmada rivercourse normal to the Tan Shear Zone (TSZ) in central India. We made efforts to derive a possible
cause of the anomalous phase using synthetic studies and the obtained results are correlated withmeasured impedance tensor data as well as local geological conditions. In central India, multiple
reactivations of the TSZ lead to the formation of a damage zone parallel to it and conjugate Riedel
shear zone (parallel to the Narmada River course) normal to the TSZ. Based on the geographicalposition of the damage zone and conjugate Riedel shear zone in the study area we presented a dual
L-shape model. From the response of the model, we noticed that near-surface 3-D heterogeneity
across the TSZ critically distorted the transverse component of MT data (YX component of
measured data) due to the strong current channeling. When the current flows across the conjugate
Riedel shear zone i.e. along the Narmada river course, charges are accumulated at the boundaries of
surficial 3-D structures. The charges associated with the current channeling leads to the reverse of
the telluric current direction that is reflected in the form of anomalous phase in the transverse
component of impedance tensor in measured data.
Keywords: Anomalous phase, dual L-shape model, CITZ, Tan shear zone, distortion, current
channeling140

Three-Dimensional Inversion of Magnetotelluric Data fromthe Tarawera Dome Complex, New Zealand
Authors: P. Semper, E. A. Bertrand, G. Caldwell, W. Heise, M. Scheunert, K. Spitzer
We have merged our long-term numerical software developments at TU Bergakademie Freiberg and
TU Chemnitz into a finite element toolbox that now provides blueprints for the implementation of
forward operators and inversion routines for arbitrary geophysical EM problems.
Here we present an application of this toolbox to the magnetotelluric method by inverting bothsynthetic and real MT field data sets to study the behaviour of the implemented numerical solutions
and prove the applicability to large field data sets. The MT data were acquired by GNS Science at 68
sites over an area of 25 x 30 km2 at the Tarawera Dome Complex, New Zealand, from 2006 to 2017.
For the 3D inversion, we have used 16 periods between 0.013 s and 341.297 s and placed particularemphasis on an appropriate representation of the rugged topography. In a first attempt, we adapted
the level of detailedness of the topography using an inverse distance weighting scheme, which for thetime being sufficiently resolves the features of the topography that are important for this particularsurvey setup. We explicitly show that parts of the data are strongly affected by the topography at sitesthat are less than 2 km away from steep slopes and that these topographic features have to be resolved
with sufficient accuracy. The free software programs gmsh and MUMPS are used to discretize themodel domain and solve the forward problem.
For the synthetic case, we have used the targeted field dataset as a guide. A simplified model of the
Tarawera Dome Complex has thus been created to generate a synthetic data set. The model is basedon the knowledge of the regional geology and an analysis of the raw data. It consists of four main layers
and two conductive plume-like structures. In addition, the lakes in the study area and the Pacific Ocean
have been included using rough geometric approximations of their true shape. We can clearly see thatthe lakes cause static shifts at nearby sites and that the ocean affects the longest periods at all sites.
For the inversion of the synthetic data, we have restricted ourselves to the impedances which leads to
the resolution of the stratigraphy in terms of layer thicknesses and resistivities. However, the plumelike structures were not resolved, which is probably due to their low significance within the synthetic
data.
The 3D inversion of the impedances of the real data set yields very similar results to the ones obtainedin previous studies by Heise, et. al. (2016) and Bertrand et. al. (in review). Two conductive plume-likestructures, which seem to rise from a larger conductive zone at depth, are well resolved. These havebeen previously interpreted to be either high temperature fluids or partial melt rising from a partial
melt body at depth.
Both studies give us positive feedback regarding the practical inversion of extensive MT field datasetswith our toolbox.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Plane wave correction and 3D inversion of tensor CSRMT data
A. Shlykov1, A. Saraev2, N. Bobrov3 and B. Tezkan4
1 St. Petersburg State University, a.shlykov@spbu.ru
2 St. Petersburg State University, a.saraev@spbu.ru
3 St. Petersburg State University, n.bobrov@spbu.ru
4 Cologne University, tezkan@geo.uni-koeln.de

SUMMARY
The plane wave correction of tensor CSRMT data measured in the transition zone of controlled source for thesubsequent 3D inversion is proposed. Using 1D controlled source inversion of transition zone CSRMT data
the plane wave curves were derived for main components of the impedance tensor. Efficiency of proposedcorrection was confirmed by the comparison with AMT curves. Corresponding CSRMT plane wave responses:
apparent resistivity and phase curves were combined with AMT data and inverted using the ModEM threedimensional code including topography. The obtained model was validated by the a priori geologicalinformation.
Keywords: Plane wave correction,
Audiomagnetotellurics, 3D inversion

Transition

INTRODUCTION
3D inversion of radiomagnetotelluric (RMT) data
andcontrolled
sourceradiomagnetotelluric(CSRMT) data in the far-field zone is based on thesolution of the plane wave forward problem and isusually performed using magnetotelluric codes(Newman et al, 2003, Bastani et al, 2012). Modern
computers and developed inversion codes like
ModEM (Kelbert et al, 2014) permit to fulfill theinversion quite fast. Controlled source 3D inversion
considering data in the transition zone of a sourceis the much more time-consuming task. There arestill no publications about 3D transition zone
inversion of the CSRMT data. Usually CSRMT
measurements are performed both in the far-field
and transition zones. Here we propose a relatively
simple and fast procedure for the transformation of
the mixed transition and far-field zone curves into
fully plane wave equivalent curves without losing
the phase and applicable for tensor CSRMT
datasets (main components of impedance tensor).
Validation of the proposed procedure was
performed using the joint inversion of transformed
CSRMT and audiomagnetotellurics (AMT) data andthe comparison with a priori geological information.

zone,

Controlled-source

radiomagnetotellurics,

along 9 profiles with 50 m step and 20 m separation
between stations along a profile. At the realizationof soundings three main frequencies were used: 0.5,
5 and 50 kHz with 9 odd subharmonics for eachmain frequency to cover the range 0.5-950 kHz with27 points on the sounding curve. Tensor CSRMT
measurements were performed using traditional
consecutive switching on transmitters: one
measurement when the first transmitter is switchedon and second one when the second transmitter isswitched on.

DatasetField measurements were performed on thegeophysics test site near St. Petersburg, Russia.
For CSRMT measurements we used the RMT-F 5channel recorder and two GTS-1 transmitters(Saraev et al, 2017) with perpendicular grounded
wires of 620-630 m length. Measurements by the
CSRMT were fulfilled in the broadside area of
perpendicular wires at offsets from 13 to 640 m

Figure 1. The layout of the sources and measuring
stations in the topography map. 1 – AMT stations, 2– CSRMT stations. Local coordinate system isshown in the map. Red circle shows the selected
station for the analysis of transition zone effect.
Tensor AMT measurements were carried out using
the 4-channel ACF-4M recorder (Saraev et al, 2014)
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Shlykov, A. et al., 2022, Plane wave correction and 3D inversion of tensor CSRMT datain the frequency range 3-400 Hz. AMT stations werelocated on the regular mesh 100x100 m within thestudied area. The layout of the measurement
stations in the topography map is presented in
Figure 1.

model a plane wave response can be computed.
We can assume that the computed 1D plane wave
response is equivalent to the plane wave response
from the real resistivity distribution on themeasurement station.

The range of topography changing is about 10 m inthis area. Inversion was done accounting the
topography. Local X-axis is directed along the
profiles and local Y-axis – is across profiles.

In this context the task of 1D inversion of thetransition zone CSRMT data is the fitting of
measured data as close as possible but not thefinding of a geologically reasonable model. The
layered Marquardt inversion can be a goodcandidate for this procedure. For tensor data suchcorrection has an obvious limitation. It is no way tocorrect diagonal XX and YY components but themain XY and YX components only. But theadvantages of this approach are the independent
fitting of each curve (XY and YX) and the joint
correction of apparent resistivity and phase contrary
to existing transition zone correction procedures like
(Bartel, Jacobson, 1987).

An example of the CSRMT and AMT sounding
curves is presented in Figure 2. A station was
selected near the sources (red circle in Figure 1) toillustrate the impact of the transition zone effect on
CSRMT data. We can see the significant gap
between AMT and CSRMT curves because oftransition zone effect.

Figure 2 contains an example of results of theapplication of proposed procedure. Dotted lines on
the CSRMT curves are the corrected ones. It isclearly visible that both apparent resistivity a andimpedance phase Z AMT curves are naturally
continued by the corrected plane wave CSRMT
curves and we have smooth common curves oversix decades of the frequency. For tensor CSRMT
data in the transition zone when the diagonalcomponents of the impedance tensor are smallenough this procedure can be a reasonable
alternative to controlled source 3D inversion.
Results of 3D plane wave inversion
Figure 2. An example of sounding curves. 1 – XY
AMT curve, 2 – YX AMT curve, 3 – XY CSRMT
curve, 4 – YX CSRMT curve, 5 – synthetic plane
wave curve after 1D inversion of XY CSRMT data,
6 – the same for YX CSRMT data. Position of thestation is marked by a red circle in Figure 1.
Plane wave correction procedure
The correction procedure for transforming of thetransition zone CSRMT data into the fully plane
wave equivalent curves is described below.
Following to the principle of equivalence, we cansay that any 1D model can be considered as anequivalent to the 3D model or the real distribution of
the resistivity in the subsurface media if theelectromagnetic (EM) response from this 1D modelis close enough to the 3D response or measured
data. It is valid for the plane wave and controlled
source response (both, in transition and far-fieldzones). Almost any transition zone CSRMT data
can be fitted with some acceptable tolerance using
1D controlled source inversion. For the obtained 1D

For the 3D inversion we used the ModEM code
(Kelbert et al, 2014). The full AMT impedance tensor
data at 43 stations and the main components of thetransformed CSRMT impedance tensor data at 222
stations were used at the inversion. The finite
difference rectangular model had 65x58x62 cellswith the inner horizontal part 700x540 m with size ofcells 20x20 m. The first (top) 12 m part of the model
was divided into 8 equal layers with 1.5 m thickness
to discretize the topography. Each other layer of themodel had the thickness 1.2 times bigger than aprevious layer down to 68 km. Total size of themodel was 53х53х68 km.
Inversion was performed using the nonlinear
conjugate gradients (NLCG) algorithm (Rodi,
Mackie, 2001). We used 2% error-floor for the main
components of the impedance tensor and 15%
error-floor for the diagonal components of the AMT
data. Start resistivity of the model under the “air”
cells was 40 m. All AMT data and a priory
geological information show that the basement of
the section is presented by the resistive crystalline
rocks. Using the smooth Occam inversion, theAbstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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position of such sharp (by resistivity) border will be
blurred and cannot be mapped confidently. In areas
where the distance between AMT and CSRMT
stations was less than 5 m we applied the 1D
Marquardt joint inversion with 5 layers in the modelusing TM curves. For this inversion the original (notcorrected) CSRMT data were used. As results of the
1D inversion the depths to the roof of the resistive
basement were obtained in the range 220-310 m
with slightly dipping toward the east with near tohomogeneous the overlaying layer with resistivity
around 30-60 m and thickness around 140-180 m.
According to a priory geological information thislayer corresponds to the Riphaean sandstones andsiltstones. Most of inhomogeneous are located in
the top part of the section represented by
Quaternary sediments. We estimated the
topography of the top border of crystalline rocks andinserted this information to the start 3D model withthe resistivity of basement 10000 m.
For the 3D inversion the start RMS was 15.3% andfinal one after 60 iterations – 2 %. An example offitting of the measured curves is presented in
Figure 3. An example of section across the 3Dmodel is shown in Figure 4 with overlapped a priori
borehole information. The top part of section is theresistive Quaternary sands and loams. Its thickness
matches the borehole data quite well. Lower we cansee the more conductive layer with local resistive
areas in the top, which corresponds to the Riphaeansandstones with gravel and pebbles. Lower part ofmodel above the basement is the homogenies layer
with resistivity of 30-40 m which corresponds to
Riphaeansandstones,
siltstones
andmetasandstones. The resistive basement remained
without any significant changes from the start model.
This confirmed that our 1D estimation of thebasement topography was correct. The top borderof the basement according to geological informationfrom one borehole in the vicinity of this area. It is
presumed and shown by the dashed line in Figure
4. Our survey clarified the relief of this border.
General view on the 3D model is presented in
Figure 5.
CONCLUSIONSThe procedure for the plane wave correction of
tensor CSRMT data in transition zone is proposed.
Procedure is based on the 1D controlled source
inversion of measured CSRMT data and thecalculation of plane wave response for the derived
model. The inversion is performed independently for
XY and YX curves. As a result, we obtained theplane wave response, which is an equivalent to the
AMT-RMT response which we can expect to obtainon the measuring station. Comparison with AMT

data show that proposed procedure gets CSRMT
response which naturally continue AMT curves.
Using the 3D plane wave inversion of AMT andcorrected CSRMT data we obtained the model
which is in good agreement with a priori geology
information.
ACKNOWLEDGEMENTS
The presented results were obtained with thesupport of the Russian Science Foundation, projectNo 21-47-04401, the Center GEOMODEL of St.
Petersburg State University and the German
Science Foundation, (project TE 170/21-1).
REFERENCES
Bartel LC, Jacobson RD (1987) Results of acontrolled-source audiofrequency
magnetotelluric survey at the Puhimau thermalarea, Kilauea Volcano, Hawaii. Geophysics
52(5):665.
Bastani M, Hubert J. Kalsheuer T, Pedersed LB,
Godio A, Bernard J (2012) 2D joint inversion of
RMT and ERT data versus individual 3D
inversion of full tensor RMT data: an example
from Trecate site in Italy. Geophysics
77(4):WB233.
Kelbert A, Meqbel N, Egbert GD, Tandon K (2014)
ModEM: a modular system for inversion ofelectromagnetic geophysical data. Computers &Geosciences 66:40.
Newman GA, Recher S, Tezkan B, Neubauer FM(2003) 3D inversion of a scalar radio
magnetotelluric field data set. Geophysics
68(3):791.
Rodi WL, Mackie R (2001) Nonlinear conjugate
gradients algorithm for 2-D magnetotelluric
inversion. Geophysics 66(1):174.
Saraev A, Simakov A, Shlykov A, Tezkan B (2017)
Controlled source radiomagnetotellurics: a toolfor near surface investigation in remote regions.
Journal of Applied Geophysics 146.
Saraev A, Antashchuk K, Pertel M, Eremin I,
Golovenko V, Larionov K (2011) The softwarehardware system of audiomagnetotelluric
sounding ACF-4M. The V Russian Workshop
on the electromagnetic soundings of the Earth(EMS-2011), St. Petersburg.

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Figure 3. Comparison of synthetic and measured data for one station close to the sources.
1-4 – components of impedance tensor (1 - XX, 2 – XY, 3 – YX, 4 – YY), dots – measured data, lines –synthetic data.

Figure 4. A section across the 3D model along the X-axis with overlapped borehole data. 1 – Quaternary sands
and loams, 2 – Riphaean sandstones with gravel and pebbles, 3 – Riphaean sandstones and siltstones, 4 –Riphaean metasandstones, 5 – Precambrian crystalline basement (gneisses). The top border of the gneisses
is not confident with borehole data but clarified by the resistivity section.

Figure 5. General view of the 3D model. Top resistive bodies correspond to the Quaternary sands. and
Bottom resistive layer is the crystalline basement.
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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Convolutional Neural Networks Applied to 2D and 3D DC Resistivity Inversion
S. Weit1 , R.-U. Börner1 , M. Brändel2 , P. Gödickmeier1 , R. Gootjes2 , S. Kost2 , O. Rheinbach2 , M. Scheunert1
and K. Spitzer1
1
Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg
2
Institute of Numerical Mathematics and Optimization, TU Bergakademie Freiberg

S UMMARYA neural network approach has been developed to invert 2D and 3D apparent resistivity data. The network
utilizes convolutions as well as pooling and unpooling operations to transform pseudosections into an underground resistivity model. To train the network, synthetic data were produced using an in-house finite element
routine. The subsurface models to produce the data consist of homogenous halfspaces with 0 to 5 conductive
spherical anomalies per simulated measurement. The anomalies, if present, have a constant total crosssectional area. The network was trained on 15300 simulated measurements in 2D and 7500 in 3D. Results
show a fairly accurate match of anomaly resistivity and location between ground truth and prediction for larger
anomalies, while smaller anomalies often blend together in the prediction. The Background resistivity is often
overestimated by the network. Due to the way training was performed, the applicability of the network is currently limited to a small number of scenarios. Despite its limitations, the speed of the prediction and the lackof required a-priori information are advantageous. Possible applications of neural networks in DC resistivity
inversion lie in the generation of suitable starting models for other, more traditional inversion techniques.
Keywords: Machine Learning, Convolutional Neural Network, DC, Inversion

I NTRODUCTION
Geophysical Inversion is often very time consuming
and requires previous knowledge on the geologicalenvironment of the survey area. Both of these factors could be avoided or alleviated by utilizing neuralnetworks for geophysical inversion. Distinguishing
the effects of anomalies from the effects produced
by a geologic background is very similar to an image segmentation approach. This approach is very
established and has been shown to work in a variety of fields. Image segmentation is traditionallyaccomplished using convolutional neural networks.
Here, we present an approach of utilizing convolutional neural networks for the task of regression
applied to DC data. Network architecture, forward
modelling and preliminary results will be discussed.

A RCHITECTURE
Architecture for segmentation often follows an encoder/decoder scheme (see Figure 1). During theencoding, convolutional layers and pooling layers
are used to filter out the most important information.
This information is then distributed and transformed
during the decoder step with the use of unpoolinglayers and more convolutions. The traditional SegNet architecture (Badrinarayanan et al, 2017) uses
a varying number of pooling and unpooling layers,
with a final activation function (usually softmax) for
pixel classification. Here, this architecture is slightly
modified to perform regression by replacing the final
activation function with a fully connected layer (seeFigure 1). This way, not the categories of each cell
(anomaly/background) but the value of each cell (resistivity) are predicted. This 2D network also mirrors
the approach to an architecture used by Vu and Jardani (2021). To reduce the amount of overtraining
and therefore make the network generalize more,
dropout layers were introduced into every convolutional block. These set random entries of the input
tensor to 0. In the 3D case, the unpooling layer isreplaced by a combination of upsampling the output from the previous step as well as concatenating
it with the output of the respective layer during theencoder.

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4

1

1

1/

1/

2

2

1/

1/

4

spherical anomalies in a homogenous backgroundof varying resistivity. The amount of data in each set
are shown in Table 1. The 0-5 spherical anomalies
were randomly placed, with their centres below thecentral profile.

Conv+Dropout+
BN+ReLU

Max
PoolingMax
Unpooling

Convolution+
Flatten+Dense

Figure 1: Architecture used for training 2D inversion

This approach effectively turns the network into a
U-Net (Ronneberger et al, 2015) for the 3D case.
This decision was made due to the lack of an established unpooling approach in 3D that utilizes the
positional information of the pooling step. All training and evaluation were performed using the Keras
API for Tensorflow (Abadi et al, 2016).

DATA S IMULATION AND P REPARATION
To attain the amount of data needed for training aneural network, synthetic DC data were generated.
The synthetic forward modeling was performed using Matlab with an in-house finite element routine(Scheunert et al, 2022). We simulated DC measurements along 17 Profiles with 17 Electrodes eachin a dipole-dipole configuration. Each simulated
3D measurement (17 dipole-dipole profiles with 17
electrodes each) will be called a "sample" in thefollowing. The distance between profiles and electrodes was 3 m. This setup was deliberately chosen
to compare results with Vu and Jardani (2021).
DatasetDataset 1 (base)Dataset 2 (no anomalies)
Dataset 3 (varying ρ for BG)

ntrain
7500
800
7000

neval
1563
200
1000

Table 1: Amount of training and evaluation data foreach dataset.

For training, 3 sets of data were used: A basic one
with spherical anomalies in an otherwise homogenous background of constant resistivity, one datasetwith fully homogenous background and one with

Figure 2: Mesh example for 3 spheres. Mesh is
only shown on boundary surfaces.

The sum of the cross sectional area of all spheres
was constant among all samples (excluding the homogenous halfspace set). Background resistivity
was set to 4, 000 Ωm for the base data set and varied from 4, 000 − 40, 000 Ωm for the third data set.
Anomaly resistivity varied from 10 Ωm to 2, 000 Ωm.
An example with 3 spheres is shown in Figure 2.
The 17 pseudo sections were mapped onto a
32x32x16 grid with grid cells of the size 1.5m in every spatial direction (see Figure 3 for one profile).
Background resistivity was assumed for every cell
not corresponding to a value from the pseudo section (again note Figure 3).

Figure 3: Mapping of a pseudo section to a 32x16
grid.

Inputs ρa and ρ were then finally transformed before
being supplied to the network:

ρa,in = 10(log10 (ρa ) − 3.5)(1)

ρin = log10 (ρ)(2)

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Noisy data were created by the addition of normally
distributed random numbers (µ = 0 and σ = 75)
onto every grid point value belonging to the pseudosection.

R ESULTS
Figure 4 shows the 2D network prediction for datafrom all 3 datasets by a network that was trained using samples from all 3 datasets. The samples themselves have not been part of the training but wereseparated before. Structures close to the surface
can be resolved fairly accurately for both cases withanomalies. Deeper structures often blend together
or are not resolved at all.

Figure 5: Results for image regression in 3D withthe base dataset.

This could be attributed to the fact that a smaller
percentage of underground cells belongs to ananomaly compared to the 2D case. Larger Scale
anomalies (such as the right and center cases in
Figure 5), are resolved well with regards to their position and resistivity.

Figure 4: Results for image regression in 2D with all
3 datasets. Top represents the input grid, middle the ground truth, bottom the prediction.

This can be expected due to deeper structures being masked by shallower structures in the data.
The background resistivity is generally wrong acrossevaluations and often shows small-scale variations
not present in the ground truth. This is particularly visible in the evaluation of the pseudosection
for the homogenous halfspace. There, a wide rangeof resistivities is predicted, with a great amount oflocal, small-scale variation, despite none of it being present in the ground truth. This effect can result from a variety of factors, such as small network
complexity, dataset structure, noise level, amount of
data, etc.
Figure 5 shows network prediction in 3D for the firstdataset. A lot of the same effects as in the 2D casecan be observed (missing resolvability of deeper
anomalies, strong local variation in background resistivity) for this case as well. The general resolvability for smaller structures is worse than in the 2D
case.

Figure 6: Loss for the training of 2D (above) and 3D
networks (below).

Figure 6 shows the loss during training and validation. The curves showcase a small plateau during
the start of training, followed by a large drop in loss
during a few iterations . After around 10 iterations,improvement of the loss is small for the rest of the
training process in 3D. No overtraining (divergenceof training and validation loss) can be noticed in either case.
A limitation of the approach is illustrated in Figure
7. There, data from a subsurface model with a moreresistive anomaly leads to a prediction that does notmatch the ground truth.

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present for this type of network and therefore, alternative approaches should be considered to attainstronger generalization.

R EFERENCES
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean
J, Devin M, Ghemawat S, Irving G, Isard M, et al(2016) Tensorflow: A system for large-scale machine learning. In: 12th {USENIX} symposiumon operating systems design and implementation({OSDI} 16), pp 265–283

Figure 7: An example of an untrained scenario being evaluated poorly.

Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence
39(12):2481–2495

As there are no samples covering this case present
in the dataset, the network is not able to evaluate
cases like these correctly. This illustrates the highdependency on the training data for this type of network.

Ronneberger O, Fischer P, Brox T (2015) U-net:
Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241

C ONCLUSIONSThe presented approach shows promising results
for resolving large to medium sized sphericalanomalies. Huge limitations lie in the evaluation of
scenarios that have not been used for training, suchas positive resistivity anomalies. Some of these limitations could potentially be overcome by training
with more data and data covering more scenarios.
A strong dependency on training data will always be

Scheunert M, Blechta J, Börner RU, Ernst O,
Spitzer K (2022) A matlab fe library for thesimulation and inversion of em problems. In:
29. Schmucker-Weidelt-Kolloquium für Elektromagnetische Tiefenforschung, Deutsche Geophysikalische Gesellschaft e. V., pp 132–132
Vu M, Jardani A (2021) Convolutional neural networks with segnet architecture applied to threedimensional tomography of subsurface electricalresistivity: Cnn-3d-ert. Geophysical Journal International 225(2):1319–1331

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Multivariate statistical analysis of geophysical data and models by neural network
approaches
R. Vadoodi1, T.M. Rasmussen1, M. Abdolmaleki2
1Luleå University of Technology, Sweden, roshanak.vadoodi@ltu.se
2University of Toronto, Canada

SUMMARY
Combination of geophysical data including magnetotelluric and potential field data provide useful
information for mapping and interpreting geological units. Application of neural network approaches has provento have potential to analyze and classify complex multivariate and high-dimensional data. Results fromapplication of quantitative classification based on the unsupervised Self Organizing Map (SOM) neural network(Kohonen, 1982) to regional-scale potential field data combined with newly acquired broadband
magnetotelluric data (Vadoodi et al., 2021) were presented by Vadoodi and Rasmussen (2022). The data
covered a 200 km by 250 km area located in the Paleoproterozoic Norrbotten ore province (northern Sweden).
The quantitative analysis integrated the petrophysical parameters associated with three data types (obtained
from 3D individual inversions of gravimetric and magnetic fields and magnetotelluric data). The input
parameters to the SOM algorithm were defined such that the information on depth variation of eachpetrophysical property enters the classification jointly with actual values. The input data contained 3D resistivity,
magnetic susceptibility, and density model parameter values for six selected depth levels covering the entire
crust. The SOM analysis provided an easy way to map and characterize three petrophysical models jointly tofind the locations in the crust that are similar with respect to all three petrophysical parameters as well as
similar depth-wise distribution. The domain classification is discussed with respect to the geological boundaries
and composition of the crust. In general, consistency was observed regarding the visual comparison between
the SOM domains and the mapped geological units. However, some discrepancies were noted in specificareas where more work is needed to analyze the reasons for the discrepancies.
Deep learning algorithms have been profoundly used in the earth science during the last few years for avariety of scientific problems such as image processing and classification. It uses multiple layers toprogressively extract higher-level features from the input data. In the current study, we present results fromthe application of an unsupervised deep learning algorithm (stacked auto-encoder) for the above-mentioned
data including resistivity, magnetic susceptibility and density values obtained from 3D inversion models for thesame depth sections presented by Vadoodi and Rasmussen (2022). The domain classification is further
discussed and compared with the results from the SOM analysis.
Keywords: Magnetotelluric, Potential field, 3D inversion, Neural network, Deep learning

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Application of the skew parameters in 1-D and 2-D inversion of MT data
D. Yakovlev1, K. Koryagin1, D. Epishkin1, N. Zorin1,2 and A. Yakovlev2
1STC Nord-West, stcnordwest@gmail.com, Moscow, Russia
2Lomonosov Moscow State University, Moscow Russia

SUMMARY
1-D and 2-D magnetotelluric (MT) inversion is often used in practice. However, some MT data used insuch inversions may show 3-D behavior in a certain frequency range, and the question is how these effects
should be accounted for. The idea we present here is to increase the confidence intervals for the data within
the frequency range, where they correspond to a higher-dimensional media, proportionally to the values of theobserved skew (inhomogeneity) parameters.
For example, the following criteria may be used to prepare MT curves for 1-D inversion. For amplitude
data the values 𝑁𝑁, 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 and 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵 are used. These parameters are dimensionless and range from 0 to 1,
indicating deviation of the medium from 1-D (𝑁𝑁) and 2-D (𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 , 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵 ) models. Then the modified
confidence intervals 𝛥𝛥𝑙𝑙𝑙𝑙|𝑍𝑍| for the amplitude curves may be calculated as: 𝛥𝛥𝑙𝑙𝑙𝑙|𝑍𝑍| = 𝛥𝛥𝑙𝑙𝑙𝑙|𝑍𝑍|𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 + 𝑁𝑁 +𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵 . For phase data, the phase tensor parameters 𝛥𝛥𝛥𝛥 and 𝛽𝛽 are used. These parameters aremeasured in degrees and also indicate deviation of the medium from 1-D (𝛥𝛥𝛥𝛥) and 2-D (𝛽𝛽) models.
Accordingly, the modified confidence intervals for the phase curves 𝛥𝛥𝑎𝑎𝑎𝑎𝑎𝑎(𝑍𝑍) may be defined as: 𝛥𝛥𝑎𝑎𝑎𝑎𝑎𝑎(𝑍𝑍) =𝛥𝛥𝑎𝑎𝑎𝑎𝑎𝑎(𝑍𝑍)𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 + 𝛥𝛥𝛥𝛥 + 𝛽𝛽. The MT curves prepared in this way during inversion will make it possible to fit
data more accurate in those frequency ranges where the dimension of the medium corresponds to thedimension of the inversion used.
This technology is implemented in the Inversio program, which is designed for analysis and 1-D/2-D
inversion of large amount of MT data. At the analysis stage the interpreter, looking at the frequency sectionsof the skew parameters, decides which of the parameters to use to penalize the curves. The simplicity of thetechnology makes it very convenient in large-scale MT projects with a variable geological structure of theenvironment.
Keywords: Skew parameters, 1-D inversion, 2-D inversion

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Impedance of capacitive electrodes and wires on the ground surface
1,2*

N. Zorin

1

1

2, D. Epishkin , D. Yakovlev and A. Bobachev

1

STC Nord-West, Moscow, Russia
Moscow State University, Moscow, Russia*
corresponding author; nikita.zorin.geophys@gmail.com
2

SUMMARY
In this paper we derive corrected for the edge effects formula for the capacitance of a thin disk over a
conducting plane, and offer a number of generalized expressions for assessing the transfer impedance of acapacitive electrode over the ground with finite conductivity. It is shown that the transfer impedance of an
insulated wire on the ground surface in a wide frequency range is described by the Cole-Cole formula with an
exponent parameter slightly less than 1.
Keywords: contact impedance, disc capacitance.

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3D imaging of electrical conductivity structures in the Eastern Cheb Basin acrossthe Hartoušov and Bublák mofettes
B. Aleid1&2, U. Weckmann1&2, A. Platz1, J. Pek3, S. Kováčiková3 & R. Klanica3
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, aleid @gfz-potsdam.de2
Institute of Earth and Environmental Science, University of Potsdam, Germany
3Institute of Geophysics of the Czech Academy of Sciences, Prague, Czech Republic

1

SUMMARY

The mofette fields of Bublák and Hartoušov are among the most prominent CO2 degassing centers in Europe,
which are locacted in the Cheb Basin, a shallow Neogene intracontinental basin in the western part of Czech
Republic. The massive degassing of CO2 in the Cheb Basin, especially in the mofette fields, originates fromgreat depths. These mantle/lower-crustal derived fluids might use fault zones for their ascent. In addition,
Quaternary volcanoes were discovered in the area; their magmatic ascent paths and the interaction of theother deep magmatic processes as well as the observations at the earth's surface such as mofettes are notyet fully understood. With our research, we would like to contribute to a comprehensive and holistic
interpretation of the tectonic regime, and image the ascent paths of fluids. In this context, the magnetotelluric(MT) method was applied which is sensitive to electrically conductive phases such as brines and fluids, partial
melts or metallic compounds. We expect that the pathways for fluids from the mantle towards the mofettes
have a much higher electrical conductivity than the surrounding crystalline rocks. As part of an ICDP drilling
programme, two overlapping experiments along a regional profiles were conducted and one local grid of MT
stations were measured in the Cheb basin close to mofette fields. The local dense grid of 97 MT stations, thescope of this work, has an average sites spacing of 500 m. We measured five component broad-band MT data
in a frequency range of 0.0001–1000 s with an average recording time of three days. Unfortunately, there aremany man-made electromagnetic noise sources such as power plants, DC electrified railways, and the heavily
populated study area with its infrastructure. These disturbances had to be removed from the recorded data
within the course of data processing prior to any modelling and inversion. It was finally possible to improve thedata quality to 1s by advanced processing approaches. The presented high resolution 3D image of theelectrical conductivity of the area surrounding the mofettes matches with available drill logs from the Czech
Geological Survey and the previous geophysical studies. A shallow high resistivity layer is related to
Quaternary deposits NE of the mofette fields. The high conductive layer around and beneath the mofettes
might be related to the ascent paths of fluids. A prominent contrast between the sedimentary and the phylliticgranitic basement can be observed. Large-scale regional models hint at deep reaching pathways fostering theascend of mantle derived fluids into the regions of mofettes and swarm earthquakes. However, The mostprominent large-scale conductivity features of the other two regional models are several channels from thelower crust to the surface, possibly representing pathways for fluids into the earthquake swarm region, mofette
fields, and known spas. However, such a conductive channel is absent in our local model beneath the surface
expression of the mofettes. To assess whether the experimental layout, the reduced data quality or inversion
issues are responsible for the lack of such an ascending pathway, or if fluid migration in this area is rather
horizontal than vertical, we applied synthetic inversion test. Results from synthetic modelling studies andavailable geoscientific constraints hint that such a channel might exist directly beneath the mofette field. Still,it seems to be challenging to resolve due to the given data quality, station distribution, and the subsurface
conductivity structure within a conductive sediment basin.

Keywords: Magnetotellurics; West Bohemia; Earthquake swarm; Fluids; 3D inversion
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Time-lapse resistivity imaging : CSEM-data 3D double-difference inversion andapplication to the Reykjanes geothermal field

1

Frédéric Dubois1 , François Bretaudeau1
BRGM, 3 Av. Claude Guillemin, Orléans 45060, France

S UMMARYTime-lapse resistivity tomography bring valuable information on the physical changes occurring inside a geological reservoir. In this study, resistivity monitoring from CSEM data is investigated through synthetic and realdata. We present three different schemes currently used to perform time-lapse inversions and compare thesethree methods: parallel, sequential and double difference. We demonstrate on synthetic tests that double difference scheme is the best way to perform time-lapse inversion when the survey parameters are fixed between
the different time-lapse acquisitions. We show that double difference inversion allows to remove the imprintof correlated noise distortions, static shifts, and most of the non-linearity of the inversion process including
numerical noise and acquisition footprint. It also appears that this approach is robust against the baseline
resistivity model quality, and even a rough starting resistivity model built from borehole logs or basic geological knowledge can be sufficient to map the time-lapse changes at their right positions. We perform thesecomparisons with real land time-lapse CSEM data acquired one year apart over the Reykjanes geothermal
field.
Keywords: Controlled-Source Electromagnetics, time-lapse, inversion, geothermal monitoring, resistivity

I NTRODUCTION

source electromagnetics (CSEM). Therfore monitoring electrical resisitivity may be very usefull.

Geological reservoirs are exploited for various economic reasons: oil and gas production, power andheat generation from geothermal energy, C02 storage or water supply management. Monitoring oftime-lapse resistivity changes in the reservoir properties using geophysical techniques can provide
valuable information about the reservoir evolution,and is becoming more and more common, facilitated by permanent or semi-permanent acquisition
systems. It consists in carrying out at different timea geophysical acquisition over a same region, andproduce a structural image showing the temporalchanges between the two acquisitions.

For monitoring process, the first acquisition or reference dataset is generally called baseline and thefollowing acquisitions are considered as monitors.
In time lapse electric imaging studies, inversion of
the baseline and monitor data sets are generally
performed separately, or in cascade using baselinemodel as a starting guess for the monitor inversion.
Then the difference between the two inverted models is presented as the time-lapse variation in resistivity of the reservoir. We call those schemes
parallel and sequential time-lapse inversion. The
high non-linearity and non-uniqueness of the resistivity imaging problems make the inversion results
still strongly dependent of the path taken by the inversion. This effect may be exacerbated in presenceof high level of noise, which can result in very different inverted models, even without significant modelchanges.

More specifically, electrical resistivity provides valuable information in many applications trough its sensitivity to permeability, fluids, clay content or temperature, and can be obtained using a large range ofdiffusive geophysical methods such as DC electricalresistivity tomography, time-domain electromagnetics, magnetotellurics or frequency domain controlled

To overcome problems of parallel inversions, different time-lapse inversion schemes have been pro-

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posed, mostly based on the used of constrains between the time-lapse models to force the stability
and keep consistency between the time lapse models. For common-acquisition setup between surveys (same geometry, same acquisition parameters), an alternative inversion technique is available
to obtain more stable results by reducing the effectof noise and non-linearity of the inversion process.
This scheme called double difference or differential
inversion is based on the combination of baseline
and monitor data. It is commonly applied in medical imaging, is used in seismic travel-time inversion and have been applied successfully to seismic
Full Waveform Inversion by Asnaashari et al (2014).
But its evaluation on time-lapse resistivity problems
have not been reported yet.

M ETHODSCSEM inversions are intrinsically non-linear, and iterative optimization methods have to be employed.
For each iteration k, we try to minimized the data
residual vector which is typically defined as the difference between observed data and data calculated
in the current model mk :
δdk = dobs − dcal (mk )We define two different data sets corresponding toa baseline acquisition (dobs1 ) and a monitor acquisition (dobs2 ). First, a baseline reconstruction needs tobe done by minimizing the difference between dobs1
and dcal generated in a m0 starting model. However,at the end of this first inversion, discrepancies remain between observed and predicted data comingfrom our inability to properly build the true resistivity
model.
We can split the different contribution of each data
set as follows=

In case of parallel scheme, baseline and monitor inversions are completely decoupled. We computethe time-lapse image by taking the difference between the final inverted models. Baseline and monitor data set do not need to have the same geometry (number of data, number of receivers/sources).
This seemingly advantage is a potential pitfall for inversion tuning. However, we may compare models
with potentially different inverting mesh, regularization parameters and thus very different local resolution. This will induce signals in the time-lapse
model mainly related to the resolution difference between baseline and monitor inversion. Besides, theinverse problem is highly non-linear which implies
complex noise propagation between data and model
estimates. In double difference inversion, extra careshould be taken on the perfectly match between
baseline and monitor data set geometry. First, thebaseline is reconstructed exactly as in parallel inversion. However, in the second step instead of inverting the monitor data we invert the data difference(Asnaashari et al, 2014). We thus define data difference as ∆d = (dobs2 − dobs1 ). Then, we rewrite thedata residual vector at an iteration k as:(1)

with the computed data dcal related to the resistivity
model mk by the forward operator G.

dobs1

dpert the time-lapse signal related to geological variations.

dm0 + δdm0 + dstatic + dnoise1 (2)dobs2 = dm0 +δdm0 +dstatic +dnoise2 +dpert .
(3)with dm0 the data predicted by the forward modelling, δdm0 un-modelled effects in the forward modelling, dnoise and dstatic the uncorrelated and correlated noise between the two acquisitions , and finallyδd2 dble=(dobs2 − dobs1 + dcal (m1 )) − dcal (m1 )= dpert + (dnoise2 − dnoise1 ).

(4)

A very interesting feature of this approach is illustrated by Equation (4). The data residual is not anymore dependent on the error of baseline reconstruction δdm1 , making the inversion much more robust
against the starting model, numerical modeling inaccuracies and non-linearity of the inversion.

R ESULTS
The three different inversion approaches are applied
to a synthetic case with a resistive and conductive
anomaly (Figure 1). This shows how different canbe these three different inversion schemes and how
weak are the artifacts in the double difference strategy. Then we applied the three different inversion
strategies to the Reykjanes geothermal field locatedat the south-west of Iceland at the landward extension of the Reykjanes Ridge (Darnet et al, 2020).
Two surveys are acquired one year apart, a first one
in September 2016, while drilling of RN-15/IDDP2 well. The other is performed in August 2017,

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after the thermal stimulation of the RN-15/IDDP-2
well. Darnet et al (2020) assess the influence ofinternal and external noise on survey repeatability
between the two acquisitions. Over the whole frequency band, repeatability is within 2-3% and 23◦ for the amplitudes and phases respectively.
From the first data set, we build the baseline model
noted m1 by using a steepest-descent gradient optimization algorithm available in POLYEM3D. Oncean acceptable baseline is found, we proceed to theinversion of the monitor data. Baseline reconstruction depicts the resistivity variations from the data
set acquired in September 2016. Once an acceptable baseline is found, we proceed to the inversionof the monitor data by carrying out the three different time-lape inversion approaches. For parallel andsequential inversions: unfitted baseline structures,
static shift structures and noise (correlated and not)are inverted in the same time as temporal resistivity changes. The parallel inversion is unable to discriminate between these different contributions, unlike the differential inversion which is focusing exclusively on time-lapse changes which limits drasticallythe number of artifacts.

ble to image and localize resistivity variations even
without a proper baseline reconstruction, or with abaseline model built with independent information.
For instance, a large scale baseline model could
be built with well logs, geological knowledge or adense EM geophysical survey, and the monitoring
performed with a reduced subset of the EM survey
kept permanent between time steps.

ACKNOWLEDGEMENTS
The research and developments of the POLYEM3D
code leading to these results has received fundings
from the French ANR project EXCITING. Acquisition and processing of the Reykjanes dataset has
been funded by the European research program
H2020 in the framework of the project DeepEGS
N◦ 690771. Large scale 3D EM modeling and inversion was performed with BRGM’s POLYEM3D codeon the Occigen supercomputer thanks to GENCI(Grand Equipement de Calcul Intensif) and CINES(Centre Informatique National de l ′ enseignement
Supérieur) super computing facilities.

C ONCLUSIONSIn this study we investigate several time-lapse inversion strategies to infer the temporal changes ofresistivity. We compare the commonly used parallel inversion framework with the sequential anddouble difference schemes. Unlike the parallel difference, the double difference inversion focuses on
the time-lapse signal only. We show this approach
is much more robust to noise since static noise andmodelling errors are completely removed. Double
difference is also less dependant on the starting
model. Quantitative estimation of the time-lapse
resistivity variations however is still dependant on
the quality of the baseline model. It is also possi-

R EFERENCES
Asnaashari A, Brossier R, Garambois S, Audebert
F, Thore P, Virieux J (2014) Time-lapse seismic
imaging using regularized full-waveform inversion
with a prior model: which strategy? Geophysicalprospecting 63(1):78–98
Darnet M, Wawrzyniak P, Coppo N, Nielsson
S, Schill E, Fridleifsson G (2020) Monitoring geothermal reservoir developments with thecontrolled-source electro-magnetic methodâa calibration study on the reykjanes geothermal field.
Journal of Volcanology and Geothermal Research
391:106437

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Figure 1: Slices of 3-D time-lapse resistivity models: (a) targets, (b) parallel, (c) sequential, (d) double difference, using a baseline reconstructed from the smooth 1D model, (e) double difference model using theexact baseline as a starting model and (f) double difference model using an homogeneous backgroundas a starting model.

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Global Optimization Inversion of Horizontal Electric Dipole Time-Domain
Electromagnetic Data Using Particle Swarm Optimization
C. A. Hapsoro1 , W. Srigutomo2 and E. Agustine3
Astronomy and Geophysics Research Group, Physics Department, Faculty of Mathematics and Natural
Sciences, Universitas Negeri Malang, Jl. Semarang 5 Malang 65145, Indonesia,
cahyo.ajihapsoro.fmipa@um.ac.id2
Physics of Earth and Complex System, Physics Department, Faculty of Mathematics and Natural Sciences,
Institut Teknologi Bandung, Jl. Ganesa 10 Bandung 40132, Indonesia, wahyu@itb.ac.id
3
Faculty of Mathematics and Natural Sciences, Padjadjaran University, Sumedang 45363, Indonesia,
leo@geophys.unpad.ac.id contact
1

S UMMARY
We implement the global optimization of horizontal electric dipole-time domain electromagnetic (HED-TDEM)
data through the application of particle swarm optimization (PSO) algorithm. This probabilistic approach isalternative to the widely used deterministic-local optimization approaches. In PSO algorithm, each particle that
constitutes the swarm epitomizes a probable geophysical model comprised by subsurface resistivity values
at several layers and thickness of each layer. The forward formulation of the TDEM problem for calculating
the vertical component of the induced magnetic field is expressed first in Laplace domain. Transformationof the magnetic field from Laplace domain into time domain is performed by applying the Gaver-Stehfest
numerical method. The implementation of PSO inversion to the TDEM problem is straightforward. It only
requires adjustment of few inversion parameters such as inertia, acceleration coefficients and numbers of
iteration and particles. The PSO inversion scheme was tested to synthetic noise-free data and noisy data,
as well as to field data recorded in a volcanic-geothermal area. The results suggest that the PSO inversion
scheme can efficaciously solved the HED-TDEM 1D stratified earth problem.
Keywords: time-domain electromagnetic, horizontal electric dipole, particle swarm optimization, resistivity

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Singular Value Decomposition of the Phase Tensor
M.Karaş1 and S.B.Tank1
1Boğaziçi University, Kandilli Observatory and E.R.I., İstanbul, Türkiye, karasm@itu.edu.tr
1Boğaziçi University, Kandilli Observatory and E.R.I., İstanbul, Türkiye, bulent.tank@boun.edu.tr

SUMMARY

To understand the detailed characteristics of magnetotelluric data (MT), the current study proposes a
decomposition based on the singular values of the phase tensor. This mathematical methodology is inspiredby the conventional ellipse illustration of the distortion free phase tensor however, it uses the full singularvalue decomposition (SVD) in a traditional manner. The outcomes of the SVD are utilized to illustrate thedimensionality information of the MT data by comparing the configurations of two associated ellipses. As
another illustration method, Mohr circle diagrams are utilized in this study. To control the orientation of thesingular value-based ellipses and to check the dimensionality of the data, Mohr circles were plotted forcomparative purposes. Furthermore, extension to earlier works is provided by implementing the error-based
weighted-least-square (WLS) analyses of multi-site and multi-frequency combinations. Our extension may be
used to clarify the discrepancy between the two-dimensional and the three-dimensional modeling approachnecessities. Synthetic tests were performed to observe validity of the developed methodology for both singlesite-single-frequency and multi-site-multi-frequency handling of the data.
Keywords: Singular value decomposition, Phase tensor, Dimensionality

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Modelling tippers in a spherical geometry1

M. Kruglyakov1 and A. Kuvshinov2
University of Otago, New Zealand, mikhail.kruglyakov@otago.ac.nz
2
ETH Zurich, Switzerland, kuvshinov@erdw.ethz.ch

S UMMARY
Several continental-scale magnetotelluric (MT) surveys have been initiated in the past decades. This motivates exploiting spherical geometry to model MT responses. However, while the MT impedances in sphericalcoordinates can be modelled by using different polarizations of a uniform external magnetic field as the source,
for tippers, one needs another type of excitation. This is because the uniform external magnetic field of any
polarization contains a non-zero radial component.
To overcome this issue, we elaborate a new source model, which leads to valid MT tippers in spherical geometry. This, in particular, allows researchers to exploit an immense amount of magnetic field data around theglobe and probe the three-dimensional distribution of electrical conductivity at lithosphere and upper mantle
depths on a global or semi-global scale.
The proposed source model has been validated by a novel, accurate, computationally efficient solver called
GEMMIE. This solver is based on the nested domain integral equation approach and allows us to calculate
high-resolution EM fields and responses, including tippers, globally and regionally, taking into account realistic
oceans, sediments and mantle structures.
Keywords: Magnetotellurics; Electromagnetic theory; Geomagnetic induction; Numerical modelling

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Anomalous Phase in Elongated Prism Body: A Synthetic 3D MT Forward
Modelling
Dharmendra Kumar1, Arun Singh2, M. Israil1
1Indian Institute of Technology, Roorkee, 247667, India,
2Indian Institute of Technology (ISM), Dhanbad, Jharkhand 826004.

Summary:
The magnetotelluric (MT) method is one of the successful geophysical techniques to investigate subsurface
structures from shallow to great depths. In this method, two horizontal components of the electric field andthree components of magnetic fields are measured and are transformed into transfer functions such as
impedance tensor. Normally, the impedance phases for the off-diagonal components of the impedance
tensor lie in the first (0°<Φ<90°) and third quadrant (-90°<Φ<-180°) for the XY and YX components,
respectively. Sometimes, some phases may lie out of these quadrants. Such phases are termed Anomalous
phases (AP). The AP acts as a barrier to the proper interpretation of the MT data. Simple 1D or 2D structures
do not cause AP. Previously, complex 3D models are used to demonstrate and discuss the AP. In this study,
we present a simple 3D model that can also generate AP. A conductive prism orientated N60°W isembedded in half space at a shallow depth. The impedance tensor was computed at a regular grid. Fromthe simulated data, we observed AP near the two edges of the prism. The electric field vector rotates morethan 90° at these places for a period greater than 10 s. The simple structure is able to produce the AP due
to the current channeling toward the conductor and reversal of the electric field. The AP depends on thelength/width ratio and orientation of the prism and the conductivity contrast between the prism and thebackground. Further, a discontinuity was introduced at the center of the prism. Using the computed forward
responses, AP was observed around the discontinuity region.
Keywords: Magnetotelluric, Anomalous phase.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Electrical resistivity tomography image enhancement using neural network
1

K. Phueakim1, C. Vachiratienchai2 and P. Amatyakul3
Department of Physics, Faculty of Science, Mahidol University, kittipong.phueakim@gmail.com
2
Curl-E Geophysics Co., Ltd., chaichai.vac@gmail.com
3
Department of Physics, Faculty of Science, Mahidol University, puwis.ama@mahidol.ac.th

SUMMARY
The development of neural networks is gradually improving in recent years. It shows excellent
performance in data pattern learning for several applications. Several types of neural networks are successful
in image processing. This work applies the neural network to enhance subsurface resistivity tomography
images given by a geophysical prospecting method called the direct-current resistivity (DCR) method.
The resistivity tomography images or resistivity models from the inversion process of the direct-current
resistivity (DCR) method are mostly vague, requiring professional analysis to deduce the appropriated modelsof the subsurface. A framework of image enhancement using a neural network, ResEN, is constructed toreduce professional biases and increase the resolution in the interpretation process of DCR.
The data used in the neural network are synthetic resistivity models and their corresponding inverted
models. Resistivity models from the inversion process are computed by reliable software. Several validation
cases are tested to show the enhancement ability of the developed framework using a neural networkarchitecture. The results show that the framework can improve over 90 percent of inverted models in validating
data. The framework is also tested with our developed straightforward data-driven inversion and commercial
software with edge enhancement feature. Results show that the developed algorithm gives a reasonableaccuracy in recovering true resistivity models compared to those approaches. The developed algorithm shows
the ability to resolve the ambiguity problem of the resistivity distribution from the DCR method for various
structures depending on the training set of the data.
Keywords: Neural network, Direct-current resistivity survey, Image processing

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Hydrothermal system beneath Mt.Erciyes inferred from Three-dimensional
Magnetotellurics, Central Anatolia, Türkiye
R.Yazıcı1, M.Karaş1 S. Üner2 and S.B. Tank1
1Boğaziçi University, Kandilli Observatory and E.R.I., İstanbul, Türkiye, ruken.yazici@boun.edu.tr
1Boğaziçi University, Kandilli Observatory and E.R.I., İstanbul, Türkiye, karasm@itu.edu.tr
2İstanbul Technical University, Faculty of Mines, Dept. of Geophysics,
1Boğaziçi University, Kandilli Observatory and E.R.I., İstanbul, Türkiye, bulent.tank@boun.edu.tr

SUMMARY
Thirty-five wide-band magnetotellurics (MT) data were collected in three survey campaigns in 2013, 2014 and2018 to decipher the electrical structure beneath the Erciyes Volcanic Complex and the surrounding Erciyes
pull-apart basin in Central Anatolia, Türkiye, within the context of an NSF funded project, Continental Dynamics/ Central Anatolian Tectonics (CD/CAT). Bounding the Erciyes volcanic complex in the west and in the east,
the Erciyes basin is made up three major faults, the Yeşilhisar, Gesi and Develi faults. While the Yeşilhisar andDEveli faults define the west and east of the basin, the Gesi fault runs in the middle of the pull-apart basin
making up the main branch of the Central Anatolian Fault (CAF) Zone, and crosses Mt. Erciyes. The Erciyes
volcanic complex is proven to form in two stages: (i) Koçdağ (>2.8 Ma) and (i) New Erciyes (<2.8 Ma) stages,
with various magmatism types. For determining the dimensionality of the MT data, phase tensor analyses
were performed and three-dimensional models were developed using ModEM including topography to image
the electrical conductivity variations beneath the volcanic complex, major faults and the basin. As a result
significant and widespread conductive layer was observed beneath the volcanic complex that extends toaround 5 km. The conductive layer, which appear to have deeper roots in the west when compared to east of
the basin, was clearly marked by conductive-resistive interfaces near the three major faults.
Keywords: Mt.Erciyes, Hydrothermal system, Fluids

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Static shift correction in sedimentary basins
D. Yakovlev1 and A. Yakovlev2
1STC Nord-West, stcnordwest@gmail.com, Moscow, Russia
2Lomonosov Moscow State University, Moscow Russia

SUMMARY
The work considers the problem of static shift of magnetotelluric (MT) curves obtained in sedimentarybasins. The MT data of recent years obtained in Argentina, Bolivia, Brazil, Iran, Kazakhstan and Russia wereused. The problem of static shift is considered in detail on the data obtained in the Yenisei-Khatanga regional
Megatrough (Northern Russia). This trough is composed of Jurassic-Cretaceous terrigenous rocks withalmost horizontal bedding. To study the effect of near-surface inhomogeneities on MT curves, such conditions
are ideal, since they make it possible to simplify the accounting for the effects of the background section,
considering it to be one-dimensional. In the Yenisei-Khatanga regional Megatrough, about 25 thousand MT
soundings were performed, accompanied by 2-D seismic and time-domain (TDEM) measurements. Such alarge amount of data made it possible to test various methods for detecting static shift and correcting it.
As a reference result, a static shift correction was used based on geoelectric models obtained from the
TDEM data. All other correction methods were compared with this reference result. The work shows that forregional studies, a statistical static shift correction based on the spatial filtering of the MT impedance at a
certain frequency gives satisfactory results. For oil and gas and more detailed survey, it is necessary to use
third-party data to correct the static shift. In more cases the best fit are the TDEM data. In their absence,
seismic survey results can be used.
The work also shows examples when TDEM data cannot be used to correct static shift: due to complex
topography (Bolivia), due to strongly inhomogeneous upper part of the section (Siberia), due to intensive
high-frequency induced polarization effects in permafrost (Taimyr). Based on the TDEM data, examples ofsystematic underestimation of the level of amplitude MT curves (Bolivia, Taimyr) are shown.
Keywords: sedimentary basins, static shift correction, TDEM

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Near surface resistivity structure estimated from time domain electromagnetic data
recorded along a profile in HFT Zone in Mohand area, Uttarakhand, India
M. Israil1, Rehan Raza Ansari1, M. Zubair1, P. Yogeshwar2, B. Tezkan2
1 Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India, mohammad.israil@es.iitr.ac.in
2Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany,pyogeshw@uni-koeln.de, tezkan@geo.uni-koeln.de

SUMMARY
Time-domain electromagnetic (TDEM) data were recorded using Fast TEM device deployed in a coincidence
loop geometry at 24 stations along a profile in the Himalayan Frontal Thrust (HFT) zone in Mohand area,
Uttarakhand, India. The profile is approximately 1.6 km long and extends from the Indo-Gangetic plain in thesouth to the north of HFT in the Khajnawar Rao, a seasonal river bed. The processed data at each station was
inverted using Occam and Marquardt inversion techniques to obtain 1D resistivity depth models at each station.
A stacked geoelectrical section is generated along the profile from the inverted models. North and southdipping beds are reflected on either side of the Mohand anticline thrust, passing nearly through the middle ofthe profile. Resistivity variations are interpreted in terms of the tectonics geometry of the fault zone, geologicaland geo-hydrological scenario of the area. A near-surface resistive layer (> 200 Ω-m) consisting of dry fluvial
deposits, gravels, pebbles, and boulders of varying sizes can be seen throughout the profile. A very low
resistivity (~ 10 Ω-m) is observed in the fault core zone. This zone is exposed along the river terrace zone on
the west bank of Khajnawar Rao. The derived resistivity variations explain the geological and geo-hydrologicalscenario of the area and are consistent with the information available in the literature.

Keywords: Near-surface resistivity structure, Himalayan Frontal Thrust (HFT), Time Domain
Electromagnetic (TDEM), Sub-Himalaya

INTRODUCTION
Loop source time-domain electromagnetic is acontrolled source electromagnetic (EM) method inwhich the transient decay of the magnetic field ismeasured after switching off the current injected
through a transmitter (Tx) loop. The switching-off ofthe Tx current induces eddy currents in the finitely
conducting subsurface. This induced current
diffuses downward and outward in the subsurface.
The diffusion process generates a time decaying
secondary magnetic field in the absence of primary
field. The measured decay characteristics of thesecondary magnetic field are a function of theresistivity distribution of the subsurface formation.
The decay rate of the transient is slower in a
conductive formation, whereas it is faster in a
resistive formation. The theoretical concepts of
TDEM are given in various literature (Kaufman, A.
A. et al., 1983, Nabighian et al., 1991). The recordedtransient response is inverted in terms of resistivity
distribution of subsurface formation (Danielsen,
2003). The TDEM method is fast and accurate for
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mapping the depth and thickness of resistivity
structure. It is less sensitive to the lateral variationof resistivity than the DC resistivity method, thus
used as an efficient and accurate alternative to theconventional DC resistivity methods. In the present
study, we have used the TDEM method to delineate
resistivity structure along a profile passing throughthe HFT zone in the Sub-Himalayan region in andaround Mohand, Uttarakhand, India. The resistivity
structure is further interpreted in terms of geological,
tectonics and geo-hydrological formation of thearea. Additional Radiomagnetotelluric data wererecorded along the complete profile andperpendicular shorter transects to retrieve shallow
high resolution data. However, here we only focus
on the TDEM data.
The main aim of our study is to improve the nearsurface geological information for an improved
understanding of the tectonic process and its
surface manifestations.

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Israil M et al., 2022, Near surface resistivity structure of HFT zone in Sub-Himalayan region

DATA RECORDING AND INVERSION
We recorded TDEM data using TEM-FAST 48
portable device. Twenty-four stations spread along
a 1.6 km long profile in the HFT zone in the Mohand
area were recorded. A coincident loop setup
geometry with a single square of 50m x 50m wireloop was used in the field measurement. The
transmitting current was 3.8A, and the recording
time range was set from 4 to 2024 μs. The southernend of the profile is located in the Piedmont zone,
which is the northern limit of the Indo-Gangetic plainin the study area. As defined in the geologicalliterature (e.g. Srivastava et al. 2016), theHimalayan Frontal Thrust (HFT) passes nearly
perpendicular to the profile line at the 10th stationfrom the south with visible outcrops at the surface.
The remaining 14 stations are located in the HFT
hanging wall zone on the north of the HFT. The
location of stations are shown in Figure 1.

Figure 1 Location map of the study area showing
TDEM stations.

(Chi) are shown in Figure 2. Convergence isachieved in all station data; however, some stations
show larger misfit errors. The used field data error
is set a minimum percentage error of 3%.
Comparison of smooth and layered inverted models
at selected station along with the data and the fitted
responses are shown in Figure 3. All three models

Figure 3 Top panel shows data and fitted response
and bottom panel shows comparison of 1D layered
and smooth inversion of TDEM response recorded
at station 11.
are in general consistent within small variations.
Subsequently, 1D inverted models were stitched
together and interpolated between the stations togenerate a smooth 2D image along the profile, as
shown in Figure 4.

Figure 2 Initial and final error (chi) for all stations
data using Marquardt inversion.
1D inversion of transient response at each station isdone using the EMUPLUS code (cf. Haroon, A.,
Yogeshwar P., et al., 2015; Yogeshwar et al., 2020)
for smooth model (Occam’s) and minimum layered
model (Marquardt) inversion. Initial and final errors

Figure 4 2D resistivity-depth section obtained bystacking and smoothing of 1D models along the
profile.

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Israil M et al., 2022, Near surface resistivity structure of HFT zone in Sub-Himalayan region

The profile distance from station one in km, and
TDEM station numbers are shown on top andbottom of the Figure 4, respectively.
RESULTS AND DISCUSSION
The study area is located in the Sub-Himalayan
region, where the Upper and Middle Siwaliks Groupof rock are exposed in the immediate vicinity of the
Indo-Gangetic plain (IGP). TDEM stations are
passing through the HFT, which is the northern limitof the IGP. Upper Siwaliks conglomerate in the area
has been classified as a sandy matrix showing bothcoarsening and fining upward in stratigraphic
sequence (Rohtash et al., 1991). The conglomerate
is mainly composed of cobble to pebble clasts ofquartzite, limestone, sandstone, and shale.
Exposure of Middle and Upper Siwaliks subgroups
can be seen in the area. The middle Siwalik
subgroup comprises sandstone dominated intervals
which pass upward transitionally into the Upper
Siwaliks boulder conglomerate sequence. The
general stratigraphy of the hanging wall zone can be
described from top to downward: Top thin soil coverare fluvial sediments, 1-3 m thick loamy sand soil,
and 2-3 m thick pebble-cobble ( Wesnousky et al.,1999 ). A trench in the study area shows that the
HFT reaches the surface and is covered by a thin (~10 m) fluvial material on top of the bedrock (Senthil
et al., 2006). The boundary between Siwalik
bedrock and alluvial deposits of the floodplain isabrupt and steep.
A 2D resistivity-depth section was obtained bystitching and smoothing 1D models along the profile,as shown in Figure 4. The derived resistivity-depth
image shows a resistivity discontinuity at the HFT
location. High resistivities are observed in the southof the HFT, whereas relatively low resistivity and low
angle dipping resistivity features are observed in thenorth of the HFT. Based on the low resistivity trend
in the north of the HFT, low angle north dipping beds
are shown by a black line. This low resistivity zone
is consistent with the fault core zone exposed on thewest bank of the Khajnawar Rao (Srivastava et al.,2016). TDEM stations 12, 13 and 14 correspond to
the low resistivity fault core zone, a highly pulverised
fault gouge with a resistivity value (~10 Ω-m). On
the north of the HFT, a gouge dominated damage
zone is observed; these zones are dipping andrepresented by a resistivity between 20-50 Ω-m.
The low resistivity indicates highly fractured andwater-saturated thrusting beds. Similar alternative
low resistivity zones are continued in the north along
the profile.
In the southern zone of the HFT, the near-surface
top layer is fluvial deposits represented by resistivity
in the range of 200-400 Ω-m. The thickness of thislayer is minimum (~ 3m) near HFT, which increases

in the south and reaches up to 20 m. These areolder alluvial flood plain deposits ( Wesnousky et al.
1999). Beneath this resistive (> 300 Ω-m) thickMiddle Siwaliks sandstone layer is present. Further,
low resistivity (~ 50 Ω-m) thick, saturated aquiferzone is present beneath this layer.
CONCLUSIONSTDEM data at 24 stations along a profile
passing through the HFT zone in Mohand,
Uttarakhand area were recorded and inverted interms of resistivity distribution of the near-surface
formation. The dry top surface layer is resistive (>200 Ω-m), representing thin fluvial sediments
deposited of Khajnawar Rao, seasonal river. The
thickness of this layer varies along the profile from
3 -20 m. Followed the fluvial sediments cover, in thenorthern zone of profile highly jointed and fractured,partially and fully saturated with water, Upper andMiddle Siwaliks formations are present. The
average resistivity of this zone is 40 Ω-m. The
dominant rocks in Upper Siwaliks and Middle
Siwaliks are conglomerate and sandstone
respectively. In the southern end of the profile, therocks are more compacted and represented by highresistivity (> 500 Ω-m) formations. The fault corezone is a fine material partially and fully saturated
with water, represented by low resistivity (~ 10 Ω-m).
Further analysis of 2D and possible 3D effects in
TDEM data and the integration of existing RMT data
will further improve the interpretation.
Acknowledgements
This work is carried out in the frame of INDOGERMAN joint research collaboration, DST-DAAD,
Project no. DST/INT/DAAD/P-19/2019. Financial
support from funding agencies (DST and DAAD) are
thankfully acknowledged.
REFERENCES
Haroon, A., Yogeshwar P., ockmann J. B, Ossen
D. November 30, 2015, EMUPLUS ManualJens, E. D., Esben Auken, Flemming Jørgensen,
Verner Søndergaard, Kurt I. Sørensen (2003)
The
applicationofthetransientelectromagnetic method in hydrogeophysicalsurveys, Journal of Applied Geophysics,
Volume 53, Issue 4, ISSN 0926-9851, Pages
181-198,
Kaufman, A. A. and Keller G. V. (1983) Frequency
and Transient Sounding Methods inGeochemistry and Geophysics, Vol. 16,
Elsevier, Amsterdam, pp. 686
Kumar, R. and Ghosh, S.K. 1991. Sedimentological

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Israil M et al., 2022, Near surface resistivity structure of HFT zone in Sub-Himalayan region

studies of the Upper Siwalik Boulder
Conglomerate Formation, Mohand area,
district Saharanpur, U.P., Jour. Him. Geol.,
2,159-167
Nabighian, M. N. & Macnae, J. C., 1991. Time
Domain Electromagnetic Prospecting Methods,
in Electromagnetic Methods in AppliedGeophysics, vol. 2, chap. 6, ed. Nabighian, M.
N., Society of Exploration Geophysicists
Senthil Kumar, Steven G. Wesnousky, Thomas K.
Rockwell, Richard W. Briggs, Vikram C.
Thakur, and R. Jayangondaperumal (2006),
Paleoseismic evidence of great surface rupture
earthquakes along the Indian Himalaya,
Journal of Geophysical Research, Vol. 111,
B03304, doi:10.1029/2004JB003309, 2006.
Srivastava, V., Malay Mukul, Jason B. Barnes, 2016
Main Frontal thrust deformation andtopographic growth of the Mohand Range,
northwest Himalaya”, Journal of Structural
Geology, Journal of Structural Geology 93,
131-148
Wesnousky, Steven G., Senthil Kumar, R. Mohindra,
and V. C. Thakur, (1999), Uplift andconvergence along the Himalayan Frontal
Thrust of India, TECTONICS, VOL. 18, NO. 6,
PAGES 967-976
Yogeshwar, P., Küpper, M., Tezkan, B., Rath, V.,
Kiyan, D., Byrdina, S., Cruz, J., Andrade, C.&
Viveiros, F. (2020). Innovative boat-towed
transient electromagnetics—investigation of
the Furnas volcanic lake hydrothermal system,
Azores. Geophysics, 85(2), E41-E56

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Some fragments of my 70 years activity in EM geophysics
I.I.Rokityansky
Subbotin Institute of Geophysics of the National Academy of Sciences of Ukraine, Kiev, Ukraine
E-mail: rokityansky@gmail.com

SUMMARY
It is my farewell to EM community. I recall some earlier published half-forgotten results which can be useful
today and express my opinion how can be improved some methodological details of EM studies.

Keywords: geoelectromagnetics, electrical conductivity anomalies, Carpathian anomaly

INTRODUCTION
In 1953, I was graduated from the Faculty ofPhysics of St. Petersburg University (SPbU) with adegree in geophysics and was allocated to Moscow
for postgraduate studies at the Institute of Physics
of the Earth (IPE). An outstanding mathematician(regularization
theory)andgeophysicist(magnetotelluric sounding (MTS)) academician
Andrei Nikolaevich Tikhonov were appointed as mysupervisor. I chose the topic "Induced polarizationof ion-conducting rocks" and defended my PhD
thesis in 1957 (Rokityansky 1957, 1959). At thattime a great event began - the International
Geophysical Year (IGY), and I was sent to Alushta,
Crimea to equip the observatory and carry out
registration and processing of natural variable
electric E and magnetic B fields with a period from1 s to 1 day. I had unique materials in my hands. I
was interested in the relationship between E and B
and its variability along the Earth's surface, that is,in essence, the MTS-MVP (Rokityansky 1961,
Rokityansky et al. 1964). In the first decade of the
MTS method, the Tikhonov-Cagniard (T-C) model
was used in its purest form - a plane wave over a
horizontally layered Earth. As a result, depth to the
conductive base of the upper mantle was obtainedat depths from 100 to 2000 km, even at close
observation points. Such a spread did notcorrespond to the theory of EM induction and theresults of other geophysical methods. I analyzed
many practical MTS curves, made model
calculations and was the first who discovered theexistence of static distortions, and formulated
simple methods for minimizing them – binding(tying in) to trusted data, static shift to global GDS
data, grouping, which made it possible to carry out
deep MTS on the real geological structures withlocal distortions (Rokityansky 1971, 1982, p.
201-209).
In 1957-1965 I worked in IPE department EM fields

headed byValery Troitskaya.
Valery was outstanding personality, scientist andorganizer of international cooperation in the «coldwar» times. She was born in 1917. In the years ofrepressions of 1936-1939, her father was arrested
and awaited an execution. To save him Valery, astudent of SPbU, decided to ask for an audience
with the formidable head of KGB, Lavrenty Beria,
and managed to convince him of her father's
innocence. Father was released (the rarest case).
During IGY and after, Valery decisively for the
benefit of cooperation violated the ridiculous KGB
restrictions on the behavior of Soviet citizens
abroad.
She established a network of electromagnetic
observatories across USSR, in Arctic andAntarctica and studied ULF micropulsations withperiod 1-600 s. She organized the first experiment
betweenmagneto-conjugate
pointsSogra-Kerguelen and measured geomagnetic
fluctuations at the depth 2.6 km in the Frenchbathyscaphe«Archimed».
She
made
2
internationallyrecognizeddiscovery:
micropulsations «Pearls» and «DPO» - decreasing
period oscillations indicating contraction of themagnetosphere. Troitskaya was member of the
IUGG Bureau (1963-1967), the first woman
president of IAGA (1971-1975), since 1985 Honorary IAGA Member, member of the firstSteering
Committee
oftheInternational
Geosphere-Biosphere Program (1986-1990). I
participated in some of these studies, as well as in
the registration of USA nuclear tests on a Pacificisland and Soviet underground tests in Central Asia.
I also organized an EM observatory in Cuba.
In 1960 academician M.A.Sadovsky became the
Director of IPE. He obliged all departments to

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participate in the Earthquakes (EQ) Forecast. This
topic also was proposed to me. I refused and in1965 moved to Institute of Geophysics in Kiev forGeoelectromagnetic investigation of the Earth’s
crust and mantle
I organized the EM Research Laboratory and led it
25 years. In the first 5 years I have tested, studied
and understood the possibilities of various methodsof the deep electrical conductivity study andformulated a methodology for the interconnected
use of the three main methods MVP-MTS-GDS(Rokityansky 1970 and in all my consequentmonographs). Search and study of the anomaliesof geomagnetic variations and associatedconductive anomalies was a hobby for me. Every
year we carried out field works for 3-4 months. I
went out into the field and manually processed andinterpreted the photorecords in order to determine
where the next observation sites should be placedin order to optimally map the anomaly.
Many anomalies have been found and studied
using induction vectors and anomalous field inhorizontal magnetic components, that is by MVP
method. Large anomalies in Eastern Europe:
Kirovograd(1969),
Carpathian(1972),
Moscow-Tambov (1977), Ladoga (1981, moreover,
MTS were carried out there before the MVP, but theanomaly was not discovered), Donbas (1988, but inthe data of more than twenty MTS and records ofpulses from the Volgograd-Donbas long (487 km)line the anomaly cannot be clearly distinguished).
Physics of the anomalous field formation
Anomalous currents in a high conducting bodyarise due to local electromagnetic induction inside
this body, as well as due to conductive
redistribution (and concentration) of currents
induced in the host medium on the large territory
comparable with the external source size.
Quantitative estimates were made in the early
1970s based on analytical solutions for a cylinderand sphere presented as an infinite series which
first term is proportional to the applied electric field(it forms the conductive anomaly), the second – tomagnetic field – it forms the magnetic eddy typeanomaly. Available
results of 2D and 3D
numerical calculations and physical modeling werealso considered.
Analysis of natural situations showed that
conductive type anomalies are predominate for
elongated conductors, and a corresponding theory
was developed for them (Rokityansky 1975-a and-b; 1982 p. 247-277, 290-307). The frequency
characteristics of the conductive type anomalous
field are equal to the product of the non-decreasing
function of the period V (0≤V≤1, V=1 correspondsthDC), which describes the degree of filling of the
conductor by anomalous currents and the normal
impedance of the given region, apriori studied by
the GDS-MTS soundings. The impedance is adecreasing function of the period, so its productwith function V has a maximum at some period T O.
The position TO is closely related to the total
longitudinal conductance G [S×m] - of theanomalous body, that is, the scale of the anomaly.
On the period TO, the anomalous fields and theinduction vector become real C=Cu, the imaginary
induction vector Cv passes through zero changing
sign. On shorter periods Cu and Cv are parallel, on
longer periods they are anti-parallel for 2D
anomaly.
Short specification of the MVP method
Necessary and sufficient condition to make theconclusion on the existence of an electricalconductivity anomaly is the existence of ageomagnetic variations anomaly.
The existence of an anomaly of geomagnetic
variations is evident from the stable presence ofvertical component and from the difference inhorizontal components at moderately spaced sites.
The shape of the profile curves of the anomalous
field yields reliable estimation of the maximum
possible depth of the anomalous currents center dand the width 2L of the anomaly.
Frequency response of the anomalous field
yields estimate of the total longitudinal
conductance G of the elongated anomalous body.
- It is a simple completely reliable MVP
pre-computer results, which (anomaly location, d, L
and G with their uncertainties) should be used as
apriori information for consequent thorough
MVP-MTS inverse problem computer modeling.
MTS
MTS can give false results even in relation to sucha fundamental category as the existence orabsence of an anomaly. For example, on thesouthern slope of the Ukrainian Shield (US), a
dozen MTS curves above the axis of the
Kirovograd anomaly, which is confidently identified
according to the MVP data, show the absence of
this anomaly (the reason is screening by sloping
sedimentary layers).
However, MTS can more accurately than MVP
determine the depth h of the upper edge of theanomaly although harmful distortions fromnear-surface inhomogeneities remain. So, a dozen
MTS over the axis of Kirovograd anomaly in US
made it possible to determine only the average

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result of h=15±5 km (Rokityansky et al 2018)transformation of response functions.

Carpathian anomaly.

Esotericism and related issues

Let me remind of the Carpathian anomaly. Having
data of more than 200 MVP points, the position of
its axis was determined with good uncertainty of
5-10 km, and according to 8 profile graphs of theanomalous field in the Western Carpathians, themaximum possible depth of the center ofanomalous currents was estimated: 24, 16, 19, 18,
18, 24, 26 and 21 km (Jankowski et al. 1985). The
next step should be MTS to clarify the depth of theupper edge of the anomaly. In Ukraine, in the
1970s and 80s, we made about 50 MTS, identified
6 zones and, using 11 curves directly above theaxis, determined the depth of the anomaly upperedge of 12±4 km.

In 1989 I became interested in dowsing considering
it as geophysical method but without support inphysics which I planned to achieve. In next 5-years
laboratory Plan-Project instead of the MT topic(which I led 25 years), I naively proposed a topic on
dowsing, which was not accepted in institute. I wasleft without a Project and was sent to retire.
2 years I worked in a private company on esoterics,
in 1994-1998 in «Ukranian Natational ResearchCenter for Defense Technologies» was the only
geophysicist and had the task of developing
geophysical weapons. There, I tried to improve
Kozyrev's theory (Rokityansky II 2008, 2012),
studied the locality of thunderstorms and theirconfinement to geological structures, studied
experiments on informational weather control andproposed its explanation (Pokityansky II 2014),
studied the tectonic factor of the Chernobyl disasterand so on. After the collapse of the USSR, theeconomy constantly degraded in independent
Ukraine and nobody’s developments were used.

At 22EMIW in Murnau I proposed «Project of the
Carpathian anomaly depth study by means ofdetailed MTS over its axis». Project was notsupported with a criticism: «for the MTS
interpretation 2D profiles across the anomaly arenecessary» - I strongly disagree with the latter.
When the anomaly axis is known, only longitudinal
curves are needed to determine its depth.
The anomaly in the center of Europe should bebetter studied. It can be, for example, a source ofgeothermal energy. I recommend to accept andrealized «Project of the Carpathian anomaly depth
study by means of detailed MTS over its axis» andwould be happy to participate in it.
On 2D inversion and regularization
And in general, I think the mandatory 2D inversion
is not justified. Exact 2D structures do not exist, theinevitable presence of deviations from 2D creates asystematic error in the 2D inversion results, whichcannot be predicted and described using astatistical uncertainty obtained during dataprocessing.
Unlike practical science (electrical prospecting),
deep geoelectrics is regarded as a fundamental
science, which sets the task of an honest, reliable
study of the objective reality — the Earth. Since
observation of electromagnetic fields is possible
only on/above the Earth in a limited number of sites
with limited accuracy, the conclusions ofgeoelectrics are always ambiguous. Providing onlya single solution, in particular resulting from the useof regularization, can lead to false conclusions, andregarded as a manipulation of facts, that candiscredits both the authors and the whole scienceof geoelectrics. So, many products of inversion (inparticular 2D) should be regarded not as a wellproven geological result but as one of possiblethReturn to geophysics
In 1999, at the EGU Assembly in Nice, I organizeda Session on Quasi-spontaneous Variations(Rokityansky 1999). In 1999-2001, I collaborated
with the discoverer of seismic electric signals (SES)
prof. P.Varotsos (Greece). To explain high SES
sensitivity in Ioannina place, Varotsos supposed
existing under the place highly conducting channel.
≈40 MTS were made and interpreted, 2 PhD
dissertations were defended but channel was notfound. I made MVP and joint MVP-MTS data
revealed conductor and its parameters.
I returned in Institute of geophysics, but noinstruments for field works, no money for businesstrips to conferences and field works, for purchasing
and printing articles. I and 3 PhD students
processed worldwide Intermagnet data, Japanesegeomagnetic data and obtained some results(Rokityansky en al 2019). Very low salary inacademic institutes made my students move fromthe institute to private IT companies.
REFERENCES
Jankowski J et al. (1985) The results of GDS in
west
Carpathians.
Geophys
JRAstrSoc
80:561-574
Rokityansky II (1957) Laboratory study of induced
polarization of sedimentary rocks. Izv AS USSR
sergeophys 2: 217-227
Rokityansky II (1959) On the nature of induced

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polarization of ion-conducting rocks. Izv AS
USSR ser geophys 7: 1055-1060
Rokityansky II (1961) On the application ofmagnetotelluric method on anisotropic andinhomogeneous massifs. Izv AS USSR sergeophys 11: 1607-1613
Rokityansky II et al (1964) The coast effect in thevariations of the Earth’s electromagnetic field. J
Geomagn Geoelectr 15: 271-274
Rokityansky II (1970) Study of deep electricalconductivity. Geophys comm AS UkrSSR 38:
102-108
Rokityansky II (1971) Deep magnetotelluric
soundings at the presence of distortions fromhorizontal inhomogeneities. Geophys Comm AS
UkrSSR 43: 71-77
Rokityansky II (1975-a) The interpretation of
anomalous fields by using their frequency
characteristics. PEPI 10: 271-281
Rokityansky II (1975-b) Investigation of electricalconductivity anomalies by the method ofmagneto-variational profiling. Naukova Dumka,
Kiev, 279 p.
Rokityansky
II(1982)
Geoelectromagnetic
invtstigation of the Earth’s crust and mantle.
Springer-Verlag, Berlin Heiderberg New York,th381 p.
Rokityansky
II(1999)
Phenomenon
ofquasi-spontaneous
globally
synchronizedvariations of physical parameters (QSV). Phys
Chem Earth (A) 24: 705-710
Rokityansky II (2008) Absolute motion as the basisof Kozyrev’s theory of time. Acta Geod Geoph
Hung 43(4): 461-469.
Rokityansky II (2012) North-South asymmetry ofplanets as effect of Kozyrev’s causal
asymmetrical mechanics. Acta Geod Geoph
Hung 47(1): 101-116.
Rokityansky II (2014) Physical basis of weather
informational correction. Geoinformatics 2(50):
77-80.
Rokityansky II, Sokolova EYu, Tereshyn AV,
Yakovlev AG (2018) Electrical conductivity
anomalies in the junction zones of the Archean
and Proterozoic geoblocks of the Ukrainian andBaltic shields. Geophys J 40(5): 208-244. Doi:
10.24028/gzh.0203-3100.v40i5.2018.147490
Rokityansky II, Babak VI, Tereshyn AV, Hayakawa
M (2019) Variations of Geomagnetic Response
Functions before the 2011 Tohoku Earthquake.
Open
J
EarthquakeRes8:70-84.
https://doi.org/10.4236/ojer.2019.82005

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Improving geophysical model resolution with magnetotelluric and gravity joint
inversion: application to the Asal Rift geothermal region, Republic of Djibouti.
Rachid Robleh Rageh1,3, Pascal Tarits1,2, Sophie Hautot2, Mohamed Jalludin3
1University Western Brittany, Brest, France
2IMAGIR Sarl, Saint Renan, France
3Center for Studies and Research of Djibouti (CERD),SUMMARY
Magnetotellurics (MT) provides images of low resistivity alteration characteristic of hydrothermal
alteration
generally associated with active up flow. The accuracy of the resistivity images is therefore an important factor
in defining the conceptual model. The sensitivity of the MT decreases with depth. To improve the geophysicalimage, we investigate how to combine MT, gravity and drilling data in the Asal Rift region of the Republic of
Djibouti. In this study, we present a new gravity inversion approach constrained by a 3D MT resistivity modelon the Asal Rift data. The results are conclusive and promising in the application to natural resources
exploration.
Keywords: Magnetotellurics, gravity, constraint inversion, geothermal,

INTRODUCTION
Geothermal exploration in the Republic of Djibouti(Figure. 1) has led to several boreholes being drilled(BRGM, 1975a, b and AQUATER 1989, Caver,2019). Field studies and deep drilling revealed highsalinity, low permeability, dry boreholes despite
positive indications from geophysical models.

Because of their complementarity, magnetotelluric(MT) and gravity measurements are generally used
to characterize this geothermal resource. We
present in this study a linear gravity inversion
approach constrained by the MT resistivity modelthrough cross-gradient coupling. This approach
could help reduce the non-uniqueness of gravity
inversion with a short computational time comparedto the traditional joint inversion collected in the Asal
Rift Zone, Djibouti (Figure 1).
MT AND GRAVITY INVERSION

Figure 1. (a) Geological and structuralcharacteristics with the main geothermal drillings of
the Asal Rift (modified from Le Gall et al., 2015). (b)an ASTER digital elevation model, the black
rectangle is the study area.
The major difficulty in obtaining a global vision of theresource is to have a methodology to characterize
the geological and geothermal structures at depth(heat source, circulation of hydrothermal fluid,
impermeable roof) and to understand how the areasof real interest for exploitation can be evaluated.

We used data from 79 MT sites in this study (Figure.
2), obtained during an exploration undertaken in2008 through a collaboration between ISOR in
Iceland and the Centre d'Etudes et de Recherches
de Djibouti (CERD). The data were recorded usinga Phoenix MTU-5A system. Tthe gravity data comefrom a large survey carried out by the Bureau deRecherches Géologiques et Minières (BRGM,
France) in the Asal rift in 1979 and 1980 (Demange
and Puvilland, 1990,). In total, we used 641 data
points (Figure. 2), acquired with a Worden gravity
meter n°641 and a Lacoste-Romberg graviteters.
The 3D MT inversion of the full impedance tensors
was performed with the MINIM3D code (Hautot et al,2007) for all stations (Figure. 2) and at all available
times in the Asal Rift. The MT inversion was
performed on a 29×28×15 grid oriented along the 40° N direction which corresponds to the rift
propagation direction (Magnighetti et al., 1998). The
initial root mean square (RMS) of 23.2 is reduced to
3.2. We developed a 3D gravity inversion scheme
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and inverted theresidual gravity data on a grid
similar to the one used for the 3D MT inversion(Figure 2) of the resistivity model. We have also
represented in Figure 4, the unconstrained density
model of these 4 profiles.
Figure 3 shows the results of the P1 to P2 profiles(Figure 2) of the resistivity model. We have also
represented in Figure 4, the unconstrained density
model of these 4 profiles.

Figure 2. Asal Rift study area, gravity data inorange, MT data in blue, boreholes in red star andblack lines are the profiles (P1 to P4).

CONSTRAINED INVERSIONIn this section, we propose a constrained linear
inversion to combine these two methods. The
advantage of this approach is to couple gravity andMT data, to identify the same structures withextremely reduced computational time unlike theclassical joint inversion. The most widely usedmethod is the cross gradient developed by Gallardo
and Meju (2003, 2007). The cross gradient coupling
function is defined by (eq. 1): The cross-gradientcoupling function in a joint inversion formulation is
nonlinear as density and resistivity vary. Here, we
are actually dealing with a resistivity-constrained
inversion and, therefore, relation 1 becomes linearwith respect to the density, as we have considered
the resistivity to be constant. The new cost function
is now defined as follows(eq.2):2
𝐶𝑂2 = ∑𝑀
𝑚=1 |𝛻𝑑 × 𝛻𝑟|

(1)

2
2
2
𝐿2 = ‖𝑔⃗⃗⃗⃗⃗⃗⃗⃗
𝑜𝑏𝑠 − 𝐺𝑑 ‖ + 𝜆‖𝑊𝑧 𝑑 ‖ + 𝛼𝐶𝑂(2)

The solution of equation 2 is written as follows:−1𝑑 = (𝑡𝐺𝐺 + 𝜆𝑊𝑧 + 𝛼𝑍) 𝑡𝐺𝑔⃗⃗⃗⃗⃗⃗⃗⃗
𝑜𝑏𝑠(3)

Where G is the sensitivity kernel matrix, gobs is theobserved data. A weight Wz is introduced in the costfunction, which depends on the mean depth foreach cell (Li and Oldenburg 1998)
RESULTSThe results of the inversions are presented in theform of the following vertical sections 4 profiles thatcross the boreholes (Figure. 2).
Figure 3 shows the results of the P1 to P2 profiles

Figure 3. Vertical section of the 3D resistivity
model, along the profiles of Fig. 2

Figure 4. Vertical cross-section of the 3D
unconstrained density model, along the profiles inFig. 2.
In the 4 resistivity profiles (Figure. 3) at the level ofthe boreholes, we have a very resistive layer thatappears from ground level to 400 m depth, due tothe presence of basalts and hyaloclastites that
occurred during the last basaltic eruption in 1978
(Demange et al, 1980). Then comes a very
conductive layer from 400 to 1200 m, due to thepresence of Pleistocene clays and trachyte. This isfollowed by a less conductive layer up to 2200 m
which probably corresponds to basalt having

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undergone alteration, rhyolites (AQUATER, 1989).

Figure 5. Vertical cross-section of the 3D
unconstrained density model, along the profiles in
Figure 2
The unconstrained density models (Fig. 4), show
mainly two features: two positive anomalies in the
NW-SE and SW-NE directions, separated bynegative anomalies in the center of the 4 longprofiles. However, these structures have low lateral
resolution within the first kilometer in depth.
The result of the resistivity-constrained density
model (Figure, 5), differs significantly from theunconstrained density model. Especially in the deep
layers between 800 and 4806 m, where positive andnegative anomalies due to resistivity constraints
appear (Figure, 3). For the individual profiles (Figure,5), the constrained model describes well with goodvertical resolution the structural features that arepresent in both individual models (Figures, 3 and 4).
Furthermore, due to the resistivity constraint, theboundaries separating the negative and positive
anomaly domains are clearly visible in theconstrained density model (Figure, 5)). The highly
conductive layers in the resistivity model of thedifferent profiles (Figure. 3), present between
depths 143 and 1046 m, are placed almost at thesame level in the constrained model, but with asmall density contrast close to 0 g.cc. This may be
due to the fact that the density and resistivity
gradient tends to zero, or it may be due to the factthat there are no structural similarities between
these two physical parameters in the near surface.
With the exception of borehole F1 (Figure. 2) whichshows a strong anomaly in the P4 profile, all otherdrill holes show negative densities in the correlationof conductors showing the resistivity pattern. These
two indices are positive for geothermal exploration.

DISCUSSION
Gravity inversion constrained by the 3D resistivity
model of the Asal Rift data led to a density model(Figure. 4), with clear visibility of fault zones at depth.
Notably in the Fialé caldera area (Figure. 4, P4),
where a negative density contrast zone associatedwith a hydrothermal metamorphic zone or possibly
a mineralized water-saturated basaltic zone isobserved to the southwest and a heavy unit zone
possibly due to a volcanic (basalt) formation to thenortheast (Figure. 4, P4). Numerous open faults areobserved in the fialé caldera zone and numerous
fumaroles are also present (Figure. 1). In profiles P2,P3 and P4, the constrained density model highlights
a rising structure with very low densities -0.2 g.cc
(Figure. 14 b). This structure corresponds to the
upward flow directly connected to the magma
chamber. This is in good agreement with theresistivity model and the seismic model (Doubre,
2004) which respectively interpret a zone of very
low resistivity and a seismic zone between 15004000 m originating from the interaction of hot rockand overlying rocks (rhyolite, basalt) or perhaps
molten magma in a strongly faulted zone. Gravity
inversion constrained by the 3D resistivity model,
adds information on structural geometries, fault
zones, water intrusions and allows visualization of
upward fluid flow.
CONCLUSIONSWe tested the linear inversion approach using thecross-gradient technique on real data on the Asal
Rift without any a priori geological information. Thismethod allows the recovery of individual models.
These tests also showed that a joint inversion canbe performed in a geological context that does not
require a priori petrophysical knowledge. The
results of these tests are conclusive and promisingin the application of natural and other resources.
The drawback of this joint inversion of the different
physical parameters is the determination of thecoefficients of the cost function (eq. 2). Although thecoupling coefficient plays a role in calibrating theconstraint of the physical parameter targeted in thiscase for us the density, it is, therefore, necessary to
perform several inversions by varying the weight ofthe cross gradient coupling. However, from this typeof inversion, the speed of the computation time, witha good resolution of the lateral of the constrainedmodel, follows.
ACKNOWLERGEMENTS
We thank the International Gravity Bureau, forhelping us with the geographical coordinates of the
Asal rift gravity data. IPG Paris provided the
Ghoubbet bathymetry

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Robleh, RR. et al., 2022, MT and gravity joint inversion at lake Asal, Djibouti

REFERENCES
AQUATER. (1989). Djibouti geothermal exploration
project: Republic of Djibouti. AQUATER.
BRGM : Territoire Français des Afars et des Issas:
rapport de fin de sondage: résultats des premiers
essais de production. BRGM75-SGN-442-GTH(1975a) 18p.

france and surrounding margins. Tectonophysics
164 (2-4), 251-258.
Manighetti, I.; Tapponnier, P.; Gillot, P.Y.; Jacques,E.; Courtillot, V.; Armijo, R.; Ruegg, J.C.; King, G.
Propagation of rifting along the Arabia-Somalia
Plate Boundary: Into Afar. J. Geophys. Res. Space
Phys. 1998, 103, 4947-4974.

BRGM : Territoire Français des Afars et des Issas:
rapport de fin de sondage, interprétation desdonnées géologiques de Asal 1 et Asal 2. BRGM75SGN-443-GTH. (1975b) 19p
Carver, C. T., Garg, S. K., Davis, L. C., & Jalludin,
M. Reservoir Characterization from Exploration Well
Completion Tests in the Fiale Caldera,
Djibouti.(2019)
Demange, J., Stieltjes, L., & Varet, J. (1980).
L'éruption d'Asal de novembre 1978. Bull. Soc. Geol.
Fr, 7, 837-843.
Demange, J., & Puvilland, P. (1990). Champgéothermique d’Asal. Synthèse des Données. Bur.
de Rech. Geol. et Min., Orléans, France.
Doubre, C. (2004). Structure and mechanisms of
volcano-tectonic rift segments: studies of ancient
rifts (Scotland, Iceland) and an active rift (AsalGhoubbet) (Doctoral dissertation, Le Mans
Gallardo, L. A. (2007). Multiple cross-gradient joint
inversion for geospectral imaging. GeophysicalResearch Letters, 34(19). 178, 198
Gallardo, L. A. and Meju, M. A. (2003).
Characterization of heterogeneous near-surface
materials by joint 2d inversion of dc resistivity andseismic data. Geophysical Research Letters, 30(13).
178, 198
Hautot, S., Single, R., Watson, J., Harrop, N.,
Jerram, D., Tarits, P., Whaler, K., Dawes, D., 2007.
3-d magnetotelluric inversion and model validation
with gravity data for the investigation of flood basalts
and associated volcanic rifted margins. GeophysicalJournal International 170 (3), 1418-1430.
Le Gall, B., Daoud, M. A., Maury, R., Gasse, F.,
Rolet, J., Jalludin, M., ... & Moussa, N. (2015). Carte
Géologique de la République de Djibouti, Echelle
1/200 000. Geological Map of the Republic of
Djibouti. Scale1/200000
Li, Y., Oldenburg, D. W., 1998. 3-d inversion ofgravity data. Geophysics 63 (1), 109-119. Lucazeau,
F., Vasseur, G., 1989. Heat flow density data from
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3D inversion of drone EM data -- the DroneSOM project
Longying Xiao1, Cedric Patzer2, Jochen Kamm3
Geological Survey of Finland (GTK), 1 longying.xiao@gtk.fi; 2 cedric.patzer@gtk.fi; 3 jochen.kamm@gtk.fi;

SUMMARY
The application of electromagnetic geophysical methods has been actively contributing to various engineering,
environmental and economic fields. In Finland, forests and lakes cover more than 75% and 10% of the landarea, respectively. This makes geophysical fieldwork such as mineral exploration and groundwater mapping
more challenging and costly. Drone measurements are therefore a cost-effective and environmental-friendly
approach, especially for large-scale mapping.
The DroneSOM (Drone Geophysics and Self-Organizing Maps) project, funded by EIT RawMaterials, intends
to develop drone-based gravity and electromagnetic (EM) exploration instruments and associated datainterpretation software. Together with the partners from both academia (i.e., Technical University of Denmark)and industry (i.e., RADAI Oy, Beak Consultants GmbH.), we aim to provide commercial geophysical solutions,as well as free educational tools at the end of the project. In particular, a set of efficient and robust 3D EM
modeling and inversion code will be the kernel for EM data interpretation. We hereby present the frameworkof 3D frequency-domain EM inversion envisioned by project.
In this work, we present numerical examples, where we solve Maxwells equations using a total field electricfield formulation. For the model meshing, the code supports rectilinear, curvilinear, and octree grids (Kamm etal., 2020; Xiao et al., 2022). The edge finite element method was used to discretize the diffusion equation
derived from Maxwell’s equations. To solve the resulting linear system of equations, we use for now the direct
solver MUMPS. The object-oriented code is implemented in C++ to allow for easy adaptation for various sourceand data types.
For the inversion, we use the standard multi-component objective function, which includes data norm andmodel regularization norm. To solve the adjoint modeling and obtain the model update at each iteration, we
use PETSc library. In general, we employ deal.II library (dealii.org/) to perform the heavy computation, suchas the modeling equation discretization, linear system equation solving, and inversion model update, as itcomes with interface wrappers to MUMPS and PETSc. Currently, the code is parallelised using the MPI
framework for both forward and inverse modelling.
Keywords: DroneSOM, 3D, Inversion, frequency-domain, electromagnetic

REFERENCES
Kamm, J., M. Becken, and R. Abreu, 2020, Electromagnetic modelling with topography on regular grids withequivalent materials: Geophysical Journal International, 220, no. 3, P 2021–2038.
Xiao, L., G. Fiandaca, B. Zhang, E. Auken, and A.V. Christiansen, 2022, Fast 2.5D and 3D Inversion oftransient electromagnetic surveys using the octree-based finite element method: Geophysics, 0: 1-53.

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Drone based experimental TEM surveys over Lake Baikal and a uranium occurrence
V. Hallbauer-Zadorozhnaya1, Yu. A. Davydenko2,3, Parshin A.V.2, and E. Stettler1,41
AeroPhysX (pty) Ltd, South Africa, valeriya.hallbauer@gmail.com
2
Irkutsk National Research Technical University, Russia, dya@geo.istu.edu
3Gelos Ltd, Russia, davidenkoya@gmail.com
4
University of Witwatersrand, South Africa, stettlere@gmail,com

SUMMARY
The results of experimental TEM surveys using drones, carried out at the Bolshoe Goloustnoye site on Lake
Baikal and at a uranium deposit are presented. L Baikal is a unique geoelectrical situation, where lowresistivity lacustrine sediments are located under a relatively isotropic water body. The upper part of thesedimentary sequence is represented by diatomaceous silt with high conductivity, the lower part has denser
clayey sediments. The task was to investigate the possibility of using a drone based TEM survey fordelineating the electrical stratigraphy of the subsurface at depths between 50-200 m separated into layers
and blocks.
The geology of the uranium bearing area consists of a high-resistivity crystalline basement, overlain by lowresistivity sedimentary deposits (sands, clays) and effusive rocks and the TEM survey was undertaken to
determine if the U mineralized strata have a distinctive resistivity signature.
The combined instrumentation unit consisted of an EGI-500 generator providing bipolar current pulses, aMars 4.0 which is a multi-channel electrical prospecting potential difference recorder, a PDI-50 receiver loop
and a SibGIS UAS - unmanned 6-rotor aircraft with a payload of up to 10 kg.
In both cases, a grounded electrical line AB was used as a source of the electromagnet field. Sixteen
traverses of survey data were measured over the Baykal ice sheet and five traverses over the uranium
deposit.
For the interpretation of the TEM data the S technique was used, which allows tracing the change in theapparent longitudinal conductivity with depth. On the sections, a high-resistivity layer is clearly distinguished,corresponding to the water of Lake Baikal as well as sedimentary deposits, composed by several blocks of
low resistivity. The U-bearing layers are confidently identified in all profiles at depths of 120-170 m.
Keywords: Unmanned Aerial Vehicles (UAV), drone, TEM, S-plane, conductance

INTRODUCTION
In the first two decades of the 21st century
airborne electromagnetic surveying has become
widespread. In recent years, drones have beenused in the transient EM soundings method. The
UAS technology has several advantages overconventional airborne platforms to collectgeophysical data such as resolution, accuracy, andcost but off-course presently have limited
endurance and weight carrying capabilities. But incomparison to ground surveying a drone cancollect in several days hundreds of thousands ofsounding points. In the aircraft and helicopter
version, as a rule, the field source (transmitterloop) is mounted directly on the wings of theaircraft or suspended on a cable under thehelicopter. The weight restriction of a drone does
not allow carrying a weight of more than 10
kilograms, where the transmitter source arelocated on the ground. As a rule, an ungroundedrectangular loop is used as a source of electricalcurrent. In our cases, a long grounded AB line witha current of 5 A was used.
Experimental TEM surveys have been carried out on
two sites: at the coastal part of Lake Baikal (delta ofBolshoe Goloustnoye river) and over a uranium
occurrence in Easter Siberia. TEM surveying at bothsites were made within one, two day period andone single day, with a high data density: the flightline spacing varied between 50 and 100m, the data
sampling rate along flight lines is about 10 m.
INSTRUMENTATION AND OPERATIONS TECHNOLOGYThe UAV-MPP technology applied on Lake Baikal
and on at the uranum deposits used a special
versions of the components of the ‘Mars’ electricalsurvey system (Patent No. 2574861), and the
SibGIS UAS unmanned platform (Patent No.
2736956).
The instrumentation assembly consist of thefollowing units described below. On the ground we
have:

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a) EGI-500 generator that delivers bipolar 50%
duty cycle current pulses. It consists of two
separate units, one delivering the stabilized current
and the second a pulse switcher. The duration of
the block shaped current pulse is 5 ms on and 5ms
off.
b) A long (2.2 km) grounded transmitter line AB isused as a source of the current pulses. The ironstakes (electrodes) of the transmitter cable are
grounded into the water through holes drilled into
the ice.
In the air we have:a) A SibGIS UAS which is an unmanned 6-rotor
hexacopter with a payload of up to 10 kg.
b) The “Mars 4.0” which is a multi-channel
electrical prospecting potential difference recorderthat collects the full TEM data series andfunctionally consists of an ADC, a GPS-enabledsynchronization unit, a data storage module and a
USB interface for connecting to a PC.
c) A PDI-50 receiver loop hanging below thehexacopter and has an equivalent surface area of
50 m2 that records the secondary induced field.
d) Microcomputer with a GNSS-reference system
mounted on to the drone. The onboard software
stores the data.
The Figure 1 demonstrates the drone withsuspended receiver box and vertical magnetic
dipole (loop).

Figure 1. Unmanned Aerial Vehicles with receiverbox and VMD. Diameter of VMD is 1 m, surface
are 2500 m2.
INTERPRETATON VIA MODIFICATION OF THE
CALCULATION OF APPARENT LONGITUDINALCONDUCTANCE S

The so-called method of differential transformationof emf signals into the curves of apparent
longitudinal conductance as function of time or
depth (method Stau, S) was proposed by Sidorov

and Tikshaev (1970). The Smethod has certain
advantages. Its use allows determining the
conductance of the section and determining thedepth (or time) where Sincreases noticeably.
Usually increasing conductance is associated withthe presence of a low resistive object (bodies
and/or layers) in the resistivity section. We used
this property when interpreting airborne data. The
procedure of the modified method is described by
Davydenko et al. (2022) as well as presented inanother abstract presented on the 25th EM
Induction Workshop (Hallbauer-Zadorozhnaya andStettler, 2022).
RESULTS AND DSCUSSION
LAKE BAIKAL
An electrical line 2.16 km long was grounded(floated) into water of Lake Baikal along the shoreat the Bolshoe Goloustnoye site (Figure 2). Six
profiles were conducted parallel to the electricalline AB, nine – perpendicularly. The TEM points
located directly above the current line were notinterpreted due to the powerful noise generated byit.

Figure 2. The layout of the profiles on the site
Bolshoe Goloustnoye site on Lake Baikal. The
black line indicates the location of the AB line. The
profiles of electromagnetic induced polarization
soundings (EMS-IP) are marked in red.
The emf curves were smoothed and after applying
the procedure of the modified Stau method foreach sounding, St(H) curves are calculated. An
example of a cross-section of apparent longitudinal
conductance for profile 6 is shown in Figure 3.

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Figure 3. Apparent longitudinal conductance along
profile 6 (close to current line AB). The section
clearly shows a high-resistivity layer corresponding
to Lake Baikal water and sedimentary deposits.
However, noise, instrumental interferences andgeological distortions make it difficult to develop
software for interpretation, especially when the
TEM profile crosses the electrical transmitter line.
The change in the shape and intensity with
distance from the current source are now recorded
and will aid in future surveys and interpretation.
A sufficiently dense network of profiles made it
possibletopresentathree-dimensional
visualization of the TEM data using the model of a
“floating” plane. Figure 4 demonstrates the geoelectrical cuts at different elevations (200 m, 100m, -30 m, -200 m).

Figure 4.
Geo-electrical cuts at different
elevations (200 m, 100 m, -30 m, -200 m). colour
bar is in Figure 3.
Conductivity of the lacustrine sediments increase
with depth. A low resistive ring structure is locatedat some distance from the delta of the Goloustnaya
River.
URANIUM OCCURRENCE
The area investigated is located on the Viktim
Plateau, West Siberia. Five profiles have been
carried out. Permafrost is ubiquitous in the area, its
lower boundary is fixed at depths of 55-90 m fromthe surface. In the first approximation, thegeoelectric section can be represented as follows:- crystalline rocks of the basement (granitoids,
diorites, limestones, metamorphic shales andsandstones) have a resistivity (190 - 2000 Ohm.m)and occur at a depth of 120-160 m;- Overlying U enriched sedimentary deposits(sands, clays with a thickness of 40-150 m) have la
ow-density (2.0 g/cm3), are weakly magnetic (0.024
SI units), low-resistivity (40 Ohm.m) and lowvelocity (2150 m/s);
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- the effusive stratum (several covers of basalts
separated by tuff layers) is characterized by anaverage density (2.5 g/cm3), high magnetic
susceptibility (2.0 - 230 SI units), high resistivity(1200 - 8000 Ohm.m) and acoustic veloсity (3800m/s).
The contrast of physical properties predetermines
the effective use of the geoelectric methods toidentify the enriched strata of rocks in the section.
Figure 5 demonstrates structures on the west side(5a) and on the east side (5b).

secondary electromagnetic field rapidly propagatesin the resistive rocks but concentrates in the
conductor. Figures 5a and b show a threedimensional image of the geoelectric sections. It is
obvious that the correlation of the main elements ofthe block is correlated both horizontally and inplan, which indicates the stability of thetransformation algorithm Sand its applicability forinterpreting the TEM data.
CONCLUSIONS
Experimental airborne TEM surveying using adrone demonstrates the effectiveness of TEM
operations as shown in the Lake Baikal example
that senses an conductor below a thick layer (100250 m) of fresh highly resistive water.
Interpretation of the TEM data using the “floating”
plane model indicates the stability of thetransformation algorithm Sand its applicability forinterpretation the TEM namely for lateraldifferentiation in the upper part of the sedimentarymaterial and the identification of large structures ofvarious resistivity’s.
To optimize the interpretation process, it isessential to place the sounding profiles parallel too
and removed from the current (transmitter) line.
The survey carried out at the site of the uranium
occurrence showed the high efficiency of theairborne TEM in identifying and contouring the
conductor at a depth of 150 m.
ACKNOWLEDGEMENTS

Figure 5. 3D visualization of apparent longitudinal
conductance. a) View from the west, b) view fromthe east. Colour bar is on the Figure 3
These structural features of the section arecharacteristic of all five profiles. In the west part ofthe site at depths of 120-160 m, a remarkableconductor is observed, which we identify as aenriched complex. The east side of the site ishighly restive. It is known that another thin layer of
sedimentary rocks (also enriched) is located above
the main deposit. In this case, it is quite possiblethat in the geoelectric section both layers appear
as one with a slightly changed (convex) surface
shape. We also note that a decrease in thelongitudinal conductivity of the upper layer(basalts) is observed. This occurs because the

The research was supported by the Russian
Science Foundation grant No. 20-67-47037"Methodological and software for processing large
amounts of data from electromagnetic soundings,
gravity surveying and UAV magnetic exploration
based on a comprehensive solution of threedimensional inverse problems for ore delineation
by geophysics".
REFERENCES
Sidorov V.A., Tikshaev V. V (1970) Interpretationof transient electromagnetic signals registered innear zone. Expolration Geophysics, M. Nedra. 42:
pp.45-54 (in Russan).
Davydenko Yu.A., Hallbauer-Zadorozhnaya V.,
Bashkeev A.S., Parshn A.V (2022) UAV-TEM data
inversion with S-Plane method to highlight coastal
geological structure of Lake Baikal. The Near
Surface Geoscience Conference & Exhibition
2022. Belgrad, Serbia.

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Regional to Deposit scale exploration in Fennoscandia based on mineral systems
approach
Maxim Yu. Smirnov1 , Graham Hill2 , Jochen Kamm3 , Jan Vozar4 , Jirigalatu1 , Pankaj Mishra3 , Kenneth
Muhumuza2 and DREX WG
12Luleå University of Technology, Sweden, maxim.smirnov@ltu.se
Institute of Geophysics Czech Academy of Sciences, Prague, Czech Republic
3
Geological Survey of Finland, Espoo, Finland
4
Earth Science Institute Slovak Academy of Sciences, Bratislava, Slovakia

The D-REX (Deposit to Regional scale Exploration) project is based upon the application of the mineral systems concept a potential paradigm shifting approach to mineral deposit identification. Transport, precipitationand concentration of metals into economic mineral deposits involves a combination of processes occurring
over different scales and depths. Regions with similar surficial geologies often have widely disparate metalendowment levels especially in cratonic environments. By applying the mineral systems approach we can elucidate lower crustal structures that may play a key role in the processes responsible for metal endowment in
the upper-most crust.
Mineral deposit formation is often driven by deeply originating large scale fluid transfer (magmatic and/or
metamorphic), which requires; a source region for metal bearing fluids, an energy source for driving fluid
circulation, pathways for the migration of these enriched fluids, a depositional mechanism responsible for theformation of the deposit, and a fluid outflow. The overarching goal of the D-REX project is to identify previously
unrealised metal endowed regions, via application of a new workflow inclusive of large-scale regional data setswith the ability to identify the deep fluid source regions and transport pathways/mechanisms to the upper crust,for targeted exploration and characterisation of the near surface zones of metal deposition and concentration.
3D magnetotellurics can provide information at all scales of the mineral system, as such it is the primary toolof investigation used in D-REX. MT data sets have been collected at three 100x100 km2 regions of economic
potential in the Fennoscandian shield (central Norway, northern Sweden, and central Finland).
D-Rex Working Group (D-REX WG):
Luleå University of Technology (LTU): Maxim Yu. Smirnov, Jirigalatu, Thorkild M. Rasmussen, Tobias Bauer,
Oskar Rydman
Institute of Geophysics Czech Academy of Sciences (IG-CAS): Graham Hill, Kenneth Muhumuza, Svetlana
Kovachikova, Jorge Puente, Nazia Hassan, Nooshin Najafipour
Geological Survey of Finland (GTK): Jochen Kamm, Pankaj Mishra, Cedric Patzer, Uula Autio, Jarkko Jokinen,
Jouni Luukas
Earth Science Institute Slovak Academy of Sciences (ESI-SAS): Jan Vozar , Jozef Madzin, Lenka Ondrasova,
Peter Vajda
Boliden Minerals AB: Tobias Hermansson, Kirsi McGimpsey, Janne Kaukolinna
Boliden Kevitsa Mining OY: Katri Vaittinen
LKAB: Niklas Juhojuntti, Harald Van Den Berg
Geological Survey of Norway (NGU): Sofie Gradmann, Jomar Gellein
Keywords: magnetotellurics, mineral systems, data inversion, Fennoscandia, metal endowment
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Results from the DESMEX semi-airborne EM survey at the Gosetal/Rammelsberg(Harz Mountains, Germany)
A. Thiede, M. Becken, P.O. Kotowski and the DESMEX Working Group
Institute of Geophysics, University of Münster, Germany; athiede@uni-muenster.de

S UMMARY
The nowadays depleted world-class SEDEX Rammelsberg deposit and its adjacent areas are a prime location
to test and demonstrate new geophysical exploration methods. Here, we present preliminary results of a semiairborne EM survey centered at the Gosetal and the Rammelsberg, which was conducted in September 2020
as part of the DESMEX II project. Previous airborne EM exploration surveys, carried out about a decade ago
in that area, revealed a high-conductivity anomaly in the Gosetal, but subsequent drillings could not verify its
existence. Therefore, the results of the airborne EM survey remained enigmatic. Semi-airborne EM has thepotential to image deeper than pure airborne EM and may detect conductivity anomalies from the near surface
to about 1000 m, including a possibly deeper root of the enigmatic Gosetal anomaly.
We installed four electrical dipole transmitters of 2-3 km length and injected an alternating current with a fundamental frequency of 9.26 and 4.63 Hz and covered an area of about 45 km2 with the Helicopter induction coil
DESMEX system. EM Transfer functions were derived in the frequency range up to 4 kHz. Analysis of the datais complicated due to the presence of steep topography and over 500 m altitude differences in the surveyed
area. Synthetic 3D modeling using the unstructured finite element code custEM shows that strong topographic
effects are evident in the collected data and must thus be taken into account in any inversion. We discussthese effects as well as topography correction schemes for flat surface inversion models based on normal fieldcorrections. Ongoing work is focused on comparison of 3D inversion applying the custEM code supporting
complex survey geometry and the 3DINV code using corrected data on a flat surface model.
Keywords: semi-airborne electromagnetics; Rammelsberg mine; electrical resistivity; topography effects

I NTRODUCTION

The Harz Mountains (Germany) are widely known
for its long mining history. One of the outstandingsites in the Upper Harz Mountains is the Rammelsberg mine, a world-class SEDEX (sediment exhalative) deposit highly enriched in Zn- and Pb-sulfides(Large and Walcher, 1999). Nowadays, the mineis fully exploited but past airborne EM surveys revealed a shallow high-conductive anomaly in the adjacent Gosetal. However, subsequent drillings up to
500 m depth could not verify the existence of an ore
body and the Gosetal-anomaly remained enigmatic.
Considering the mining background of this region,
it is perfectly suited as location for a demonstration
survey for novel geophysical exploration methods.
The DESMEX project seeks to develop and establish such methods, including semi-airborne electro-

magnetics (EM), for exploration of deep-seated ore
bodies. Previous DESMEX surveys (Smirnova et al,
2019, 2020) demonstrate the capability of semiairborne EM in flat terrain. The Gosetal area features high altitude differences of more than 500 m
and very steep valleys making the location a challenging environment for EM data evaluation.
The Gosetal survey was conducted in September 2020. Four grounded dipole transmitters (Tx) of
2-3 km length were installed in the survey area (seeFigure 1). We injected a rectangular current waveform with fundamental frequencies at 9.26 Hz (Tx1,
Tx2), 4.63 Hz (Tx3) and 9.6 Hz (Tx4) and a 100 %duty cycle. Flight areas were chosen for each Tx
individually and with an overlapping area at the survey target. Tx were operated sequentially and flightlines are 250 m apart and control lines have 1 km
spacing.

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tal which indicates a conductive anomaly in the valley.

Figure 1: Survey Area: Blue lines show Tx locations(Tx1 to Tx4 from NW to SE), white dotted lines
depict flight lines. Triangles and squares marktelluric and full-MT ground site positions. The
red star shows the position of the Rammelsberg mine.

T HE SEMI - AIRBORNE EM METHOD AND
T RANSFER F UNCTIONSThe semi-airborne EM method combines advantages of purely airborne and purely ground-based
controlled source EM methods. Using a powerfulground-based Tx, the method provides a great penetration depth of up to 1000 m while maintaining thehigh efficiency and spatial resolution of an airborne
system.
The injected current induces a magnetic field consisting of the normal field produced in a homogeneous halfspace and the anomalous field produced by anomalies therein. A linear relation can bestated between the observed magnetic flux density~(B(ω,~r)) and current amperage (I(ω)) recordings~~ ~r)I(ω)B(ω,~r) = B(ω,Figure 2: Amplitude ratio (top) and phase difference (bottom) of normal field responses (Bz )for Tx3 (black line) received from models withand without topography using an electrical resistivity of 500 Ωm. The dashed line gives thecorresponding flight area.

(1)

where ω is the angular frequency and ~r denotes the~ relates both
point in space. The transfer function B
quantities and holds information about the subsurface conductivity structure.
Transfer functions were estimated in the frequency
range up to 4 kHz following the procedure describedby Becken et al (2020). Figure 3 shows amplitudesof transfer function estimates at three different frequencies along a coincident flight line for Tx1 to Tx3.
The amplitudes produced by Tx2 and Tx3 decreaseabruptly at all frequencies when crossing the Gose-

TOPOGRAPHIC E FFECTS AND
P RELIMINARY I NVERSION R ESULTS
The semi-airborne EM data are strongly distorted
by topography (see Figure 2). A comparison of simulated transfer functions of models with and without topography imply distinct differences even aside
from the vicinity of the Tx and zero crossings. Calculated phase differences of > 10° are in the sameorder of magnitude as field variations produced by

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anomalies and thus need to be taken into accountfor inversion.
Only few open source inversion codes can handle semi-airborne EM data and mostly do not allow for complex survey geometry such as in theHarz Mountains. Preliminary 3D inversion results
are shown in Figure 4. We used the finite difference code 3DINV (Grayver et al, 2013) which only
supports flat surface models. To reduce topography effects on the data, we applied a Normal Field~topo ofCorrection that substitutes the normal field B
a homogeneous half space with topography by the~f lat of a half space with a flat surface.
normal field B~inv = B~obs − B~topo + B
~f latB(2)~inv still includes topographic effects
The residual B
but these are expected to be less dominant. We es~topo and B~f lattimated the normal field response of B
using the unstructured finite element code custEM(Rochlitz et al, 2019) for 500 Ωm electrical resistivity.
Corrected transfer functions Binv,z were used as inversion input in a frequency range of 9 to 1000 Hz.
The model box had a size of 9 × 15 × 4.02 km plus
padding cells with a 500 Ωm halfspace as starting
model. The model was rotated to fit the generalstrike direction of 45°. Data with a relative error> 20% and in the vicinity of the Tx were masked.
A RMS of 1.48 was reached after 10 iterations.
Inversion results indicate a 500 to 1000 m deep conductor in the Gosetal slightly dipping to SE direction (A in Figure 4). However, the conductor is nota unique feature as assumed from raw data. Strong
anomalies in the vicinity of the transmitter (B) as wellas conductors following the structure of valleys (C)indicate incomplete topography correction. Other
anomalies (D) seem to be artefacts placed at themodel edges with low data point coverage and low
sensitivity.

C ONCLUSIONS
We conducted a semi-airborne EM survey in themining area Rammelsberg (Harz Mounatins, Germany). Estimated transfer functions indicate thepresence of a conductive anomaly in the adjacent Gosetal. Analysis of the data is complicated,though, by the presence of steep topography thatneeds to be taken into account. Available 3D inversion codes only support flat surface models so wetested a Normal Field Correction to reduce topography effects. Preliminary results using corrected
data show a good conductor in the Gosetal at 500 to1000 m depth but remaining topography effects canlead to artefacts. Ongoing work is focused on 3D inversion using the unstructured finite element code
custEM that was recently extended to perform inversions.

ACKNOWLEDGMENTS
DESMEX Working Group members: Raphael
Rochlitz, Thomas Günther, Mathias Ronczka, Annika Steuer, Olaf Cortes Arroyo, Hauke Petersen,
Pritam Yogeshwar. We would like to thank all students and technical staff for helping with field work,
flight operation and bird assembling. We are grateful
to A. Grayver and R. Streich for providing the 3DINV
code for 3D inversion. This project series (DESMEX
I & II) is funded by the German Federal Ministry for
Education and Research under grant no. 033R130.

R EFERENCES
Becken M, Nittinger CG, Smirnova M, Steuer
A, Martin T, Petersen H, Meyer U, Mörbe
W, Yogeshwar P, Tezkan B, Matzander U,
Friedrichs B, Rochlitz R, Günther T, Schiffler M, Stolz R, the DESMEX Working Group(2020) DESMEX: A novel system development forsemi-airborne electromagnetic exploration. GEOPHYSICS 85(6):E253–E267
Grayver AV, Streich R, Ritter O (2013) Threedimensional parallel distributed inversion of
CSEM data using a direct forward solver. Geophys Journal International 193(3):1432–1446
Large D, Walcher E (1999) The Rammelsberg
massive sulphide Cu-Zn-Pb-Ba-Deposit, Germany: an example of sediment-hosted, massive sulphide mineralisation. Mineralium Deposita
34(5):522–538
Rochlitz R, Skibbe N, Günther T (2019) custEM:
Customizable finite-element simulation of complex controlled-source electromagnetic data.
Geophysics 84(2):F17–F33
Smirnova M, Juhojuntti N, Becken M, Smirnov M(2020) Exploring Kiruna iron ore fields with largescale, semi-airborne, controlled-source electromagnetics. First Break 38(10):35–40
Smirnova MV, Becken M, Nittinger C, Yogeshwar
P, Mörbe W, Rochlitz R, Steuer A, Costabel

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Thiede, A. et al., 2022, Results from the semi-airborne EM survey at the Gosetal

S, Smirnov MY, the DESMEX Working Group(2019) A novel semiairborne frequency-domain
controlled-source
electromagnetic
system:Three-dimensional inversion of semiairborne

data from the flight experiment over an ancientmining area near Schleiz, Germany. GEOPHYSICS 84(5):E281–E292

Figure 3: Amplitude of the Bz magnetic transfer function at Tx1 (light color), Tx2 and Tx3 (dark color). The
lowermost frequency of Tx3 differs from the ones at Tx1 and Tx2 due to different base frequencies.

Figure 4: Horizontal slice (left panel) and cross sections (right panels) of the inversion results using normal fieldcorrected data. The horizontal slice is shown on a topographic map to compare conductivity structuresand mountain shapes. The hatched area and the red star mark the Gosetal and the Rammelsberg mine,
respectively. Black lines show Tx positions.
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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Examination of geomagnetic data as precursors of the September 5, 2018 (MW = 6.6)
and August 20, 2016 (MW = 6.0) earthquakes in Japan
1

2

Hamideh TAHERINIA , Shahrokh POURBEYRANVAND
M.S. student, International Institute of Earthquake Engineering and Seismology, Tehran, Iran,hesar.t.n266@gmail.com
2
Assistant Prof., International Institute of Earthquake Engineering and Seismology, Tehran, Iran,beyranvand@iiees.ac.ir
1

SUMMARY
Magnetic anomalies have been discussed long ago as an earthquake precursor. In this study, thecharacteristic curves are obtained by frequent overplotting of magnetic data per 24-hour time frames on eachother for three geomagnetic stations in Japan (MMB, KAK, KNY). All three components (Z, Y, X) or (Z, D, H)
were processed in one year. In order to increase the intensity and distinction of the anomalies observed
before the earthquake compared to the original data, we tried to eliminate the effect of daily variation of themagnetic field on the geomagnetic records by this method.
Keywords: anomaly, characteristic curve, Earthquake, Japan, magnetic field

INTRODUCTION
Earthquakes are one of the most devastating
natural disasters, and their impact on humansociety, in terms of casualties and economic
damage, has been significant throughout history.
Earthquake prediction can aid in preparing for thismajor event, and its purpose is to identify
earthquake-prone areas and reduce their financial
and human losses. Any parameter that changes
before the earthquake in a way that one can predict
the earthquake with a careful study of its variations
is called a precursor. Recently, more attention has
been
paid
to
geophysical,
geomagnetic,
geoelectrical, and electromagnetic precursors. The
term Tectonomagnetism has been used in thiscontext which involves changes in the magnetic
field associated with an earthquake. The effect ofmagnetic seismicity was investigated by Ricky Taki
(1976) (Meloni et al., 1998). The effect of magnetic
seismicity is quite evident from comparing
simultaneous data of a geomagnetic network (Liuet al., 2006). In the present study, the geomagnetic
data of three stations, obtained through
INTERMAGNET, with a distance of less than 500
km to the September 5. 2018 and August 20, 2016,
Japan earthquakes are investigated.
Then the method of characteristic curves is used toremove the effect of diurnal variation of thegeomagnetic field. After that, by examining themore distinct anomalies after implementing themethod, the cases are matched with the seismic
activities of the region. The pure anomaly can beobserved by separating the noise from the desired
signal. Among the various magnetic components,
the horizontal components are more suitable thanthe others for the proposed process because of

more variations in the geomagnetic field in thevertical direction due to f the geomagnetic gradient.
Each component behaves differently as a functionof the geological and geomagnetic conditions of thestation site. In the present study, one year ofmagnetic data, including three stations and for X, Y,
and Z components in addition to seismic data forJapan, are used to implement this method. The
method is based on plotting different magnetic fieldcomponents in specific time intervals in the same
24 hours frame. This will lead to a plot that shows
the geomagnetic nature of each component of thegeomagnetic field for each station. After averaging
the values for every point at the horizontal axis of
the plot, which is a unit of time depending on thesampling (hourly mean, minute mean, etc.), a curve
will be obtained called the characteristic curve.
Then we reduce the characteristic curve values
from geomagnetic data to reveal the anomalies freeof diurnal variation noise so that the possible
anomalies related to earthquakes will be shown
more distinctly. After drawing the components ofthe magnetic field and removing the daily changes
from each of the components, we can observe theanomalies related to the earthquakes to justify theobserved anomalies better and considering thestandard
deviationforeachcomponent,
pre-seismic anomalies have a more significant
distinction than the original data for being studied
as a seismic precursor.
One of the problems with using this indicator is theambiguity in separating seismic anomalies from thedata (Hayakawa et al. 2007) because the numberof factors affecting the earth's magnetic field islarge and how some of them affect it is notcompletely clear. There is ample practical evidence
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for specific magnetic and electrical oscillations
before an earthquake occurs. Previous studies
have used discrete wavelet transform to process
geomagnetic data to automatically detect thesudden occurrence of magnetic storms (Gamri etal., 2013).
The study of changes in the earth's magnetic field
before, at the same time, and after an earthquake
has continued from the middle of the last century to
the present day, and its related scientificformulations have evolved. Existing theories forinterpreting magnetic anomalies related to seismic
activity can be classified into three theories:
magnetohydrodynamic,
electro-synthetic
andpiezomagnetic. For more information on these
theories, refer to the relevant references (Edwin
and Roberts 1983).
Methods
According to the research in this field in the present
study, this research has been done for Japan using
geomagnetic data from three stations with adistance of less than 500 km. The geomagnetic
stations used in this study are three stations, MMB,KAK and KNY and this information has been
received from the INTERMAGNET site. Based on
0.43M
the Dobrowolski relationship P = 10
(Dobrowolski et al. 1979), it is expected that theprediction phenomena will be observed in a radiusof about 700 km. However, due to the lack of dataand geomagnetic stations in the region, one of thestations in a radius of about 950 km was inevitably
used for this purpose.
Figure (1) shows the location map of the studied
stations, as well as the epicenter of the earthquakeand seismicity of the region from 1900 to 2014.

to 2014 (yellow circles).
In this research, the method of characteristic
curves has been used to eliminate the effect offactors affecting the earth's magnetic field at thelocation of magnetometric stations. Relevant isremoved from the data.
Then, by examining the abnormalities that havebecome clearer after the above steps andeliminating the noise due to daily changes, thesecases are matched with the seismic activities of theregion. Then, by isolating the noise from thedesired signal and finally the observed abnormality,
it is amplified as much as possible to make more
use of the data in question as a precursor to theearthquake.
Among the various magnetic components, the X
component is more suitable for the proposed
process than the Y and Z components. Of course,
other components can also be used depending on
the geological and geomagnetic conditions of thestation. In the present study, to show examples of
the application of this method, the magnetic andseismic data available on the Japan Indicator
Database website have been used. To display thedata, you must first delete the values that havebeen registered as unsuitable data, which in the
program written in PYTHON, these values havebeen replaced with their previous values. After
selecting the appropriate time period using theavailable data from each station, the diagram of
repeating the observed values over 2 hours on
different days shows the desired station's
geomagnetic nature; thus, the characteristic curve
for a period of one year in the station in question.
Comments were made on the magnetic
components X, Y and Z in question. Figure (2)shows the characteristic curve at one of thestations for the Y component of the magnetic fieldof two earthquakes.

Figure 1. Location map of the studied stations(white triangle), as well as the epicenter of twoearthquakes (red star position of the firstearthquake and green star position of the second
earthquake) and seismicity of the region from 1900
th

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Figure 2. The characteristic curve (continuous line)
is drawn at one of the stations for component Y of
the magnetic field of two earthquakes (the first
figure for the earthquake of 05/09/2018 and thesecond figure for the earthquake of 20/08/2016).
The standard deviation dashes are based on 1s
calculations. The horizontal axis represents time(minutes) and the vertical axis represents theamplitude of the magnetic field (nanotesla).
After drawing the magnetic field components andremoving the daily changes from each component,
one can observe the anomalies related toearthquakes better; to justify the observed
anomalies and consider the standard deviation foreach component, which is the definition of eachcomponent. It is shown in Figure (3) for the firstearthquake and in Figure (4) for the second
earthquake.

Due to the fact that an earthquake occurs between
5-15 days after the geomagnetic anomaly, ananomaly is observed in the first earthquake on
26/08/2018, which can be used as an indicator ofan earthquake with a magnitude of 6.6 on
05/09/2018. In the second earthquake, theanomaly of 05/08/2016 can be considered as aprecursor to the earthquake with a magnitude of
0.6 on 20/08/2016 in Japan. Component X is moresuitable for detecting this anomaly and shows abetter anomaly. Component X of the magnetic field
is compared for raw and processed data andrelated anomalies to observe the changes made by
the characteristic curve method and its applicationin geomagnetic prediction studies. The firstearthquake is shown in Figure (4) and the second
earthquake in Figure (5).

Figure 3. The Characteristic curve for one year of
raw data at one station (Figure above) and dataprocessing at the same station (Figure below) forthe magnetic component (X).

Figure 5. Curves of nature related to raw data
(unprocessed, figure above) and data processing
along with the time of the first earthquake for themagnetic component (X) (figure below).

Figure 4. The Characteristic curve for one year of
raw data at one station (Figure above) and dataprocessing at the same station (Figure below) forthe magnetic component (X).

Figure 6. Curves of nature related to raw data
(unprocessed, figure above) and data processing
along with the time of the second earthquake forth

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the magnetic component (X) (figure below).
By comparing these two earthquakes, which are
related to close geographical points and haveapproximately equal magnitudes (6.6 and 0.6), it
was found that the earthquake of 05/09/2018
occurred at a depth of 35 km and the earthquakeon 20/08/2016 Occurred at a depth of 10 km. As
shown in Figure (7), the focal mechanism ofearthquakes is inverse.

REFERENCES
Dobrovolsky, I.P., Zubkov, S.I. & Miachkin, V.I.,
1979.
PAGEOPH (1979) 117: 1025.
doi:10.1007/BF00876083
Edwin, P.
and Roberts, B., 1983. Wave
propagation in a magnetic cylinder, Sol. Phys.,
88, 179.
Ghamry, E., Yumoto, K. and Yayama, H., 2013.
Effect of SC on frequency content ofgeomagnetic data using DWT application: SC
automatic detection. Earth Planets Space, 65,
1007-1015.

Figure 7.
05/09/2018(right).

Focal mechanism of(left) and earthquake

earthquake
20/08/2016

As shown in the diagrams, between these twoearthquakes, the earthquake that occurred at agreater depth has a higher CLVD and has a more
non-tectonic component and has a more specificanomaly.

Hayakawa, M., Httori, K., and Ohta, K., 2007.
Monitoring of ULF (Ultra-Low-Frequency)
Geomagnetic variations associated withearthquakes. Sensors, 7, 1108-1122.
INTERMAGNET (hosted by Natural Resources
Canada,G.
O.
(2018,
June
18).
INTERMAGNET.
Retrievedfrom
http://intermagnet.org/index-eng.php.

CONCLUSIONS
Based on the obtained results and the processing
performed on the magnetic records of threegeomagnetic stations in Japan, abnormalities
related to the change in the magnetic field havebeen identified. Finally, by comparing these two
identified earthquakes, an earthquake that
occurred at a greater depth has a higher CLVD anda more specific anomaly.

Meloni A., Mele G. and Palangio P., 1995,
Tectonomagnetic field observations in central
Italy 1989-1995, Physics of Earth andPlanetary Interiors, vol 105, pp 145-152.
Liu J.Y, Chen C.H, Chen Y.I. and Yen H.Y., (2006,
Seismo-magnetic anomalies and M ≥ 5.0
earthquakes observed in Taiwan during 19982001, Physics and Chemistry of the Earth,
2006, vol. 31, pp 215-222.

ACKNOWLEDGEMENTS
The Intermagnet is appreciated for providing
access to the digital geomagnetic data used in thisstudy.

th

Rikitake, T., 1976. Earthquake Prediction, Elsevier
Scientific Publishing Company.

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Recognition of pre- and co-seismic electromagnetic signals in magnetotelluric
measurements: a case study in the northern region of Algeria
Ahmed Seddik. Kasdi1,2, Mohamed. Hamoudi2, Abderrezak. Bouzid1 and Naïla. Kerbadj1,2
1 Center of Research in Astronomy, Astrophysics and Geophysics, Subsurface Geophysics Division, 16340,

BP 63 route de l'Observatoire Bouzarèah, Algiers, Algeria.
2 University of Science and Technology Houari Boumediene, Department of Geophysics - FSTGAT, BP 32 El

Alia Bab-Ezzouar, 16111, Algiers, Algeria.
SUMMARY
Seismo-electromagnetic signals are anomalous electromagnetic (EM) signals related to seismic activity. In
recent years, the northern part of Algeria has experienced numerous moderate earthquakes. This seismic
activity provided a rare opportunity to study earthquake-related temporal patterns of EM signals using
magnetotelluric (MT) measurements. In this paper, we present an analysis of the MT time series using wavelet
transform for several cases where co-seismic EM signals were observed. We present the precursor signaturesof two main earthquakes using the spectral polarization technique. The results of the wavelet analysis revealed
the time-frequency characteristics of the co-seismic EM signal with a high spectral amplitude at different
frequency ranges. The spectral polarization technique revealed the existence of anomalies related to theearthquake several hours before its occurrence. This anomaly is correlated with the Dst index. We inferredthat the unusual pre-earthquake behavior is related to the precursor signature but not to the solar-terrestrial
effect.
Keywords: Northern Algeria, Magnetotelluric, Earthquakes, Seismo-electromagnetic, Time Series Analysis

INTRODUCTION
Seismo-electromagnetic signals (SES) havebeen reported over a wide frequency range (Hattori
et al. 2006 and references therein). These EM
signals may be precursory (prior to an earthquake)or co-seismic (coinciding with earthquakes). The
observation and analysis of these EM phenomena
is being used for seismic prediction and tounderstand the subsurface physical mechanisms
related to the generation of these EM signals.
Various physical mechanisms have been
proposed to explain the pre-and co-seismic EM
signals, such as piezoelectric effect, piezomagnetic
effect, microfracture electrification, seismic dynamo
effect, and electrokinetic effect (Molchanov andHayakawa 1998; Ogawa and Utada 2000;
Matsushima et al. 2002; Honkura et al. 2002; Huang,
2002; Kasdi et al. 2022). The focal zones are mainly
characterized by mechanical deformations, or
fracturing generates EM signatures during andbefore the earthquake in the ULF band (Dudkin et al.
2010).
Previous studies suggest that EM earthquake
precursors have emerged as a potential candidate
for short-term earthquake prediction (Hayakawa et
al. 2007; Dudkin et al. 2010). In this study, we use
the spectral polarization technique to identify
seismogenic emissions. This method is more
flexible with respect to other methodologies thatexist for obtaining the precursory signatures(Hayakawa and Hobara 2010).
Northern Algeria is in an active seismic zone in
the Mediterranean region. The seismic activity is theresult of the convergence between the two main
Afro-Eurasian plates. Over the last two decades,
northern Algeria has experienced numerous
moderate-to-strong earthquakes (Khelif 2019). The
recent seismic activity in Northern Algeria provides
an opportunity to observe and study SES.

Figure 1. Map of the MT site and the distribution ofearthquake epicenters cited in Tab 1.

Observational Database
The geomagnetic observatory of Medea,
located about 60 km south of Algiers (Algeria), waschosen to conduct a MT experiment. The MT station
was installed in December 2018 (labelled MT-site,
Lat: 36.31° N, Log: 2.72° E). The station is equippedwith an MTU-A System device from PhoenixGeophysics. We measured the five components ofthe EM fields (Ex, Ey, Hx, Hy, and Hz). The sampling
rate of the MTU-A is set to 15 Hz. Table 1
summarizes the origin time, position, magnitude,and epicentral distance (d) of earthquakes that
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occurred near to the MT site where a co-seismic EM
signal was observed in the MT time series. Figure 2
illustrates examples of the observed EM signals
after the occurrence of the 4.4Mb earthquake citedin Table 1, where t=0 indicates the earthquake origin
time. The distinct change in the components of theelectric and magnetic fields a few seconds after theearthquake origin time marks a co-seismic EM
signal related to the seismic events.
Date

Time

Lat °N

Lon °E

Mag (Mb)d (Km)

26/09/2018
29/09/2018
29/09/2018
05/11/2018
12/12/2018
01/01/2019
13/05/2019
19/05/2020
13/08/2020
10/10/2020
23/12/2020
17/01/2021

20:24:35
08:00:09
14:43:37
16:51:56
22:11:33
04:26:12
06:31:57
21:06:03
08:04:48
15:52:48
18:36:52
02:59:56

36.37
36.35
36.48
36.43
36.60
36.42
36.40
36.06
36.42
36.60
36.15
36.17

2.41
2.65
2.52
2.24
3.03
2.47
2.51
2.82
2.75
2.95
3.16
2.83

4.1
3.9
3.5
3.7
3.9
4.1
3.9
3.8
4.4
3.9
4.3
4.1

28.3
08.6
27.3
45.4
44.5
26.2
21.3
27.7
14.9
40.3
43.2
17.2

Table 1. The location of earthquakes occurred near theMT site (from: https://www.emsc-csem.org).

Figure 2. The observed time series data of the fivemagnetotelluric components measured at the MT site
related to the 4.4Mb earthquake.

MT Time Series Analysis Methodology
Wavelet Analysis
Wavelet analysis (Morlet et al. 1982) is a
powerful mathematical method in signal analysis
that allows one to locate the temporal-frequencychange in a time series. The wavelet transforms arefurther available in two forms, i.e., the continuous
wavelet transform (CWT) deals with arbitrarily
selected scales, A discrete wavelet transform (DWT)
deals with only specific scales. CWT is more
appropriate than DWT as it decomposescontinuous-time functions into wavelets, whereas,in DWT, wavelets are discretely sampled (Lui andNajmi 1997).
Consequently, we used CWT to obtain thetime-frequency representation for the MT timeseries. The wavelet spectral power values were

derived in the 0.05–6 Hz frequency range. As an
example, the five component wavelet spectra plotsof the MT field signals components related to the
4.4Mb earthquake are presented in Figure 3.
Spectral Polarization Ratio Analysis
The polarization method is based on themeasurement of the ratio of spectral power of thevertical magnetic field to the horizontal magnetic
fields. This ratio is known to provide us with a lot of
information, whether the observed variation is of
ionospheric origin (or solar-terrestrial effect) or
seismic-related. The polarization ratio analysis, hasbeen demonstrated by prior studies to be effective,
especially in the ULF range (Hayakawa et al. 2007;
Yusof et al. 2019).
The procedures of the spectral polarization
ratio analysis are briefly described. Hourly data is
examined and a Fourier transform is applied to eachhourly window. The method is repeated for theentire duration of the data set. We used a hamming
function for windowing. The FFT transforms timedomain data into the frequency domain in terms ofa power spectral density (PSD). The PSD spectrum
was obtained in the frequency ranges between 0.05
and 1 Hz. The values of the amplitude spectra wereaveraged to attain an hourly mean (𝜇!"#$%&_()*+ ).
The normalization approach was implemented on
the observation of the total duration of the dataconsidered for this analysis to remove cyclic or daily
variations. The standard deviation (𝜎,-._-. ) of thetotal duration of observation along with the mean ofthe data set (𝜇()*+_-. ) calculated for obtaining thenormalized hourly value. The normalized hourly
value (𝑁!"#$,0 ) calculated by using the above
values is as follows;
𝑁!"#$,0 = 𝜇!"#$%&()*+,0 − 𝜇()*+_-.,0 ⁄𝜎,-.!",0 (1)Here, k represents the three different geomagnetic
field components (Hx, Hy, and Hz). ’td’ refers to thetotal duration of data considered for this analysis.
The spectral polarization ratio can be calculatedusing the following formula:5
5
𝑃1/3 = 𝑁!"#$,4# ,*𝑁!"#$,4+ 𝑁!"#$,4$%(2)Here, G indicates the total horizontal fieldcomponents (𝐺 = /𝐻65 + 𝐻&5 ).
The spectral polarization ratio of different magnetic
components shows an anomalous behavior before
the earthquake (Yusof et al. 2019; Vijaya Kumar etal. 2021).
The spectral polarization technique has been
implemented on the magnetic time series for twoearthquakes, 4.4Mb and 4.3Mb mentioned in Table
1, respectively. The magnetic data is analyzed forearthquake precursors in the frequency band 0.05–
1 Hz. The magnetic data was analyzed to determine
the temporal variations of PZ/G.

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Geomagnetic index
In order to eliminate the effects of global
geomagnetic activity and avoid false precursory
signals, this study used a global geomagnetic index,
namely the disturbance storm time (Dst) index,
recorded during the same observation period. The
Dst index is used to understand the difference
between quiet and disturbed geomagnetic
ambiance (Borovsky and Shprits 2017). The Dst
index is derived on a one-hour basis from the H
component
measuredbygeomagnetic
observatories at the middle to low latitudes. The Dst
index was taken from the World Data Centre (Noseet al. 2015). A Dst index of less than -30 nT indicatessolar activity known as a geomagnetic storm(Gonzalez et al. 1994).
Results and Discussion
The time-frequency representation obtained
from the CWT supports the existence of co-seismic
EM signals related to the seismic events. The
analyzed MT components exhibit spectral
enhancement in the frequency range of 2 Hz to highfrequency (6 Hz in our case) for all of theearthquakes listed in table 1. The enhancement in
the spectral content starts after a few seconds (8 to
15 seconds) of the onset of the mainshock of theearthquake. The increase in the spectral content
after the mainshock depends on the epicenter
distance as well as the magnitude of the earthquake.
Figure 4 demonstrates the hourly polarization
ratio PZ/G (1st panel) and Dst index (2nd panel) from10/08/2020 to 15/08/2020 (Fig. 4a) and 18/12/2020
to 24/12/2020 (Fig. 4b). The two earthquakes that
occurred on 13/08/2020 (Mb = 4.4) and 23/12/2020(Mb = 4.3) are represented by the vertical blue line.
The threshold value defined µp±2σp is consistentthroughout the recorded duration. An anomalous
peak appeared in the PZ/G panel before 10 and 32 hof the occurrence of the Mb = 4.4 and Mb = 4.3
earthquakes, respectively. We correlated these two
peaks with the Dst index, suggesting that a value
greater than -30 nT defines non-geomagnetic
activity during this time. From these results, it issuggested that the anomalous peaks should beconsidered as precursors of the two earthquakes.
Conclusions
The installation of a MT station in the northernpart of Algeria provided the opportunity to study
SES. Co-seismic EM signals were observed during
several earthquakes. Wavelet analysis hasrevealed the time-frequency content of the coseismic EM signals observed simultaneously in theelectric and magnetic field components. In thepresent work, we try to explain the precursor
signatures using the spectral polarization technique.
It is observed, for the frequency band 0.05–1Hz, asignificant increase in the value of the polarization
ratio several hours before the occurrence ofearthquakes. After comparing the precursor resultsof each earthquake with the Dst index, we deduced
that the unusual behavior before the earthquake isrelated to the precursor signatures.
These results will help in understanding the
SES associated with earthquake activities in the
North Algerian region. This kind of analysis should
be carried out for many earthquakes to understandthe seismo-electromagnetic phenomena.
Acknowledgements
This research was conducted with the support of the
Research Center of Astronomy, Astrophysics andGeophysics (CRAAG, Algeria). Data collection was
carried out using Phoenix Geophysics MTU-A
system devices belonging to the Geophysics
Laboratory (USTHB/Algiers). We thank the
Geomagnetic Observatories (Kakioka [JMA],
Honolulu and San Juan [USGS], Hermanus [RSA],INTERMAGNET, and many others for theircooperation to make the final Dst index available.
References
Borovsky J E, & Shprits YY (2017) Is the Dst index
sufficient to define all geo-space storms? Journalof Geophysical Research: Space Physics,
122(11), 11-543
Dudkin F, Rawat G, Arora BR, Korepanov V,
Leontyeva O, Sharma AK (2010) Application ofpolarization ellipse technique for analysis of ULF
magnetic fields from two distant stations in
Koyna–Warna seism-oactive region, West India.
Nat Hazards Earth Syst Sci, 10:1513
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Hayakawa M, Hattori K, Ohta K (2007) Monitoringof ULF (ultra-low-frequency) geomagnetic
variations associated with earthquakes. Sensors
7:1108–1122
Hayakawa M, Hobara Y (2010) Current status ofseismo-electromagnetics
forshort-term
earthquake prediction. Geomat Nat Hazards
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Honkura Y, Matsushima M, Oshiman N, Baris S, Ito
A, Iio Y, Isikara AM (2002) Small electric andmagnetic signals observed before the arrival ofseismic wave. Earth Planets Space 54(12):e9–e12

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Kasdi, A.S. et al., 2022, Recognition of pre- and co-seismic EM signals in MT measurements, North Algeria

Huang Q (2002) One possible generation
mechanism of co-seismic electric signals. Proc
Jpn Acad Ser B 78(7):173–178
Kasdi AS, Bouzid A and Hamoudi M (2022)
Electromagnetic Signal Associated with Seismic
Waves: Case Study in the North Central Algeria
Area. Pure and Applied Geophysics, pp.1-15
Khelif MFA, (2019) La sismicité récente de la région
algéroise. Phd Thesis. Universite des Sciences
et de la Technologie Houari Boumediene(USTHB, Algeria), (in French)Lui ATY, & Najmi AH (1997) Time‐frequency
decomposition of signals in a current disruption
event. Geophysical research letters, 24(24),3157-3160
Matsushima M, Honkura Y, Oshiman N, Baris S, etal. (2002) Seismo-electromagnetic effectassociated with the Izmit earthquake and its
aftershocks. Bull Seismol Soc Am 92:350–360
Molchanov OA, Hayakawa M (1998) On thegeneration mechanism of ULF seismogenic
electromagnetic emissions. Phys Earth Planet
Inter 105:201–210
Morlet J, Arens G, Fourgeau E, & Glard D (1982)

Wave propagation and sampling theory—Part I & II:
Complex signal and scattering in multilayered
media. Geophysics, 47(2), 203-221
Nose M, Iyemori T, Sugiura M, & Kamei T (2015)
World Data Center for Geomagnetism. KyotoGeomagnetic Dst index
Ogawa T, Utada H (2000) Coseismic piezoelectriceffects due to a dislocation: 1. An analytic far andearly-time field solution in a homogeneous whole
space. Phys Earth Planet Inter 121(3–4):273–
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Vijaya Kumar, PV, Rawat, VS, Patro, PK, Gupta, AK
and Babu, N (2021) Assessment and recognitionofpre-andco-seismic
electromagnetic
signatures from magnetotelluric data: a casestudy from Koyna–Warna seismo-active region,
India. Acta Geophysica, 69(1), pp.1-15
Yusof KA, Abdul Hamid NS, Abdullah M, Ahadi S
and Yoshikawa A (2019) Assessment of signal
processing methods for geomagnetic precursorof the 2012 M6. 9 Visayas, Philippines
earthquake. Acta Geophysica, 67(5), pp.12971306

Figure 3. The wavelet power spectrum for the five magnetotelluric components time series data. The time t = 0 is theearthquake origin time. The used wavelet is the Morlet waveletab

Figure 4. The temporal variation of the hourly spectral polarization of the component PZ/G (1st panel) and Dst index (2ndpanel), (a) from August 10, 2020, to August 15, 2020, and (b) from December 18, 2020, to December 24, 2020. The
vertical blue line indicates the earthquake origin time (August 13, 2020, Mb = 4.4 and December 23, 2020, Mb = 4.3 for aand b, respectively). The red dashed lines represent the threshold value (µp+2σp) for Δf=0.05–1 Hz.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Investigation of Earthquake Swarm and Buried Geothermal Resources By
Magnetotelluric, Gravity Modeling and Seismological Analyses of Upper Crust
Structure of Yalova-Termal Region
E. Pekşen*,1, D. Çaka2, B. Tunç3, B. Oruç4, E. Budakoğlu5, T. Türkmen6, F. Sevim7, D. Durdağ8, K.
Zengin9, M. E. Erkan10, G. Durdağ11, Ş. Barış12
1Kocaeli University, ertanpeksen@kocaeli.edu.tr, 2Kocaeli University, caka@kocaeli.edu.tr, 3Kocaeli
University, berna@kocaeli.edu.tr, 4Kocaeli University, bulent.oruc@kocaeli.edu.tr, 5Sakarya University,
ebudakoglu@sakarya.edu.tr, 6AFAD-Yalova, taylanturkmen@gmail.com, 7TÜBİTAK-MAM Earth and Marine
Sciences Research Group, fatih.sevim@tubitak.gov.tr, 8Kocaeli University, dogukan.durdag@kocaeli.edu.tr,
9Kocaeli University, zenginkaderrr@gmail.com, 10Kocaeli University, eezgibabir@gmail.com, 11Kocaeli
University, gamzeayhandurdag@gmail.com, 12Kocaeli University, sbaris@kocaeli.edu.tr

SUMMARY
In this study, velocity, resistivity, and density structure of Termal district and its surroundings were investigated.
The seismic activity of the region was monitored for three years. During this period, 4792 earthquake data
were read. 3D tomography of the region was obtained by performing the LOTOS program. Thus, the lateraland vertical distributions of seismic wave velocities were obtained from local earthquake tomography studies
as well as Vp / Vs ratios. Resistivity distributions in three dimensions from 39 MT and TEM data were obtainedin the vicinity of Termal district. As for the gravity method, the topography of basement in three dimensions
was obtained up to 1.1 km using gravity data measured at 268 points. Density distributions of thecorresponding area were estimated by an inversion method with the gravity data. Although the resolution of
the three different geophysical methods performed in the study area varies, their results support each other.
Keywords: Local Earthquake Tomography, Magnetotelluric, Gravity, Geothermal, Earthquake

INTRODUCTION
The Armutlu Peninsula has very active seismicity.
The study area and its territory has been monitoredby an earthquake network station (Armutlu
Earthquake Network Stations-ARNET) since 2005.
The most important and highest of these activities
as took place in August 2014. After this event, theearthquake activity of the Termal district and its
vicinity for 3 years was monitored using the ARNET
by adding 8 temporary stations to the existing
network more detail. The places where the station
to be established were determined specifically. To
do this, the measurements were taken in theselocations whether the selected places were suitable
for the earthquake station by analyzing themeasured data. After locations of the temporary
station were determined, the installation ofearthquake stations was completed.
Beside earthquake data and tomography study,
three different geophysical methods applied in the
study area. These were Transient electromagnetic(TEM), magnetotelluric (MT) and gravity methods.
Standard MT (EDI) files were created by analyzing
the data of MT measurements. The static shift effectin the MT measurements was eliminated by using
TEM measurements (Pellerin and Hohmann, 1990).
Consequently, 3D MT data (impedances) was
performed using ModEM software (Egbert and

Kelbert, 2012; Kelbert et al., 2014). Gravity data
were measured in the corresponding area andBouguer anomaly map was obtained as well. 3Dgravity inversion (Oldenburg, 1974) and 2D gravity
density inversion (Constable et al., 1987) wereachieved using gravity data. Earthquake data werecollected and read from stations periodically. Using
earthquake data, tomography results were obtained(Koulakov, 2009). By using all these methods andresults, comments were suggested about theregion. Alteration zones, fault and dyke systems,
and intrusive intrusions associated with possible
heat sources were modeled depending on thedensity variations.
GEOLOGICAL SETTING
The geological map of the study area is shown in
Figure 1. Neotectonic features of the district ofTermal and its surroundings was studied by Yiğitbaş
et al. (2006). Thermal hot water is on the border ofGemiciköy formation and Kızderbent Volcanite inTermal. The boundaries of our study area are
generally located in these two units according to thegeological map of the Armutlu Peninsula. Our study
area consists of Eocene younger units and Eocene
older units. Eocene aged rocks are andesitic andbasaltic volcanics (Akbayram, 2011; Genç et al,2004).

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195Pekşen, E. et al., 2022, Seismic Tomograpy, Magnetotelluric and Gravity Methods in Termalvariation.
METHODS
CONCLUSIONSIn this study, seismic tomography, MT, and gravity
methods were applied to examine the earthquake
activities, to understand the location of faults andthe boundaries of the hot water reservoir in theregion.
Seismic Tomography
To obtain the tomography result of the study area,
5228 earthquakes with Ml magnitudes ranging from
0.1 to 4.9 were utilized. The data set contains a totalof 92642 phase, 49232 P and 43410 S readings.
The local earthquake tomography study was carriedout using the LOTOS algorithm (Koulakov, 2009).
Thus, the velocity structure of the study area was
obtained. Figure 2(a) shows the variation of Vp
perturbations at 1 km depth. The variation of Vs
perturbations is illustrated in Figure 2(b). Figures
2(a) and 2(b) are superposed on a digital elevation
map of the study area and faults with all together.
Magnetotellurics
MT measurements were acquired at 39 locations.
MT locations are shown in Figure 1 with orange
squares. Four set of Metronix equipment were
utilized to measure MT data with one of theequipment fixed at a reference point. After collecting
the data, the data processing was applied to get EDI
files. The inverse solution was applied to themeasured TEM data. TEM result was applied tostatic shift correction. Thus, the corrected MT data
was obtained. 3D MT inversion was achieved using
ModEM program with the corrected MT data set.
The inversion of 3D MT data are illustrated at 1 km
depth in Figure 2(c). One can distinguished thedifference between the scanned area by
tomography and MT methods from Figure 2(a), (b)and Figure(c). The reason for this is the distributionof the earthquake and the MT stations.
Gravity
The location of the gravity measurement points isshown in Figure 1 with white circles. The gravity
data were measured at 268 points. The gravity datarequires some basic data processing as latitude,
free-air, Bouguer, terrain corrections to obtain
Bouguer anomaly map of the study area. The low
pass filter was also applied to the data to get rid ofsome noise from the measurement. The baseundulation at 1.1 km depth was estimated by 3D
inversion algorithm. 3D inversion was developed byOldenburg (1974). Besides the 3D inversion ofgravity data, 2D inversion of some of profiles wereinverted by Occam inversion to estimate density

Seismic Vp and Vs velocities were obtained by local
earthquake tomography. MT resistivity volume ofthe study area was achieved by a 3D MT inversion
program. 3D undulation depth of the base wasestimated by a 3D gravity program. The seismic
velocity structure of the region was revealed indetail with the local earthquake tomography study.
Although the Vp/Vs distributions were expected athigh values due to the geothermal fluid, low Vp/Vs
ratios were obtained, especially at a depth ofapproximately 1 km. We also observed that theearthquake focal depths were concentrated inregions where the Vp/Vs ratios change sharply
laterally. The electrical resistivity sections modeledfrom the MT data provided a solution up to a depthof 3.5 km, where the geothermal resources thatcause changes in the local earthquake tomography
velocity sections. Gravity anomalies provided
solutions for revealing shallow lineaments of the
project area and distribution of density differences.
Using spectral analysis of Bouguer anomalies, thedepth of the metamorphic basement rock to themedium was estimated as 1.15 km. Further, thegeometric structure of the basement rock, modeledby the Parker-Oldenburg algorithm, varies between
0.75-1.5 km. This rapid changes in a local area
reveals the importance of tectonic forces in the
structural deformation of the basement rock andmagma activity feeding deep geothermal processes.
We also suggest that the aquifer of Termal cityneeds to be characterized more details. The drilling
activities around the study should be controlled by
the local authority.
ACKNOWLEDGEMENTS
This study was supported by the Scientific andTechnical Research Council of Turkey (TÜBİTAK)under grant 117Y184. We would like to thank to PhD.
Cemal Kaya for MT data processing. Unfortunately,
PhD. Cemal Kaya passed away very sadly. He hada very helpful personality. We will never forget him
with his very good memories.
REFERENCES
Akbayram K (2011). İstanbul ile Sakarya Zonları
Arasındaki Potid-İçi Kenedinin Armutlu
Yarımadası Doğusu’ndaki Evrimi. PhD thesis,İstanbul Technical University, İstanbul.
Constable SC, Parker RL, Constable CG (1987).
Occam’s inversion: A practical algorithm forgenerating smooth models fromelectromagnetic sounding
data. Geophysics, 52, 289-300.

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Pekşen, E. et al., 2022, Seismic Tomograpy, Magnetotelluric and Gravity Methods in Termal

Genç C, İşseven T, Keskin M, Tüysüz O (2004).
Armutlu Yarımadası (KB) Eosen manyetik
kayalarının petrolojik evrimi ve
paleomanyetizma özelliklerinin araştırılması:TÜBİTAK Project No: 102Y032.
Egbert GD, Kelbert A (2012). Computational
recipes for electromagnetic inverse
problems. Geophysical JournalInternational, 189(1), 251-267.
Kelbert A, Meqbel N, Egbert, GD, Tandon K (2014).
ModEM: A modular system for inversion ofelectromagnetic geophysical data. Computers &Geosciences, 66, 40-53.
Koulakov I (2009). LOTOS code for local
earthquake tomographic inversion. Benchmarks
for testing tomographic algorithms. Bulletin of
the Seismological Society of America, 99, 194214.
Pellerin L, Hohmann, GW (1990). Transient
electromagnetic inversion: A remedy formagnetotelluric static shifts. Geophysics, 55(9),1242-1250.

Oldenburg DW (1974). The inversion andinterpretation of gravity
anomalies. Geophysics, 39, 526-536.
Yiğitbaş E, Bozcu M, Aylan E, Gürer ÖF, Yılmaz K,(2006). Armutlu Yarımadası’nın Yalova
Batısında kalan kesiminin morfotektonik-jeolojik
niteliklerinin incelenmesi: TÜBİTAK Project
No:101Y068.
Web of General Directorate of Mineral Researchand Exploration.
http://yerbilimleri.mta.gov.tr/home.aspx
Accessed 29 July 2022

Figure 1. The geological map of the study area (Web of General Directorate of Mineral Research andExploration). Faults are compiled by Yiğitbaş et al. (2006).

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Pekşen, E. et al., 2022, Seismic Tomograpy, Magnetotelluric and Gravity Methods in Termal

(a)

(b)

(c)

(d)

Figure 2 (a) Vp perturbations, (b) Vs perturbations, (c) MT resistivity variations, (d) Topography of basement
at 1 km depth based on gravity data.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Assessment of geoelctric field variability in Yenisei-Khatanga oil and gas provinceand space weather hazards for infrastructure
Е. Yu. Sokolova 1, 2, Е. E. Marshalko 3, O. V. Kozyreva 2, I. S. Kupriyanov 1, D. V. Epishkin 4,
G. E. Slinchuk 4, 5, D. V. Yakovlev 4
1 All-Russian Research Institute of Oil Geology, Moscow, Russia, sokol_l@mail.ru
2 Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, Moscow, Russia,
kozyreva@ifz.ru
3 Finnish Meteorological Institute, Helsinki, Finland, elena.e.marshalko@gmail.com
4 Nord West Ltd, Moscow, Russia, dmitri_epishkin@mail.ru
5 Moscow State University, Moscow, Russia, slin4ukgr@yandex.ru

SUMMARY
The geoelectric (telluric) field variability caused by the Earth’s magnetic field disturbances during space
weather anomalies is analyzed for the area of Yenisei-Khatanga regional through (YKRT) - a perspective oiland gas-bearing province located in Siberian Arctic within the auroral zone. For the analysis of telluric fields
and estimation of their extreme values, we use a unique experimental magnetotelluric impedance tensor
database collected by Nord West Ltd during the regional stage of the geophysical investigations of the troughand adjacent territories. The geoelectric field spatial-frequency distributions on the Earth’s surface arecalculated via the impedances and harmonic approximation of external geomagnetic excitation. The
correlation of obtained distributions with geological map and MT array data helps to explain the position of theareas with maximal geoelectric field distortion accounting for their geological\geoelectrical heterogeneities.
Variations in time of telluric fields at a subset of YKRT MT sounding sites are synthesized via the impedance
dependences on frequency and magnetic field time series recorded during intensive geomagnetic events atthe nearest stationary monitoring sites. The snap-shoots of telluric field vectors’ spatial distribution arepresented for the characteristic time steps of the geomagnetic variations considered, specific features ofthese distributions are outlined. As geoelectric responses to rapid changes of external magnetic field are thedrivers of geomagnetically induced currents (the most serious threat to the grounded industrial constructions
in subpolar regions) the results of the study can be used to mitigate possible damaging space weather effects
on operating and projected YKRT infrastructure facilities.
Keywords: geoelectric fields, geomagnetically induced currents, crustal heterogeneities, Yenisei-Khatanga
regional trough and oil-gas-bearing province, auroral zone_____________________________________________________________________________________INTRODUCTION
The theoretical and practical value of studying the
Earth surface manifestations of space weather
events significantly increased in the 21st century
marked by the rapid economic development ofhigh-latitude regions of Eurasia and North America.
In the central sector of the Russian Arctic, of toppriority are geomagnetically-induced currents’(GICs) problems in the oil and gas infrastructure.
This region offers experimental opportunities forsuch studies: a network of stationary magnetic
observatories is being restored, large-scale arraysof magnetotelluric (MT) surveys are being collectedin the frames of the hydrocarbon prospecting.

The geoelectric responses to the Earth’s magnetic
field rapid disturbances are drivers for mostintensive GICs in earthen industrial constructions.
So the analysis of geoelectric (telluric, E-) field
variability for the territory of Yenisei-Khatanga oil
and gas province, located within the highest levelgeomagnetic activity auroral zone (Fig. 1, 2), hasclear relevance for studying possible negative
space weather effects on the infrastructure
facilities.
We present the results of such an analysis using
the experimental data on 3D Earth conductivity andcarried out this way for the first time in YKRT area.
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Discussion of the Results

Figure 1. A case of NOAA Space Weather
Prediction Center Aurora forecast (for 2022-07-01
19:41, UTC, NOAA Homepage). Red box marks
the area of Yenisei-Khatanga regional trough(similar oil and gas province) on Taimyr peninsular.
Data And the Analysis Approaches
The study is based on unique collection of highquality broad-band MT impedance estimates formore than 650 (xi,yi) sounding sites assembled for
YKRT by Nord West Ltd (Fig. 2, Slinchuk et al.,2022) during the regional phase of Taimyr
peninsular geophysical investigations and applies
two approaches to calculate geoelectric responses:
in frequency and time domains, similar to (Lukas etal., 2018; Sokolova et al., 2019) .
The geoelectric field spatial-frequency distribution
on the Earth’s surface is calculated via all theensemble of experimental Z tensors on the basis ofclassical dependence of telluric (E) and magnetic(B) fields with harmonic approximation of theplane-wave external magnetic excitation (Fig. 3).
Variation in time of telluric fields at a subset of
YKRT MT sites are synthesized also in planarparadigm
via
the
impedance
frequency
dependences and real time series of magnetic fieldon the base of stable forward and inverse Fouriertransform routines. For calculations GIS INTEGRO(Cheremisina et al., 2022) are also used. Speciallyselected magnetic data were recorded during
intensive space weather events at the nearest
stations of the Russian Arctic geomagnetic
monitoring network (Kozyreva et al., 2022) (Fig. 2).
The variation of the voltage in pipelines are
estimated as simple line integral of vector E(xi,yi,tj)
estimates over pipe geometry for a set of fixed time
steps tj.

The variability of the geoeletric fields on the area of
Yenisei-Khatanga trough are represented in the
study results by spatial distributions of theamplitudes of horizontal electric field E(хi,уi,Т),
induced by the homogeneous harmonic 1 nT
geomagnetic field Bh(Т) oriented geographicallyN-S and E-W for number of characteristic periods(Fig. 3). The temporal variability is describedthrough E(xi,yi,t) variations with time, synthesizedon magnetic storm or sub-storm records in NOK
and DIK for some YKRT sites(xi,yi) representin
different geoeletric structures. Also snap-shoots oftelluric field E(xi,yi,t) vector spatial distribution arepresented for the characteristic time steps tj of thegeomagnetic variations considered.
YKRT stretches for over 1,000 km and has nearly
20 km-thick MZ sedimentary cover (electricalresistivity of 5-20 Ohm·m) with several basement
uplifts geophysically established within the troughbed. It is surrounded by East Siberian Platform in
the South and the Taimyr orogen in the North,which are composed of Paleozoic and older
consolidated sedimentary\metamorphic complexes(100-400 Ohm·m) partially, penetrated by evenmore resistive intrusive bodies and broken by faults(Fig. 2). Spatial distributions over the YKRT area ofeffective apparent resistivity estimated on regional
impedance collection reflect these geoelectricalcontrasts at corresponding skin depths. The
correlation of obtained E-field variability maps
(amplitude for specific frequencies) and vector
snap-shoots (for specific time steps of magnetic
storms) with geological and resistivity data helps to
explain the significant features of geoelectric
distortions’field
asproducts
of3D
geological\geoelectrical heterogeneities.
Both maps of spatial geoelectric responses andtime series of synthetic E-fields provide the ideas ofpossible extreme values/directions of geoelectricvariations on the studied area, which arecorrespondent with but give much more details
then similar small scale maps constructed recentlyin (Kozyreva et al. 2022) for central segment of
Russian Arctic on the base of global 3D
conductivity model (Alekseev et al., 2015).
The geo-induced voltage across operating andprojecting pipelines in the region, calculated onsynthetic geoelectric field time series, can exceed
the regular levels of pipeline cathodic protection.
Conslusion
Our study describes the spatial and temporalvariability of geoelectric field on the territory ofperspective Yenisei-Khatanga oil and gas province.
This component of the Earth’s electromagnetic
sphere is the main prerequisite for calculations of
GICs in grounded technological systems,
particularly – in pipelines. The following inferences
seems to be useful for mitigation of possible

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destructive impacts of space weather on operating
and future YKRP infrastructure facilities.
 For an adequate assessment of these risks it isnecessary to carefully analyze the geoelectric
structure of the area where a facility to beconstructed. Heterogeneities in the distributionof electrical conductivity of the Earth’s crust are
the major factor for the GIC patterns, bydamping or, in opposite, significantly amplifying
amplitudes of the currents and modulating thespectral composition. The most sever GICs areforeseen in the frames of YKRT, where
perspective for hydrocarbons, resistive andhighly heterogeneous in geoelecrtic properties
Paleozoic formations are spread. The maps
describing variability of regional geoelectricfields can serve as space weather hazard
maps for subpolar oil and gas provinces.
 The estimates of vectors of the horizontal
geoelectric field E(x,y,T), induced by the
harmonic oscillations Bh(Т), vary greatly inmagnitude and direction. At frequencies thatcarry most of the disturbed field's energy, they
have the maximum amplitudes in the range
from <1 to ~50-100 mV/km at a unit amplitude
of external magnetic field variations. Thus,
even with moderate amplitudes of geomagnetic
variations during substorms (300-500 nT), theirgeoelectric responses in the area of Pz
formations can reach 15 V/km and more. As
synthesized E-field time series reveal, duringconsidered real mild magnetic storm (Dst ~-60nT) telluric field variations for several
geoelectrical structures of the YKRT might
have exceeded 5-7 V/km.
 Voltage variations estimated using as input datasynthetic time series of the geoelectric field in
sites
along
projected
oroperating(Messoyaha-Norilsk) pipelines might be rearded
as threshold values’ objective porxi for cathodic
protection scheme design/optimization for
mitigation of severe spase wether impacts.
ACKNOWLEDGEMENTS
The studies were carried out as part of state
assignments of All-Russian Research Institute of
Oil Geology and Schmidt Institute of Physics of the

Earth, Russian Academy of Sciences as well as bygeophysical company Nord West Ltd and grant
314670 of the Academy of Finland.
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Alekseev D., Kuvshinov A., Palshin N. (2015)
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Cheremisina E. N., Finkelstein M. Ya., Spiridonov
V.A., Lubimova A.V. (2022) Chapter 6.5 - Newcomputer technology to solve geologicalproblems for prospecting and exploration ofmineral resources. In: Moitra A.K., Kayal J.R. etal. (ed) Innovative Exploration Methods for
Minerals, Oil,Gas, and Groundwater for
Sustainable Development. Elsevier, Kolkata, p.
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Sokolova E.Yu., Dobrovolsky M.N. (2022)
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Slinchuk, G.Ye.Е., Yakovlev, D.V., Yakovlev, A.G.,
Andreev, M.A. and Shirokova, Ye.P. (2022) A
depth three-dimensional geoelectrical model of
Taimyr. Physics of the Solid Earth, 2022, No. 5,
Accepted.
Sokolova, Е.Yu., Kozyreva, O.V., Pilipenko, V.A.,
Sakharov, Ya.A. and Epishkin, D.V. (2019)Space-Weather-Driven Geomagnetic- andTelluric-Field Variability in Northwestern Russia
in Correlation with Geoelectrical Structure andCurrents Induced in Electric-Power Grids.
Geophysical Processes and Biosphere, No. 18,
Iss. 4: pp. 66–85. DOI: 10.21455/GPB2019.4-7

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Sokolova E., et al., 2022, Assessment of geoelctric fields in Yenisei-Khatanga oil and gas province …

Figure 2. Yenisei-Khatanga oil and gas province MT sounding sites (Slinchuk et al., 2022) (purple squares
inside orange contour line) and the nearest magnetic stations (DIK- Dikson, NOK- Norilsk, yellow stars) on the
background of the regional geological map on Northern margins of East Siberian platform and distribution ofcorresponding effective apparent resistivity estimates (for T=100c), according to the color palette (in Om·m).
Blue dotted line – operating Messoyakha-Norilsk gas pipe. Transparent blue arrows show a schematic
position of western and eastern electrojets at local midnight on the meridian 100°E.

Figure 3. Spatial distribution on the Yenisei-Khatanga trough of the experimental impedance data-based
vectors of the horizontal geoelectric field E(х,у,Т), induced by the homogeneous harmonic geomagnetic field
Bh(Т) with an amplitude of 1 nT and oriented geographically N-S, for the periods T=10 s (red), 100 s (black),
and 1000 s (green). The scale bars show 20mV/km length of geoelectric field vectors.
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Induction Responses from Magnetotelluric Transfer Functions in Southland, New Zealand
K. Pratscher1, M. Ingham2 , W. Heise3, T. Bertrand4, D. Mac Manus5, C. Rodger6, M. Dalzell7, T. Petersen8
1
Victoria University of Wellington, Wellington, New Zealand, Kristin.Pratscher@vuw.ac.nz
2
Victoria University of Wellington, Wellington, New Zealand, Malcolm.Ingham@vuw.ac.nz
3
GNS Science, Lower Hutt, New Zealand , W.Heise@gns.cri.nz
4
GNS Science, Lower Hutt, New Zealand , T.Bertrand@gns.cri.nz
5
University of Otago, Dunedin, New Zealand, craig.rodger@otago.ac.nz
6
University of Otago, Dunedin, New Zealand, macda381@student.otago.ac.nz
7Transpower New Zealand, Wellington New Zealand, michael.dalzell@transpower.co.nz
8
GNS Science, Lower Hutt, New Zealand , T.Petersen@gns.cri.nz

SUMMARY
The impact of geomagnetically induced currents (GIC) on the New Zealand power grid has previously beenstudied using a thin-sheet model of electrical conductance variations across the country. Due to its relative
proximity to the auroral zone observed GIC are greatest in the south of the South Island in the
Otago/Southland region. Recent acquisition of 62 long period magnetotelluric (MT) sites in this region
provides the opportunity to better understand how GIC are related to the conductivity structure and tectonics.
MT 1Hz impedance data in the period range between 10 and 10000 seconds have been combined with 1s
resolution magnetic data to calculate the induced electric fields during the 2015 St. Patrick’s Day Storm.
Direct current measurements from sensors installed along the transmission network are compared with theinduced electric fields to analyze the GIC and phases of the storm in detail.

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Cumbre Vieja volcanic eruption (La Palma, Canary Islands): Magnetotelluric
monitoring experiment
1

2

3,4

1

3

3,4

3,4

P.Piña-Varas , J. Ledo , D. Martínez Van Dorth , P. Queralt , I. Cabrera Pérez , L. D’Auria , N. Pérez
1

Department Dinàmica de la Terra i de l'Oceà, Universitat de Barcelona, Barcelona, Spain. p.pina@ub.edu.
Physics of the Earth and Astrophysics, Faculty of Physics, Universidad Complutense de Madrid, Spain
3
Instituto Tecnológico y de Energías Renovables (ITER), 38600 Granadilla de Abona, Tenerife, CanaryIslands, Spain
4
Instituto Volcanológico de Canarias (INVOLCAN), 38320 San Cristóbal de La Laguna, Tenerife, CanaryIslands, Spain
2

SUMMARYthth

The last volcanic eruption in the Canary Islands took place in La Palma (September 19 - December 14 ,2021). For over 90 days the so-called Cumbre Vieja volcano was active, the longest volcanic eruption everregistered in this island, which represented a unique opportunity to assess the electrical resistivity changes
related to this type of activity. On the one hand, the presence of magma generates strong resistivity contrasts
with the hosting units, since magmas contain dissolved water in their composition that reduces its resistivity.
On the other hand, the whole hydrothermal system which was characterised by the 3-D resistivity model of La
Palma Island performed in 2019 will be most likely modified by the strong changes associated to this new
geodynamic context. Under these circumstances, several experiments have been performed in the island
since the beginning of the volcanic eruption, in order to understand the changes of the electrical resistivity
associated to the magma intrusion, as well as the potential of the magnetotelluric method for the volcanic
monitoring. Here, we present some preliminary results of this monitoring experiment, where importantchanges on the electrical properties of the subsoil have been detected and correlated to other information
available, such as the seismicity. Additionally, the tests conducted against the 3-D resistivity model of theisland (baseline model performed in 2019) help to understand the meaning and implications of such changes.
These results remain preliminary, but they are definitely encouraging, as demonstrate the potential of themagnetotelluric method for the volcanic monitoring activities.

Keywords: Magnetotelluric 3D, volcanic monitoring, Canary Islands.

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Geothermal exploration via magnetotelluric surveys in non-volcanic geothermal
fields in northern Thailand
1

P. Amatyakul1, T. Rung-arunwan2, C. Vachiratienchai3 and W. Siripunvaraporn
Department of Physics, Faculty of Science, Mahidol University, Thailand, puwis.ama@mahidol.ac.th2Curl-E Geophysics Co., Ltd., Thailand, t.rungarunwan@gmail.com
3
Curl-E Geophysics Co., Ltd., Thailand, chatchai.vac@gmail.com
4
Department of Physics, Faculty of Science, Mahidol University, Thailand, wsiripun@gmail.com

SUMMARY
Thailand’s geothermal fields are non-volcanic and mostly located in northern part of the country where there
are relatively high tectonic activities. Government agencies had conducted the geothermal potential
assessment projects for electricity generation in 4 geothermal fields distributed around Chiang Mai, Chiang
Rai, and Mae Hon Son provinces. Magnetotelluric (MT) surveys were then proposed and conducted in thementioned provinces. More than 150 MT stations were deployed during 2013 – 2018, mainly to locate shallow
geothermal reservoir and delineate the geothermal system in each area. The 3D resistivity models with theinvestigation depth of up to 2,000 meters were then obtained from the off-diagonal components of the
impedance tenser within the frequency ranging from 3,000 – 0.003 Hz. The resistivity feature in all areas sharethe similar manner relating to the non-volcanic geothermal system: (1) the conductive geothermal reservoir (2)the conductive hydrothermal alteration zone (3) fluid pathway in term of fault and fracture, and (4) resistive
granite heat source. To assess the potential in electricity generation, the testing boreholes were then proposedusing the model derived from MT surveys. Several drillings were success in producing the hot water from theshallow reservoirs. In addition, the borehole results help confirming the reliability of the 3D resistivity models
and reveal an important relation between resistivity structure and the geological targets.
The case study from the Mae Chan hot spring (MCH) in Chiang Rai province and the Fang hot spring(FGH) in Chiang Mai province also demonstrate the development made in the past ten years since the 3D MT
survey was introduced to the local agencies in the early of 2010s. MT surveys of the first phase in those fields
started with a very sparse clusters of MT stations to get the big picture of the geothermal system. The detailed
MT surveys with denser MT stations (up to 50 meters) were then conducted in the focusing area pinpointed
by the previous phase of the survey. The drilling results mostly agreed with resistivity model of both MCH and
FGH which will be used as a guidance in future developing production and re-injection wells. The strategy andprocedures used in these geothermal explorations can be shared with other non-volcanic geothermal
exploration in Thailand and worldwide.
Keywords: geothermal exploration, magnetotelluric survey, northern Thailand

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CSEM/MT imaging for deep EGS geothermal project derisking
in the Upper Rhine Graben (France)
Mathieu Darnet2, François Bretaudeau2, Pierre Wawrzyniak2,, Jean-François Girard3,
Guy Marquis3, Vincent Maurer1, Albert Genter1,
ES-Géothermie, 5 Rue de Lisbonne 67300 Schiltigheim – France.
2
BRGM, 3 Avenue Claude Guillemin 45000 Orléans – France
3
EOST, 5, rue René Descartes 67000 Strasbourg – France

1

SUMMARY

In the present paper, we present a combination of 3D Controlled-Source Electromagnetics (CSEM) and
Magnetotellurics (MT) adapted to image electrical resistivity of deep granitic fractured reservoirs. This method
is applied in the Upper Rhine Graben (URG) to locate and describe accurately the geothermal resource in orderto reduce the risk of future Enhanced Geothermal System (EGS) projects planned in this area. Electricalresistivity logs data from Soultz-sous-Forêts geothermal wells (Alsace, France) have been used to design an
optimal survey, since knowledge of resistivity variation in the sedimentary cover is required to properly image
deep fluid circulation. As a validation step, a large-scale 3D CSEM/MT acquisition campaign was carried out
to cover 150 km2 in Northern Alsace.
Keywords: geothermal reservoirs, EGS, electrical resistivity logs, CSEM, MT

INTRODUCTION
Electricity and heat production using Enhanced
Geothermal System (EGS) technology rely on our
knowledge and prediction capacity of hydrothermal
fluid property, mainly temperature and flow rate.
Most of hydrothermal fluid properties are obtained
from post-drilling phases, whereas proxyinformation is hard to access in an economically
viable way. The main challenge is then to develop
methods to access these properties at the earlystage of the exploration phase.
Deep fractured reservoirs are usually characterized
by non-destructive geophysical methods (seismic,
gravity, etc.). However, these techniques have a low
sensitivity to geothermal fluids and so, do notpredict accurately the geothermal resource, andmore particularly the flowrate, before drilling
operation.
Due to their sensitivity to fluids and particularly brinewater in rocks (Archie, 1942), passive
electromagnetic(EM)
techniques(e.g.
Magnetotellurics or MT) or active (ControlledSource Electromagnetic or CSEM) have been
traditionally used to investigate the subsurface
conductivity. EM methods have shown to beeffective to characterize geothermal reservoir
geometry in volcanic area (Munoz, 2014),
hydrocarbon reservoir geometry in offshore

sedimentary area or onshore mineral exploration.
Nevertheless, the ability of EM methods to image
targets with high geothermal potential in deep
fractured reservoir and in a high man-made noise
environment still need to be demonstrated. Indeed,
CSEM sources must compete with high noise levels
and a conductive sedimentary cover resulting in low
signal to noise ratio. In the framework of DEEP-EM
project, we propose to adapt EM methods layout
and imaging techniques to overcome these
constraints and so reduce the risk of EGS projects
exploration.
The benefit of the developed method will not belimited to deep fractured reservoirs but could be
adapted to different settings like shallow geothermal
reservoirs (e.g. Dogger layer in Paris Basin) andused to characterize other kinds of fluid content (e.g.
CO2 storage).
Design of the CSEM exploration campaign
using electrical resistivity logs
For about thirty years, the Upper Rhine Graben(UGR) has been a main target for research on
geothermal exploration in deep fractured formations.
Favorable targets are mainly deep and fractured
hard rocks. In the last twenty years, geothermal
activity in the Upper Rhine Graben has been ongoing with several French (Soultz-sous-Forêts,Rittershoffen, Vendenheim, Illkirch), German
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(Landau, Insheim, Bruchsal, Bruhl) and Swiss(Riehen, Basel) deep geothermal sites which areactive from exploration to exploitation. All theseprojects deal with deep fractured geothermal
reservoirs located within Triassic-sediments and/or
crystalline basement (Aichholzer and al., 2016).
Predictive modelling suggests that CSEM
anomalies in sedimentary basins are controlled at
first order by the sedimentary cover resistivity (layer
geometry and resistivity variation between andwithin the layers) and in a second order byhydrothermal fluid circulations. Hence, theknowledge of resistivity variations in thesedimentary cover is required to properly image
deep fluid flow with high accuracy.
The Soultz-sous-Forêts and Rittershoffen (Alsace,France) geothermal wells data have provided
electrical resistivity logs to study the electricalsignature of deep fluid circulations. Preliminary
results show a conductive trend for hydrothermallyaltered fractured zones and a highly resistive trend
for unaltered facies (Figure 1) and hence a stronglink between conductivity and permeable zones.
These results will help to design an appropriatedacquisition campaign, since investigation andresolution depth in EM measurements mainly
depend on formation resistivity and on thetransmitted frequency (Zonge and Hughes,1991).

Figure 1. GPK1 resistivity laterolog in granite(geothermal well from Soultz-sous-Forêts) linked

with cuttings observation. Red arrows correspond to
the position of the main permeable fracture zones(Genter and al., 1997). Red and yellow line
correspond to resistivity average for unalteredfacies and green and purple to altered facies. GRAN:
Biotite porphyritic standard granite. PROP:
Propylitised biotite porphyritic granite. MELA:
Biotite-rich granite. LEG1: Leucogranite 1. LEG2:
Leucogranite 2. HEMA: Hematitised biotite
porphyritic granite. HMOD: Moderately altered
granite. HHIG: Highly altered granite. HEXT:
Extremely altered granite (Traineau and al., 1991).
Field trial and exploration campaign
A CSEM/MT field trial (12 receivers (RX) installed
every kilometre between Soultz-sous-Forêts andRittershoffen plants) was performed to validate thedesign of a large-scale exploration campaign. The
main goal of this test was to prove that despite theanthropogenic noise in the area, the CSEM setup
was appropriate to image resistivity variations in the
deep fractured granitic basement. In particular, thedistance (~3km) between electrodes transmitter (TX)
was increased that resulted in an amplification of
the dipole moment (product of dipole length by an
inject current intensity) and allowed to obtain a goodsignal to noise ratio more than 10km away from the
TX.
CSEM data were inverted with MARE2DEM
software (Key, 2016) and inversion results showedthat the signal to noise ratio was sufficiently low toimage resistivity variations within the basement.
Duringtheinversion
process,thebasement/sediments interface defined by seismic
imaging was used to constrain the subsurface
geometry to improve the vertical resolution ofresistivity. Interestingly, inverted resistivity shows agood match with electrical resistivity logs within thesedimentary cover and granitic basement (Figure 2).

Figure 2. CSEM results from 2019 acquisition
linked with the main formation isohypses. GPK-1(Soultz-sous-Forêts) and GRT-1 (Rittershoffen)
geothermal wells are located on the W-E profile(Figure 3). Electrical resistivity logs from GPK-1
show a good match with the inverted resistivity.

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Three geothermal power plants projects consistingof quadruplets (2 injection wells and 2 production
wells) are planned in Northern Alsace with the aim
to reach temperatures over 150°C and flow rateover 250 m3/h per well. The results of this field trial
have been used to define the acquisition
parameters of a large-scale exploration campaign(6 transmitters and 100 receivers spread on 150km²,
Figure 3) carried out subsequently. It covered thewhole exploration license and aimed at providing
additional subsurface information to prioritize thegeothermal areas of interest.

acknowledge the GEIE EMC and ECOGI for theirgrateful help in this project.
REFERENCES

Aichholzer, C., Duringer, P., Orciani, S. and Genter,
A.: New stratigraphic interpretation of theSoultz-sous-Forêts 30-year-old geothermal
wells calibrated on the recent one fromRittershoffen (Upper Rhine Graben, France).
Geotherm Energy 4, 13, (2016).
Archie, G. E.: The Electrical Resistivity Log as an
Aid in Determining Some Reservoir
Characteristics. Petroleum Transactions of the
AIME, 146, (1942), 54–62.
Genter, A., Traineau, H. and Artignan, D.:
Synthesis of geological and geophysical data at
Soultz-sous-Forêts (France). Rapport BRGM
R39440, (1997), 36.
Key, K.: MARE2DEM: a 2-D inversion code forcontrolled-source electromagnetic andmagnetotelluric data. Geophysical JournalInternational, 207, (2016), 571–588.

Figure 3. 3D CSEM/MT exploration campaign.
CONCLUSIONSEM methods have been traditionally used tocharacterize shallow geothermal targets in volcanic
context. The aim of DEEP-EM project is to developthese methods in order to use them for deep
geothermal targets in sedimentary basins.
Preliminary EM results showed the ability ofresistivity measurements to provide information on
deep fluid circulations at two different scales(borehole and reservoir). Electrical resistivity logs
reveal a clear separation between altered/unalteredfacies and furthermore a link between conductivity
and permeable zones. At the reservoir scale, a
CSEM field trial demonstrated the ability of thetechnique to image resistivity variations underneatha thick sedimentary cover (>1.4 km). These results
have been used to design the acquisition of a largescale EM exploration campaign and provide
additional subsurface information to prioritize thegeothermal areas of interest.
ACKNOWLEDGEMENTS

Munoz, G.: Exploring for geothermal resources
with electromagnetic methods. Surveys ingeophysics, 35(1), (2014), 101-122.
Traineau, H., Genter, A., Cautru, J.P., Fabriol, H.
and Chevremont, P.: Petrography of the granite
massif from drill cutting analysis and well log
interpretation in the HDR borehole GPK1
(Soultz, Alsace, France). Geothermal Scienceand Technology, 3, (1991), 1-29
Zonge, K.L., and Hughes, L.J.: Controlled Source
Audio-Frequency Magnetotellurics.
Investigations in Geophysics, (1991), 713810.Baba K, Tarits P, Chave AD, Evans RL,
Hirth G, Mackie RL (2006) Electrical structure
beneath the northern MELT line on the East
Pacific Rise at 15 degrees 45’S. Geophys Res
Lett 33(22): L22,301
Cartwright J (2007) Big stars have weather too.
IOP Publishing PhysicsWeb.
http://physicsweb.org/articles/news/11/6/16/1.
Accessed 26 June 2007

The authors wish to thank the ADEME (French
Agency of Energy and Environment) in theframework of the DEEP-EM project. The authors

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Integration of magnetotelluric data with ambient seismic noise and gravity models
in order to characterize geothermal fault-controlled systems. The Vallès Basin caseof study (NE Spain).
G. Mitjanas1, J. Ledo2, P. Queralt3, P. Piña-Varas4, A. Martí5
1Universitat de Barcelona, gmitjanas@ub.edu
2Universidad Complutense de Madrid, jledo@ucm.es
3Universitat de Barcelona, pilar.queralt@ub.edu
4Universitat de Barcelona, p.pina@ub.edu
5Universitat de Barcelona, annamarti@ub.edu

SUMMARY
In the framework of a project aimed at the development of low enthalpy geothermal energy in urban areas, the
Vallès-Penedès Basin (Catalan Coastal Range, NE Spain) has been investigated by means of an integrated
geophysical methodology. Among the different investigation methods deployed, two magnetotelluric profiles
integrated with gravity and ambient seismic noise data have been performed. Although a definitive 3D
magnetotelluric and gravity model covering the Vallés-Penedès Basin is still being worked on, preliminary
results have already allowed us to image the geological structure of the area as well as to characterize thefunctioning of the main geothermal system.
Keywords: Geothermal exploration, Integration methods

INTRODUCTION
In the Vallès-Penedès (VP) Basin area, the thermal
anomalies are located in the northeastern horstgraben limit, where a highly fractured Hercynian
granodiorite is in contact with Miocene rocks by amajor normal fault, the V-P Fault (Figure 1). This
main structure seems to control the heat and the
hot-water flow (Canals et al., 1989; Fernàndez &Banda, 1988; IGME, 1986; Mitjanas et al., 2021),
nevertheless, the geological structure of this area,
as well as the role of the different geological units,
is poorly understood.
In this structural context, the electrical methods arethose which offer a sufficient contrast to
characterize the system, as they are usually used tothe delineate geothermal reservoirs, locate aquifers,
and sometimes, estimate porosity and physicalconditions within a geothermal system (Georgsson,
2009; Hersir & Bjornsson, 1991; Piña-Varas et al.,2018). However, as seen in the conclusion of
Mitjanas et al. (2021), the combination of different
geophysical methods based on different physicalproperties, is an efficient option during thepreliminary stages of geothermal exploration, evenwhen they are used for low/mid-temperaturesystems, and also applicable in urban areas.
METHODSThis study is being carried out in the La GarrigaSamalús geothermal area which is located in thenortheastern limit of the granodioritic thrust sheet(Figure 1). Affordable geophysical methods werechosen in order to try to define the main structure of
the V-P crustal fault: two magnetotelluric (MT)profiles, gravity (GRV), and horizontal-to-verticalpassive seismic (HVSR); which details ofacquisition and processing follow the procedure
presented in Mitjanas et al., (2021).
Two MT profiles were made in the eastern limit of
the V-P Basin: MT-Samalús and MT-Vilamajor.
The MT-Samalús is located above a thermal
anomaly previously reported in borehole exploration
reports (IGME, 1982, 1984, 1986). This profile ismade up of 16 sites in a NNW-SSE profile ofapproximately 6 km length from the granodioritic
unit of the Prelitoral Range and across the V-P basin.
The MT-Vilamajor is strategically located outside
the granodioritic sheet in order to identify the
structural differences with a nearby area where nothermal anomalies have been detected. This profile
is made up of 11 sites in a NNW-SSE profile ofapproximately 4 km long, which extends from themetamorphic Paleozoic rocks, towards the V-P
Basin.
The HVSR survey includes 205 sites covering thebasin area of the red outlined square (Figure 1),increasing the site density along with the MT profiles.
The main objective of this method is to generate abasin-basement limit surface and supporting the MT
inversion.
By the acquisition of new GRV measurements
together with the previously available Bouguer
anomaly data (IGC, 2012; Mitjanas et al., 2021), we
have created a Residual Anomaly map made up of1120 sites that cover the area between CM and MTVilamajor (Figure 1). The main objective of this
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Mitjanas, G. et al., 2022, The Vallès Basin case of study (NE Spain)

method is to interpret the basin and the fault
geometry in the areas where no HVSR or MT data
have been acquired (western part).
Moreover, 3D MT and GRV models are being
developed.

Figure 3. Resistivity model MT-Vilamajor.

Figure 1. Geological map of the eastern part of the
CCR. CM) Caldes de Montbuí (70ºC); G) La Garriga(60ºC); E) Caldes d’Estrac (38ºC); S) Samalús (90ºC
at 1000 m depth) (Modified from ICGC, 2002; Martí etal., 1992).

HVSR
The basin-basement limit surface (Figure 4) showsthat the deepest part of the basin is located at eastof the Congost River. This river of La Garriga town,
can be identified as the Quaternary sediments
which have a N-S orientation (Figure 1).

RESULTS
MT-Samalús
The MT-Samalús final resistivity model (Figure 2)
images the V-P Fault as a conductive verticalanomaly with a listric geometry. At the hangingwall,the conductive syn-kinematic deposits show a
gradual deepening towards the SSE, towards thecenter of the Neogene basin.
Figure 4. Sediment basin thickness along the regionof study.

Gravity
The residual anomaly map (Figure 5) shows a large
negative anomaly with an ENE-WSW direction.
Towards the north, there is a high slope with higher
values, and towards the east and south, the values
increase slightly more gradually.

Figure 2. Resistivity model MT-Samalús (Mitjanas etal., 2021)

MT-Vilamajor
The MT-Vilamajor resistivity model (Figure 3)
presents the same similar geoelectrical bodies as
the ones presented in the previous MT-Samalús
profile. The main difference relays on the V-P Fault
geometry. Unlike the geometry defined in the MTSamalús profile, in this case, the fault zone keeps
its uprightness with depth.

Figure 5. Residual anomaly map with the GRV sites
and the base geological map.

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DISCUSSION
The resistivity models, conditioned by the HVSR
data, have allowed to constraining the basingeometry and the V-P Fault.
The V-P basin is characterized by a change inthickness (Figure 6), first, in the zone where thegranodioritic thrust sheet intervenes (between CM
and S, Figure 1); and within this zone, in the easternpart of the Congost River (G, Figure 1). The changeof basin thickness in the Congost River could be
related to a N-S Congost fault.

CONCLUSIONS
The integration of the presented geophysicalmethods has allowed the identification andcharacterization of the V-P Fault and Basin.
The V-P Fault is imaged as a conductive body,
fractured and water-saturated, which acts as a pathfor hot fluids which come from great depths. The
presence of high-degree deformation zones along
the V-P Fault trace and also the intersection withother potential faults, seem to control fast paths of
up-fault flow.
The models presented, as well as preliminaryinterpretations, lead to the conclusion that the
integration of the different methods is a very
effective way to characterize geothermal systems,
even if they are low-temperature systems or are
located in urban areas.

ACKNOWLEDGMENTS
ICGC, the Catalan Geological Survey, for the HVSR
data.
Figure 6. Basin base surface obtained using MT
models and HVSR data. Leapfrog Software.

From CM and towards the eastern limit of the V-P
Basin, the V-P Fault does not completely keep thesame rectilinear orientation but forms non-aligned
linear segments connected by fault trace bends(Figure 7). In La Garriga, this step is interpreted asa fractured relay ramp. The ramp rotations and thehigh degree of deformation in the relay zone could
also favor the development of other major structures
or could act as a potential area for the developmentof cross-fault river systems – as the identified in theCongost river –. In all such cases, it will, however,act as a site for both deformation and up-fault flow
(Walsh et al., 2018).

Figure 7. Slope gravity map. The maximum slopes
would correspond with the V-P Fault trace.

PIXIL project - (POCTEFA 2014-2020). https://pixilproject.eu/
Leapfrog Geothermal Software, ©Seequent, aBentley Company.
REFERENCES
Canals, A., Albert, J., & Ayora, C. (1989). El sistema
geotérmicodelaGarriga-Samalús: :comparaciones con sistemas hidrotermales
fósiles. In Geogaceta (Issue 7, pp. 88–90).
Fernàndez, M., & Banda, E. (1988). Aproximacion ala anomalia geotérmica de La Garriga Samalus (Vallès - Penedès). Acta Geológica
Hispánica, 23(1), 1–20.
Georgsson, L. S. (2009). Geophysical methods
used in geothermal exploration. 50(3–4), 227–
249.
Hersir, G. P., & Bjornsson, A. (1991). Geophysicalexplorationforgeothermal
resources:
principles and application. 94.
ICGC. (2002). Mapa Geològic de Catalunya
1:250.000. Institut Cartogràfic i Geològic deCatalunya.
IGME. (1982). Informe sobre el seguimiento técnicodel sondeo SAMALUS-1.
IGME. (1984). Proyecto de investigación
geotérmica en el Vallés mediante sondeos dereconocimiento y síntesis hidrogeotérmica.
Control geotermico de los sondeos SAMALUS
2, 3, 4 y 5.
IGME. (1986). Proyecto de seguimiento geológico
del sonde de reconocimiento geotérmico
SAMALUS-6 (1000 m) (Valles-Barcelona).
Martí, J., Mitjavila, J., Roca, E., & Aparicio, A. (1992).

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Mitjanas, G. et al., 2022, The Vallès Basin case of study (NE Spain)

Cenozoic magmatism of the valencia trough(western
mediterranean):Relationshipbetween structural evolution and volcanism.
Tectonophysics, 203(1–4), 145–165.
Mitjanas, G., Ledo, J., Macau, A., Alías, G., Queralt,
P., Bellmunt, F., Rivero, L., Gabàs, A.,
Marcuello, A., Benjumea, B., Martí, A., &Figueras, S. (2021). Integrated seismic
ambient noise, magnetotellurics and gravity
data for the 2D interpretation of the Vallèsbasin structure in the geothermal system of La
Garriga-Samalús (NE Spain). Geothermics,
93(March).
Piña-Varas, P., Ledo, J., Queralt, P., Marcuello, A.,& Perez, N. (2018). On the detectability of
Teide volcano magma chambers (Tenerife,
Canary Islands) with magnetotelluric data.
Earth, Planets and Space, 70(1).

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Hydrothermal model of Aso volcano, Central Kyushu, Japan, inferred from AMT and
ACTIVE datasets
T. Minami 1, M. Gresse2 and M. Utsugi3
Kobe University, Japan, tminami@port.kobe-u.ac.jp
2
National Institute of Advanced Industrial Science and Technology, Japan, marceau.gresse@aist.go.jp
3
Kyoto University, Japan, utsugi.mitsuru.5c@kyoto-u.ac.jp
1

SUMMARY
Aso volcano, Central Kyushu, Japan is one of the most active volcanos in Japan. It has an activity cycle witha period of about 15 to 20 years consisting of the active phase for about one year accompanied by mud,strombolian, and phreatic/phreatomagmatic eruptions and the other quiescent phase. Since thephreatomagmatic eruptions of Aso volcano are most devastating during the active period, our aim is to reveal
the hydrothermal system of Aso volcano and understand the mechanism of the phreatic/phreatomagmatic
eruptions. To reveal its shallow resistivity structure, two kinds of electromagnetic (EM) measurements havebeen conducted in Aso volcano: AMT and ACTIVE (Array of Controlled Transient-electromagnetics for Imaging
Volcano Edifice, Utada et al. 2007). ACTIVE is a volcano monitoring system using transient EM method
composed of a source electric dipole with earthing two electrodes and induction-coil receivers for measurementof the vertical component of the induced magnetic field. Our previous study (Minami et al., 2018) revealed thetemporal change in the three-dimensional (3-D) resistivity structure through the magmatic eruptions of Aso
volcano from August 2014 to August 2015 using the ACTIVE campaign datasets. With the aid of thehydrothermal simulation code TOUGH2 (Pruess, 1991), on the other hand, we recently found that ahydrothermal model assuming empirical porosity, permeability, and heat source at depth of the active crater
explains well the 3-D resistivity structure inferred from AMT survey data (Kanda et al., 2019). We are currently
integrating the hydrothermal model from the AMT dataset and the temporal variation in the resistivity structure
revealed by the ACTIVE datasets. In the presentation, we present how our hydrothermal model explains theresistivity structure inferred from the AMT dataset and our project to integrate AMT and ACTIVE datasets intoa hydrothermal model of Aso volcano.
Keywords: volcano, Aso, hydrothermal system, MT, CSEM

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Using magnetotelluric and differential magnetometer data to quantify Space weather
risk in the UK high voltage power transmission grid
Juliane Hübert1 ∗ , Ciaran Beggan1 , Gemma Richardson1 , Natalia Gomez Perez1 and Alan Thomson1
1
British Geological Survey, Edinburgh, UK∗juliane.huebert@bgs.ac.uk

S UMMARY
Geomagnetically induced currents (GICs) that occur during severe geomagnetic storms have been identified
as a hazard to ground-based modern infrastructure like the power grid, gas pipelines and railways. During past
extreme space weather events, disruption to the electricity supply in several mid- and higher latitude countries
have been reported. In order to monitor, model and forecast GICs, sophisticated models of the ground electricfield and the network topology are required in a multi-disciplinary approach. EM geophysics can provide
realistic ground electric field estimates using magnetotelluric data and/or models of electrical conductivity ofthe earth.
We present a detailed analysis of newly collected differential magnetometer (DMM) and MT data in the UK thatallow the verification and validation of our network model for the UK power transmission grid. Combining theobservation of line GICs measured with DMM at several sites in the UK in the past four years and the electricfields derived from the MT data show an excellent fit of prediction and observation of GICs. This validation of
the network model and electric field calculation allows us to use it with confidence for real time modelling andforecasting as well as extreme event analysis.
Keywords: Space Weather, Geomagnetically induced currents (GICs), Differential magnetometer method,geoelectric field model

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Time-lapse 3D CSEM for reservoir monitoring based on rock physics simulation

1

M. Ettayebi, S.Wang, M.Landrø 1
Norwegian University of Science and Technology (NTNU), mohammed.ettayebi@ntnu.no,shunguo.wang@ntnu.no, martin.landro@ntnu.no

S UMMARY
Marine controlled-source electromagnetic (CSEM) method measures EM fields transmitted by an active source
either at seafloor or a few tens of meters above the seafloor, therefore it can image the electrical resistivity
beneath the seafloor. Being able to resolve resistivity anomalies of high saturations of petroleum, marine
CSEM sounding has gained increasing popularity after its introduction in 1981 by Cox. Nevertheless, theEM research community starts to extend its applications from petroleum exploration to an active production
monitoring tool. Our study focuses on the latter.
By utilizing various dynamic reservoir properties made available through reservoir simulation of the Wisting
field located in the Norwegian part of the Barents Sea, a realistic geoelectric model was created. To thisend, we develop geologically consistent rock physics models, such that the available simulation results can betransformed into resistivity maps. We show that the resistivity map pertaining to each time-step can be used
as an input model in a Finite Difference Time Domain (FDTD) forward modeling workflow to produce synthetic
EM data. This synthetic EM data can be studied and analyzed in light of production induced changes in thereservoir for different production phases. This will allow us to acquire insights towards developing a technicallyfeasible reservoir monitoring workflow suitable for time-lapse CSEM. Our result shows that at different production phases, the CSEM responses are different. Therefore, the method can be effectively used for production
monitoring purpose. Moreover, this study will enable the testing of other time-lapse workflows with realistic
complexities evaluating the potential of this technology for field application, through investigating the resolution
limitations and the repeatability requirements.
Keywords: CSEM, time-lapse, reservoir monitoring, production monitoring, 4D

I NTRODUCTION
The controlled-source electromagnetic (CSEM) experiment has for decades been established as areliable method for hydrocarbon exploration. It isalso widely acknowledged as a complementary tool
to the seismic experiment, mainly due to the highsensitivity of the method to resistive and conductive anomalies, such as oil reservoirs and water.
Recently, several studies have been devoted to extending the applications of the CSEM method frommerely being a risk-reduction tool to time-lapse production monitoring tool. However, there is a lack of
research that thoroughly studies the potential of the
CSEM tool in capturing production induced changes
in the fluid content for realistic and detailed reservoir
models and production strategies.

Currently, an increasing attention is drawn towards understanding the time-lapse behaviour of
the CSEM method in detail. Therefore, we conduct
research on how to enable time-lapse 3D CSEM forreservoir monitoring. By developing a geologicallyconsistent rock-physics model, the dynamic reservoir properties of the Wisting field located in the Norwegian part of the Barents sea can be converted
into resistivity models for each time-step. These resistivity models can be utilized as inputs in a Finite
Difference Time Domain (FDTD) workflow to generate synthetic EM data that can be studied and analyzed for time-lapse production effects.
The Wisting discovery was made back in 2013 by
the wildcat well 7324/8-1. The well was drilled
down-flanks on a structure in the Wisting Central
prospect and hence got the name central well (Fig1/4

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ure 1). The reservoir at this area shows good quality,
and it belongs to the Realgrunnen Subgroup which
consists of three formations: Fruholmen, Nordmela
and Stø Formations. The well entered the top of thereservoir which is the Stø Formation at the depth of662 m. The water depth here is 400 m and the structural crest is currently at 250 m burial depth. Wisting
is known for its discernible electric properties where
the background resistivity can reach up to 20 Ωm(Granli et al, 2017). The central well (7324/8-1) was
drilled with the primary objective of investigating andevaluating the Jurassic Realgrunnen Subgroup forhydrocarbons (NPD, 2022), which indeed ended up
as a discovery well.
Barents Sea

68˚ N

Norwegian Sea

gate that in future research.

R ESISTIVITY MODELING FROMTIME - VARYING WATER SATURATIONSIMULATIONSCSEM data can be used to image resistivity structures. Subsequently, getting insight into the different rock properties that can alter subsurface resistivity is of critical importance for CSEM interpretation.
Therefore, creating a reservoir model that will be
used for time-lapse CSEM does depend on the resistivity distribution across the field considered. This
brings into question the importance of a precise andrealistic mapping of the resistivity. Time-varying water saturation can be transformed to time-varying resistivities and subsequently to time-varying Anomalous Transverse Resistance (ATR) maps. The Wisting field has demonstrated an excellent oppurtunity
to show the ability of CSEM for reservoir monitoring.

60˚ N

North Sea

15˚ W

5˚ W

5˚ E

15˚ E

25˚ E

Figure 1: The Wisting field located in the Norwegian partof the Barents Sea as highlighted by the red rectangle. Lower right corner: the structure depth mapof the Wisting field. The thick black lines are intersection lines going through specific wells. Upper
left corner: schematic representation of the local
structural and sedimentological geology along bothof the thick black lines shown in the lower right corner.

A large database was made available for this timelapse study, consisting of Eclipse production simulation models, well-logs, 3D CSEM inversion results
and both conventional and P-cable 3D seismic data.
The main objective of this work is to employ andcombine the available data in various extents intoa rock physics framework enabling us to create a
serial of realistic resistivity models to represent oil
production phases. These models can be utilized toinvestigate the survey parameters repeatability requirements and feasibility of time-lapse CSEM. With
that in mind, this study consists of two main parts.
The first one deals with the construction and calibration of the reservoir model while the second is devoted to studying the feasibility of time-lapse CSEM.
As for the repeatability requirements, we will investi-

The reservoir resistivity is dependent on a number of subsurface parameters. By Archie’s law(Archie, 1942) it is clear that the saturation exponent n is one important parameter. Archie’s law
makes three implicit assumptions as pointed out by
Mungan and Moore (1968); one is that the only conductive material is brine and that the rock matrix
is non-conductive. However, if the reservoir contains clay, then rather more advanced models are
required to model it.
Another physical property that has close interplay
with reservoir resistivity is rock wettability and its distribution. Mungan and Moore (1968) state that thesaturation exponent n appears to be saturation dependent increasing with the decrease of water saturation, and it can be as high as 9 when the water
saturation is approaching connate water saturationin oil-wet formations. Bearing in mind that the reservoir at Wisting is possibly oil-wet as suggested by
Alvarez et al (2018), this introduces the possibilityof adjusting the saturation exponent n resulting inhigher modeled resistivities.
As previously studied, there are many factors that
can alter the saturation exponent and one of thesefactors is wettability (Adisoemarta et al, 2001). The
model developed here and which is calibrated withthe central well, elaborates further on this in conjunction with the conclusions made by Mungan and
Moore (1968) on oil-wet formations. The model
deals with the non-linearity of the saturation expo-

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nent in oil-wet formations depending on water saturation. The saturation exponent should increasesignificantly in oil-wet formations to account for theisolated water globules in the larger pores, andwhich will not be able to conduct current (Anderson, 1986). Moreover, the wettability effects become more prominent as the brine saturation decreases. This stipulates establishing a connate water saturation Sw,connate threshold or cut-off below
which the rock changes its wettability from a stateof mixed wettability to becoming oil-wet, resulting ina larger saturation exponent. The fact that Alvarez
et al (2018) use a saturation exponent equal to 2.8 intheir Wisting resistivity modeling supports the claimof Wisting being oil-wet. By incorporating information from both capillary pressure curves and Free
Water Level (FWL) depth maps, the connate water
saturation threshold was set to 10%.
Different gas caps scattered over the field havebeen recognized and these deserve special treatment in terms of the saturation exponent n. The latter should most likely be less than 2 in gas-bearing
formations, introducing the necessity for choosing
another threshold value for the gas saturation which
for the reader’s convenience will be given the namecritical gas saturation. This may consequently demarcate the transition from oil-wet rocks with low
water saturation combined with a large saturation
exponent on one hand, and gas-bearing zones withrelatively high gas saturation combined with a lower
saturation exponent on the other hand. After multiple attempts, a gas saturation threshold value equal
to 70% was deemed to produce the desired models. By desired models, the generated ATR maps
are consistent with our understanding of the spatial
distribution and magnitude of the most resistive targets and this conforms with the inverted 3D CSEM
ATR maps.
With this background, a non-linear saturation exponent was modeled having its maximum value of 2.5
at the top of the reservoir and non-linearly decreasing with depth and the overall water saturation trend.
Note that this only applies for intervals where thewater saturation is lower than the connate water saturation 10%. On the other hand, if the gas saturation is higher than the critical gas saturation, thenthe saturation exponent n is assigned the value 1.8.
To summarize, there are two main factors that decide what kind of value the saturation exponent is
assigned, and these are the connate water saturation and critical gas saturation thresholds. The rockphysics model was used to transform detailed water saturation simulations to detailed resistivity mapsthat are used as inputs in a FDTD forward modelingworkflow to generate synthetic CSEM data. Two resistivity maps are presented in Figure 2, in which thereservoir is marked by warm color.

Figure 2: 2D resistivity maps for two different time steps.
The year 2027 (plot a) when the field is expected
to start producing and in the year 2057 (plot b) towards the predicted end of production. The white
rectangles are receivers and the black vertical lines
going through them are towlines. Note that the colorbar scales pertaining to the two different plots aredifferent.

T IME - LAPSE PRODUCTION INDUCEDRESPONSES

The time steps at the onset of production in theyear 2027 and after 30 years of production in theyear 2057 are used for simulation. Figure 3 shows
the Ex-field normalized magnitude (NM) maps pertaining to the last time step. The source-receiver
offset is set to 2667 m and the frequency is 5.0
Hz. Normalized value 1 represented by the lightish green implies that no change happens throughthe 30 years. If the NM is larger than 1, one
can assume that there are changes to spot in thefluid content of the subsurface reservoir. It is clearthat Figure 3 depicts discernible production-inducedchanges represented by the red positive anomalies. A clear and good spatial match exists between these anomalies and the resistivity maps in
Figure 2, as we can clearly see that the epicentreof the anomaly is where most of the production ispredicted to take place. A producing hydrocarbon
field is expected to have decreasing petroleum volumes with time and hence lower resistivity values
due to water-injection. Towards the end of the lifetime of a producing field, most of the target resistor
will be gone and the end result is without the target resistor embedded in it. Figure 3 underpins thisfact with the positive anomaly, which implies that thetarget response in the year 2027 is larger than thepseudo-background response in the year 2057. Another observation worth mentioning is the relatively

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high frequency depicted here, and the higher resolution that offers. We think that a combination of relatively high background resistivity and shallow burial
depth might contribute to a setting that is favorable
for slightly higher frequencies.

that are essential for this research to be conducted,
and also for permission to publish this work. We are
grateful to Friedrich Roth for his software support
and to Jan Petter Morten for his inputs. The study
was funded by the Norwegian Research Council together with the GAMES consortium and the Center of Geophysical Forecasting (grant nos. 294404,
309960, and 324442). The modellings were performed on resources provided by Sigma2 - the National Infrastructure for High-Performance Computing and Data Storage in Norway (nn9872k).

R EFERENCES

Figure 3: Normalized magnitude (NM) 2D map of the Ex
field component for the year 2057 towards the predicted end of production. The source-receiver offset is 2667m while the frequency is 5.0 Hz. The
black dots are receiver positions.

C ONCLUSION
We showed how dynamic reservoir simulations related to different time steps can be converted to resistivity spatial distributions through a detailed, realistic and geologically consistent rock-physics model.
These resistivity maps can be used as inputs ina FDTD forward modeling workflow to generate
synthetic CSEM data. The CSEM data are studied and analyzed in different approaches towards
understanding and developing a feasible workflow
for time-lapse CSEM studies. What comes forththrough this study is the ability of CSEM data todetect and capture production induced changes in
the fluid content of a producing hydrocarbon reservoir. In addition, the presented workflow proves itself to be a feasible one, demonstrating how timelapse CSEM can be used. The future work is totake a closer look at the repeatability requirements
and limitations of time-lapse marine CSEM.

ACKNOWLEDGMENTS
We would like to thank the operator Equinor and thelicense partners for sharing with us valuable data

Adisoemarta PS, Anderson GA, Frailey SM, Asquith
GB (2001) Saturation exponent n in well log interpretation: Another look at the permissible range.
SPE Permian Basin Oil and Gas Recovery Conference DOI 10.2118/70043-ms
Alvarez P, Marcy F, Vrijlandt M, Skinnemoen Ø,
MacGregor L, Nichols K, Keirstead R, Bolivar F,
Bouchrara S, Smith M, Tseng HW, Rappke J(2018) Multiphysics characterization of reservoir
prospects in the hoop area of the barents sea.
Interpretation 6(3):SG1–SG17, DOI 10.1190/int2017-0178.1
Anderson WG (1986) Wettability literature surveypart 3: The effects of wettability on the electrical properties of porous media. Journal of
Petroleum Technology 38(12):1371–1378, DOI
10.2118/13934-pa
Archie G (1942) The electrical resistivity log as an
aid in determining some reservoir characteristics.
Transactions of the AIME 146(01):54–62
Granli JR, Veire HH, Gabrielsen P, Morten JP(2017) Maturing broadband 3D CSEM for improved reservoir property prediction in the Realgrunnen Group at Wisting, Barents Sea. SEG
Technical Program Expanded Abstracts 2017 pp
2205–2209
Mungan N, Moore E (1968) Certain wettability
effects on electrical resistivity in porous media. Journal of Canadian Petroleum Technology
7(01):20–25, DOI 10.2118/68-01-04

NPD F (2022) 7324/8-1 general information.
https://factpages.npd.no/en/wellbore/pageview/exploration/all/7
accessed: 2022-06-11

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3D modeling and inversion of ground-based TEM data, a case study of seawater
intrusion on the eastern coast of the Gulf of Aqaba, Jordan
J. Abu Rajab1, H. El-Kaliouby2, E. Al Tarazi1 and H. Al-Amoush3
Department of Earth Sciences and Environment, Prince El-Hassan bin Talal Faculty for Natural Resources
and Environment, The Hashemite University, Jordan, jafars@hu.edu.jo; eid@hu.edu.jo
2
Department of Geophysical Sciences, National Research Center, Egypt, hkaliouby@gmail.com
3
Department of Applied Earth and Environmental Sciences, Faculty of Earth and Environmental Sciences, Al
al-Bayt University, Jordan, hani.alamoush1@gmail.com
1

SUMMARY
The research describes the ground-based transient electromagnetic (TEM) survey design andmodeling/inversion scheme used to recover detailed geoelectrical structures at seawater intrusion in coastal
areas. We analyzed the simulated and experimental TEM data along the profile using side-to-side 25 m × 25
m loop dimensions, conducted perpendicular to the coastal line. In addition, a coarse grid of experimental
TEM loops of 100 m ×100 m dimensions was also evaluated for deeper investigation in view of the 3D
inversion scheme conducted along the coastal area. In our 2D and 3D simulated models, we created multiple
simplified geoelectrical structures that approximated seawater intrusion in sandy aquifers while allowing for
topographical variations. The transient responses of the saline zone (seawater 1 Ωꞏm), the mixing zone
(brackish water 3 Ωꞏm) and the fresh zone (freshwater 100 Ωꞏm) were calculated based on the modified
Druskin-Knizhnerman finite-difference algorithm. Modeling of an inclined seawater interface of less than 45°
shows subparallel horizontal layering; however, a wedge-like structure becomes more visible at a higher
angle. In the presence of two zones (saline and brackish) and three zones (saline, brackish, and fresh), thegeometry of inland intrusion is fairly determined using 1D inversion, but the resistivity values among intrusion
phases are severely overestimated, and the transient transformation images provide minimum information
about intrusion characteristics like depth to boundaries and lateral extension. However, 3D inversion along
the TEM profile yields improved geometrical characteristics of intrusion, particularly for resistivity values. A 3D
inversion of experimental TEM data distributed along profile and across the seawater interface reveals that
resistivity values and interface structure agree much better with the inclined wedge-like structure. A 3D
inversion of experimental grid TEM data spread along the seawater interface allows for high contrast at majorcontacts like brackish water-bearing sediments and saline water-bearing sediments, and the recovered model
is comparable to reference sites like total dissolved solids measurements from well data.
Keywords: seawater intrusion, TEM, 2D/3D modeling and inversion, Gulf of Aqaba, Jordan
INTRODUCTION
Coastal aquifers, particularly in arid and semi-arid
locations, are an essential source of drinking water
for urban development. Because seawatercontains more dissolved salts and minerals and isdenser than terrestrial freshwater, it has a higher
hydraulic head along the coastal margin thanfreshwater. Seawater moves into coastal aquifers
in a wedge shape under freshwater due to its
higher pressure and density (Bear et al.1999). A
transition zone develops where seawater andfreshwater meet and interact. Because fresh
groundwater levels, or the height of the freshwatercolumn, rise with land elevation, the inland extent
of the seawater wedge is varied. The transient
electromagnetic method (TEM) has been
extensively used to study coastal aquifers since it
is capable of defining groundwater resistivity with aresolution of a fraction of an Ωꞏm. The TEM
method is used to study the geometry of seawater

intrusion, utilizing information from geologicalresearch and boreholes exploration. Furthermore,
two-dimensional (2D) and three-dimensional (3D)
TEM surveys are a cost-effective solution to
improve TEM data interpretations and gain a better
understanding of seawater intrusion. Furthermore,
substantial progress has been achieved in thedevelopment of TEM instruments, surveying
methods, and 3D interpretation techniques(Newman and Commer 2005; Everett 2012;
Barsukov and Fainberg 2013; Oldenburg et al.
2013; Barsukov et al. 2015). Nonetheless, there isdebate about the efficiency and necessity of using
3D modeling and inversions for transient induction
data. Even though a 1D assumption might work in
a 1D environment, larger current flow patterns intransient induction electromagnetic systems are
more likely to require 3D modeling and inversion to
improve the physical contrasts in complex
geological conditions like fractures, faults, cavities,
and changes in the aquifer's hydraulic properties.
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METHODIn this study, we analyzed TEM data obtainedusing the TEMFAST 48 instrument in the area of
seawater intrusion in Aqaba, Jordan. The firstgroup of TEM data was optimized using a 25 m
side-to-side single turn loop across the coast line.
The second TEM group data was collected withcoarsely spaced TEM of 100 m loop side lengththat was performed as a grid area along the coastline where the lithology and total dissolved solids("TDS") of some boreholes are presented. We also
simulated and analyzed TEM data-based 2D/3D
structures in sandy aquifer with saline, brackish,and fresh water. TEM-RES Software (AEMR, Inc.,
Netherlands) was used for processing TEM
measurements and for one-dimensional (1D)inversion
purposes. The
resistivities
andthicknesses at each sounding site of the locally 1D
earth's layered model was inverted based on amodified
Levenberg–Marquardt
approach(Barsukov et al., 2015).In case of 3D environment,
the quasi-stationary approximation is used tomodel the 3D problem based Druskin andKnizhnerman (1988) method; this approximation is
feasible when the displacement currents are
infinitesimally smaller than the conduction
current. The approach for 3D modeling/inversion of
TEM data is conducted using the code TEM-3D
WIZARD (AEMR, Inc., Netherlands).
simulated TEM data along profile (2D/3D
subsurface structure)
TEM-3D WIZARD created simple geoelectricalstructures to simulate seawater intrusion in sandy
aquifers while taking topographical effects into
consideration. Figure 1 depicts the 3D model ofseawater intrusion data from a 2 × 2 km2 area and200 m depth. The x-axis is aligned with the profile
of soundings with a 25 × 25 m2 using single loop
configuration, which passes through the center ofthe 2D seawater intrusion (x: -500 to 500, y=0).
The transient responses of the saline zone
(seawater 1 Ωꞏm), the mixing zone (brackish water
3 Ωꞏm) and the fresh zone (freshwater 100 Ωꞏm).

Figure 1. A 3D model of seawater intrusion was
utilized to simulate TEM data from a 4 km2 area.
The aquifers' resistivities were constructed with a
45° inclination. A resistivity slice is shown below the
topography.
RESULTS
Figure 2 shows the transient responses in terms of
the apparent resistivity measured at severaltransient times for the 2D seawater intrusion model.

Figure 2. A 2D model of seawater intrusion andthe transient responses of the saline zone
(seawater 1 Ωꞏm), the mixing zone (brackish water
3 Ωꞏm) and the fresh zone (freshwater 100 Ωꞏm).
The response for the transient curves obtained atthree positions: -450m for seawater, +0.0 for
brackish water and the +450m for freshwater. The
modeling were calculated for the same positions
considering 2D/3D effect and presented in Figure
3.

Figure 3. Apparent resistivities of transient
responses for 2D and 3D intrusion structures, i.e.,
at -450 location, seawater response shows asignificant resistivity difference for early andlate-time response between 2D and 3D intrusion
structures.

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Figure 4 shows seawater intrusion using a 2D
object effect and the results of a stitched 1D
pointwise inversion. This made-up example wasused to figure out the range of resistivity and theshape of the resulting layer structure.

saline water zone (<1Ωꞏm), brackish water zone(~3 Ωꞏm) and possible fresh water zone (>100Ωꞏm). These resistivity ranges match the local
aquifers' resistivities.

Figure 4.TEM resistivity simulated model based 2D
structure of seawater intrusion, 1D RMS%=0.2.
The synthetic data in Figure 4 was, on the other
hand, subjected to a 3D modeling and inversion
procedure and modeled using the maximum 3D
effect (Full space of X,Y,Z cells). This approach(Barsukov et al. 2015) includes 1D point inversion(each point in the profile is independent of theresults of inversion on adjacent points), a global 1D
inversion process to optimize the resistivity scale,and mutual 3D modeling computation and 3D
inversion (Figure 5).

Figure 5. TEM resistivity simulated model based
3D structure of seawater intrusion,3D RMS%=6.5.
A case study from the east coast of Aqaba Gulf
71 TEM sites of 100 loop side length were selected
to be included in the 3D inversion and modelingprocesses. We incorporated TEM data with station
spacing averaging 350 m in most places. The
geometrical skeleton of a region produced a
discretized volume of
approximately 130,000
cells in the domain of 1D TEM data using a grid of
X = 4900 m, Y = 2200 m, and Z = 300 m. In thisarea, a 3D inversion resistivity map is shown for anelevation of -30 m below sea level (Figure 6). The
slice map reveals a considerable contrast between
saline water-bearing strata (<1Ωꞏm) and brackish
water-bearing strata along the coast (>10Ωꞏm).
Figure 6 presents the location of a TEM profile on
the study area's southwestern side. The profile iscomposed of 20 loops with a loop side length of 25
m. Figure 7 shows the resistivity model based 3D
inversion along profile and considering underlying
3D structure, the outcomes reveal three zones;

Figure 6. TEM resistivity model (Level=-30m)
constructed from 3D inversion of 71 TEM loops, 3D
RMS%=15.White square is 100×100 m2 loop.

Figure 7. TEM resistivity model constructed from
3D inversion along profile and across seawater
intrusion interface, 3D RMS%= 11.1, its location
is presented in Figure 6. White square is 25×25
m2 loop. Distances become inconsistent after TEM
15 due to site constraints.
Figure 8 shows the 3D inversion obtained from 71
TEM sites. The derived four resistivity
cross-sections provided a high contrast at majorcontacts and along seawater intrusion interface,such as those between brackish water-bearing
sediments and saline water-bearing sediments. In
the direction of S1 profile near VES3 (i.e., 10 and
1 Ωꞏm, see Figure (8b)), and in the direction of S2
profile near Tour1 (i.e., 5 and 0.9 Ωꞏm. Anothersignificant contrast has been defined at theboundary between alluvium deposits and theseawater intrusion zone (i.e., 200 and 0.6 Ωꞏm)
Figure (8d).
CONCLUSIONSThe TEM method was used to establish resistivity
modeling of seawater intrusion; however, modelingof seawater intrusion is challenging due to thecomplex geology, which includes numerous

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Figure 8. 3D inversion
model of a 100 m × 100 m
TEM loop: a) Map shows
the distribution of nearcoast 71 TEM sites and thedesigned models' profiles(S), b) 2D geoelectricalmodel along (S1) profile, c)
2D geoelectrical model
along (S2) profile, d) 2D
geoelectrical model along(S3)profile, e) 2D
geoelectrical model along(S4) profile, and f) Shaded
relief topography of thestudy area shows theboundary of deep incised
wadi (green dashed line)
and the area of selected
TEM sites (transparent
pink rectangle).

layers types and the presence of possible
fractures, faults, cavities and variations in theaquifer's hydraulic properties. So we model sandy
aquifer to mitigate these parameters. TEM along
profile employing single loop and side-to-side
survey followed by 3D inversion and considering
subsurface 3D structure is required for better
resolving subsurface structure, especially in thearea of seawater intrusion where resistivity
variation is small. False resistivity interpretations
may result from 1D point inversion or 2D stitchingof 1D models. In the case of electromagneticallycoupled station distances, 3D model-based 3D
inversion is feasible and desirable. Reconstructing
the geoelectrical structure of the medium is an
ill-posed problem, and the final solution is auser-dependent issue that must be geologically
accepted (details and stability). In the 71 TEM data
set at the Aqaba coast, these requirements were
not optimal; site restrictions have always limited
the choice between densely spaced profiles
without 3D information or too coarsely spaced 3D
data. Cross sections can be used to interpretaquifer resistivity near reference boreholes.
ACKNOWLEDGEMENTS
The Ministry of Higher Education and Scientific
Research/Jordan and The Hashemite University
are thankful for funding this study (grant number
WE/1/11/2019).
REFERENCES
Barsukov PO and Fainberg EB ( 2013)
Three-dimensional interpretation of TEM

soundings. Izvestiya, Physics of the Solid Earth
49: 517–525
Barsukov PO, Fainberg EB, Khabensky EO (2015)
Shallow investigations by TEM-FAST technique:methodology and examples. In Spichak VV( ed)Methods of Geochemistry and Geophysics.
Elsevier 40, pp 47–78
Bear J, Cheng A, Sorek S, Ouazar D, Herrera I(1999) Seawater intrusion in coastal aquifers:
concepts, methods and practices. Springer
Science, Business Media
Druskin V and Knizhnerman L (1988) A spectral
semi-discrete method for the numerical solutionof 3-D nonstationary problems in electricalprospecting. Izvestiya, Earth Physics 24:
641–648
Everett ME (2012)Theoretical developments inelectromagnetic induction geophysics withselected applications in the near surface.
Surveys in Geophysics 33: 29–63
Newman GA, Commer M (2005) New advances intransient Electromagnetic inversion.
Geophysical Journal International 160: 5–32
Oldenburg DW, Haber E, Shekhtman R (2013)
Three dimensional inversion of multisource
time domain electromagnetic data. Geophysics
78: E47–E5

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Using 3-D electrical conductivity model for understanding geological units of
nonvolcanic geothermal reservoirs
B. Erdenechimeg1, F. Samrock1, A.V. Grayver1, A. Kuvshinov1, M.O. Saar1, D. Sodnomsambuu2, Ts.
Shoovdor2 and P. Dorj3
1ETH Zurich, erdenecb@ethz.ch
2Institute of astronomy and geophysics of Mongolian academy of science
3Renewable energy center of Mongolia

SUMMARY
Understanding the geological setting of nonvolcanic geothermal systems is vital to explaining the formation ofgeothermal reservoirs and their observable surface manifestations. The magnetotelluric method, used todetermine the subsurface electrical conductivity distribution, is a common tool in geothermal exploration. In
this study, we present an integrated interpretation of an electrical conductivity subsurface model, together withgeological analyses and geochemical probes from a nonvolcanic, intermediate-temperature geothermal
system in Mongolia. We conducted magnetotelluric (MT) and telluric-magnetotelluric (TMT) measurements at
the Tsenkher geothermal area in the Mongolian Khangai dome during the summers of 2019 and 2020. The
20km*20km large area is characterized by three major hot springs with water temperatures up to 87°C. Froma total of 196 MT and TMT stations, we obtained a 3-D electrical conductivity model of the subsurface. To
interpret the data, we used a high-order finite-element electromagnetic modelling code (GOFEM) with locallyrefined hexahedral meshes that allows including accurate topography while ensuring high numerical accuracy
with a sufficiently fine discretization of the inversion domain.
The best-fitting model provides essential insights into the subsurface structure of the Tsenkher geothermalarea. The model is characterized by a strong vertical crustal conductor that appears south of the hot springs
area and rises from depths of more than 10 km to the surface. We interpret this conductor as a remnant of
past local volcanism and a zone of former magma ascent, indicating the potential source for the observed
enhanced surface heat flow in the hot springs area around Tsenkher. Additionally, the model includes a
prominent striking conductor beneath the hot springs at depths down to more than 3 km below the surface.
The conductor is spatially aligned with a major fault that intersects the survey area, and is accompanied byseveral basaltic dyke intrusions. We interpret the fault-aligned conductor as the major area of deep fluid
circulation and an accumulation zone for heated fluids. The interpretation agrees with theoretical concepts of
topography-driven deep fluid circulation and local fault zones playing a major role in the transport of hot waterfrom a reservoir to the surface. Inferred reservoir temperatures from geochemical fluid analyses areinagreement with interpretations of the maximum depth of fluid circulation inferred from the MT model.
Our MT subsurface model serves to better understand the formation of the Tsenkher hot springs in particularand intermediate-temperature geothermal systems in general.

Keywords: MT-TMT data acquisition, 3-D inversion, geothermal exploration, amagmatic geothermal systems,
deep fluid circulation

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Exploration of deep aquifer in North Jordan using TEM and MT
Gerhard Kapinos1, Nedal Atteyat², Nidal Jahed², Florian Brückner1, Falk Lindenmaier1, Armin Margane1,
Mathias Toll1, Pritam Yogeshwar3
1 Federal Institute for Geosciences and Natural Resources (BGR), Germany, Gerhard.Kapinos@bgr.de
2 Ministry of Energy and Natural Resources (MEMR), Jordan
3 Institute of Geophysics and Meteorology, University of Cologne, Germany

SUMMARY
Deep aquifers at depths of 300 m to 1000 m have rarely been the focus of groundwater research. However,their importance for groundwater supply, especially in arid regions, is increasing, not least since theunmistakable climate change.
In order to quantify the location, extent and size of such deep aquifer systems, to understand them and thus
to be able to use them sustainably, we used a combined transient electromagnetics (TEM) andmagnetotellurics (MT) approach to investigate the distribution of electrical conductivity in relation tohydrogeological structures in a field study in northern Jordan.
42 TEM and 24 MT soundings were conducted in 2018 and 2019, mostly along profiles.
The TEM and MT data are complementary. MT resolves structures below 600 m, which is below the depth ofinvestigation (DOI) of TEM. This allows deeper aquifer systems and their bases to be explored. In addition,
lateral variations in conductivity can be detected with MT. Since the MT-data seem to indicate a onedimensional (1D) background in the period range between 10-4 and 101s, the TEM-data could be used tocorrect the observed static shift in the MT data. Subsequently, both separate inversions of TEM and MT andjoint inversions of TEM and MT were performed.
Another added value of the combined use of two methods is the strengthening of the robustness of the resultsby the joint inversion.
The joint 1D inversion confirms and complements the results of the separate inversion of the TEM- and forstatic-shifted corrected MT-data. The conductivity models, which provide a two-dimensional image of the
conductivity in the subsurface, confirm hydrogeologic structures that are already known or suspected at a few
points from well data, but also reveal new structures and new insights that are very valuable for understanding
the aquifer system.
Keywords: Magnetotellurics, Transient electromagnetics, deep aquifers, joint inversion

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Exploration of geothermal areas at the central part of Mexico through the application ofMagnetotellurics and Transient Electromagnetics
Ruiz-Aguilar, D., García-Suárez, E., Cruz-Aquino, I.J., Pioquinto-Arcos, E., Roque-Pineda,
L.S., Peiffer, L., Inguaggiato, C., Delgado-Argote, L., Contreras-López, M., Arango-Galván,C.
In the framework of a collaborative project funded by the Mexican government, we applied
Magnetotellurics and Transient Electromagnetics in two geothermal areas located in
Michoacán, a state placed in the central part of Mexico. These geothermal areas (Araró and
San Agustín del Maíz) are located around Cuitzeo lake, where several geothermal surface
manifestations have been reported. In Araró, a total of 31 MT stations and 30 TEM
soundings were acquired. Whereas, in San Agustín del Maíz, we only acquired 21 MT
soundings. At both areas, the soundings were spatially distributed around the locations of
the thermal springs and water wells with high temperature. The acquired MT time series
were robust processed to estimate the transfer functions. We perform the 3D inversion
modeling of both MT data sets using ModEM. Regarding the TEM data, we interpreted themusing 1D Occam and Marquardt techniques and the derived information was integrated in
the corresponding 3D MT inversion scheme. Finally, the 3D resistivity models are appraisedand correlated to geochemical and geological data, that were also obtained within theframework of this project.

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Determination of the cap rock integrity in the Çanakkale-Tuzla hydrothermal systemfrom inversion of magnetotelluric data by using particle swarm optimization
E. Büyük1 and A.Karaman2
1Department of Geophysical Engineering, Faculty of Engineering and Natural Science, Gümüşhane

University, Gümüşhane, Turkey,
2Department of Geophysical Engineering, Faculty of Mines, Istanbul Technical University, Istanbul, Turkey,

SUMMARY
Earthquakes of tectonic or magmatic intrusion origin around a geothermal field sometimes inflict irreversible
damage to the caprock and cause temperature drop in reservoir temperature by altering the integrity of thecap rock structure. In a healthy reservoir, intact caprock seals the reservoir, maintains temperature andpressure build up for better well yield, and prevents cold water of overlain aquifers entering the system. The
integrity of the caprock with these characteristics ensures the long-term preservation of a reservoir. However,loss of caprock integrity or weaken sealing capacity leads to leakage in and around the reservoir and reduces
the sustainability and economic feasibility of a geothermal reservoir. Therefore, the geometry and integrity of
the caprock are important, and any means of inspecting a caprock integrity is valuable for sustainable
exploitation of a geothermal reservoir.
Caprock is usually identified with its striking conductive nature, which occurs because of mineral alteration andhigh clay content. Usually, the thickness of the caprock, which is usually several times less than its depth, isdifficult to discern with the utility of conventional inversion techniques because of the structural complexity
emanating from the non-sealing nature of the caprock. Starting model dependency as required for theconventional inversion methods usually forces the inversion procedure to produce a final model that is trapped
about a local minimum due to the gradient of an objective function that is explicitly evaluated by a numericalmethod. On the other hand, evaluating partial derivatives with respect to the model parameters in some nonlinear mathematical models may be impossible, or approximate schemes may not yield a sensible solution.
This study is n attempt for identifying the integrity of the caprock after the earthquake of Mw=5.3 on January
6, and subsequent swarms until March 26, using magnetotelluric data at Tuzla, Çanakkale geothermal field.
One-dimensional magnetotelluric modeling approach with higher resolution for determining caprock structurecompared to two- or three-dimensional modeling was carried out using particle swarm optimization (PSO) asan alternative to conventional inversion techniques. PSO is one of the modern global optimization methods
based on a metaheuristic approach and has been recently preferred to overcome the subtle difficulties
encountered in modeling geophysical data. Our modeling approach appeared to be successful in determining
the caprock geometry, and the findings further strengthened our conviction that PSO is a powerful modelingtechnique for the MT data obtained on a geothermal field. It is critical that the modeling results provide
important hypotheses for the loss of caprock integrity above a speculative magmatic intrusion, which extends
to the hydrothermal reservoir above non-sealing caprock, and possibly triggers seismic events on the Tuzla
fault.

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Magnetotelluric investigations in the Ubaye valley, Western Alps: a connection
between electrical conductivity, fluids, and earthquakes?
S. Byrdina1, J-L. Got1, L. Metral1, P. Hering2, M. Baques3, L. De Barros3, S. Garambois1, P. Gueguen1,
V. Rath4
1
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, IFSTTAR, ISTerre, Grenoble, France,(svetlana.byrdina@univ-smb.fr)
2
Goethe University Frankfurt, Institute of Geosciences, Frankfurt, Germany, (Hering@geophysik.unifrankfurt.de)3
Université Côte d’Azur, CNRS, Observatoire de la Côte d’Azur, IRD, Géoazur, 250 rue Albert Einstein,
Sophia Antipolis 06560 Valbonne, France, (debarros@geoazur.unice.fr)
4School of Cosmic Physics Dublin Institute for Advanced Studies 5 Merrion Square, Dublin 2, Ireland,(vrath@cp.dias.ie)

SUMMARY
The Ubaye valley is a seismically active region in the Western Alps (France), regularly struck by seismic
swarms, e.g., in 2003–2004 or 2012–2015. While some earthquakes could be associated with known faults,
the character of the observations (high seismicity – low deformation rate) requires complex driving processes
beyond local or regional tectonics. Most conceptual models involve pressurized fluids present down to
depths of several km, and/or long-range transport.
During 2020/21, a data set of 30 MT sites was acquired, covering a signal period ranging between 10 -4 to 104
s. Data quality was generally satisfactory up to 3 s and sometimes up to 100 s. For the 3-D inversion
performed using the ModEM code, we have chosen a joint inversion of induction vectors, phase tensors andoff-diagonal impedances (previously corrected for static shift with help of phase tensor inversion and few
TDEM measurements).
The main findings include (a) a prominent conductor (down to 20
Wm) located along the axis of the swarm
zone, though generally above it; (b) a regional dominance of the Peninic Front in the East and the overridden
Mesozoic (Dauphinoise) sediments in the West; (c) strike directions that agree well with most of the mapped
faults and focal mechanisms of the strongest seismic events.
Our sensitivity reaches the border of the seismic swarm activity, but does not cover its depth extent.
Conceptual models proposed for the origin of the seismic swarm activity will be discussed in the light of the
MT imaging, and the associated uncertainties.

Keywords: magneto-tellurics, pressurized fluids, seismic swarms, Penninic Front, Western Alps

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Two-dimensional electrical resistivity model of Sabalan geothermal field using
Magnetotelluric data
Gholam Abbas Fanaee Kheirabad 1, Behrooz Oskooi 2
1 Mining Department, Birjand University of Technology, Birjand, Iran, afanaee@birjandut.ac.ir
2 Institute of Geophysics, University of Tehran, Tehran, Iran, boskooi@ut.ac.ir

SUMMARY
The Sabalan Magnetotelluric (MT) survey discussed in this paper was carried out in November 2007. MT fieldcomponents in the frequency band 0.002 – 320 Hz were collected at 28 sites at an average distance of 1.4
kilometer. The selected profile with seven MT stations in the region crosses over the hydrothermally alteredzones and perpendicular to the main geological structures. The subsurface resistivity structure was modeled
to assess the size of the geothermal resources and to prepare conceptual model for the hydrology of thegeothermal fluid reservoirs. The MT sites were projected to a line for two dimensional (2-D) modeling. Electricalresistivity and impedance phase models were computed for TM and joint TE and TM mode data. These models
resolved a good correlation between the features of the geothermal field and resistivity distribution at depth.
The resulting models reveal the presence of a resistive cover layer underlain by an anomalous conductive
layer at about 500 -1000 m below the ground surface. A very low resistivity (3 - 5 ohm-m) feature at the depths
below 2000 m, bounded by two more resistive (100 - 500 ohm-m) features that interpreted as the main heatsource of the geothermal system. At shallow depths, the resistivity model obtained from the MT data is
consistent with the general conceptual resistivity model proposed for high-temperature geothermal systems atliteratures.
Keywords: Geothermal exploration, Magnetotelluric, 2-D inversion, Electrical resistivity, Sabalan.

INTRODUCTION
Geothermal resources are ideal targets forelectromagnetic (EM) methods since they produce
strong variations in underground electrical resistivity.
In thermal areas, the electrical resistivity is generally
lower than in areas with colder subsurface
temperature (Oskooi et al., 2005). Magnetotelluric(MT) studies have been conducted over geothermal
systems (Spichak et al., 2009; Heise et al., 2008).
In 1998 a dense grid of 212 MT stations carried out
on the Sabalan area that highlighted its resestivity
structure and the relations between conductive
anomalies and the geothermal reservoir condition(Talebi et al., 2005; Fanaee et al., 2010).
The most productive areas of Sabalan geothermal
field were explored in November 2007. The primaryobjective of this survey was to delineate any
resistivity anomalies that may be associated withhigh temperature geothermal resources. During
recent years Mount Sabalan is considered as the
subject of detailed volcanological, petrological andgeophysical investigations (Oskooi et al., 2016;
Fanaee et al., 2021; Oskooi et al., 2015;
Ghaedrahmati et al., 2013).
Figure 1 show the geological setting in Sabalan
area and the broader geological and geophysicalsettings of the area are described by Bromley et al.
(2000).

Figure 1. Simplified geological map of the study
area (Emami, 1994).

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METHODSThe basic principles of the MT method wereintroduced by Tikhonov (1950) and Cagniard (1953).
The impedance tensor (Z) is defined as the relationin frequency domain between the components ofthe magnetic field B and those of the electric field E
measured at the surface of the earth: E x  1  Z xx=
 E y  µ0  Z yx

Z xy   B x  Z yy   B y (1)indices x and y denoting magnetic North and East,µ0 being the magnetic permeability of vacuum.
Z is usually displayed as apparent resistivity andphase which depend on the angular frequency:

ρa (ω) =1

ωµ0Z ( ω)φ (ω ) = arctan2

Im ( Z (ω ) )Re ( Z (ω ) )

( 2)
( 3)

Simultaneously the variation of the verticalcomponent (z-direction) of the magnetic field is alsomeasured. Various skew parameters were
estimated in order to analyze the dimensionality ofthe data. Swift’s skew defined as the ratio of the onand off diagonal impedance elements, approaches
zero when the medium is 1-D or 2-D (Swift, 1967).
Skew values below 0.2 in majority indicate that the
study area could be approximated to a 2-D structure
geoelectrically. MT sounding curves show a 2-D
effect with a clear separation between the curve withthe electric field parallel to the strike (TE mode) andthe curve related to current circulation normal to thestrike (TM mode). Skew values of the impedance
strike shown in Figure 2 for the data from almost allsites and frequencies.
In cases where MT data display overall 2-D
characteristics despite some 3-D effects, results
obtained using 2-D inversion algorithms can bevalid (Ledo, 2005).

To obtain the subsurface structure along the profile,
2-D inversion code of REBOCC by Siripunvaraporn
and Egbert (2000) was used. The code seeks asmooth model with the minimum amount of features
required by the data. The data points were carefully
selected between frequency ranges 0.002–320 Hz.
The inversion started from a homogeneous half
space with resistivity of 100 ohm-m.
In the inversion, apparent resistivity and phase dataof both TE and TM modes were used. Reasonable
RMS misfit was achieved usually after 10 iterations.
The MT transfer functions along the strike (TE-mode)and orthogonal (TM-mode) directions were inverted
simultaneously, to derive the 2-D subsurface
resistivity distribution. Static shifts were notcorrected at this stage of the work, as we were
missing shallow subsurface information. However,apparent resistivity data on most sites show little
difference between the two polarization curves athigh frequencies and vary smoothly passing fromone site to the next. Several inversions were
performed of the TM and joint TM+TE modes. Since
TM mode typically suffers less 3-D distortion thanTE (Wannamaker et al., 1984), some inversions
took into account only the TM mode data. The
apparent resistivity was down-weighted during theinversion process to help to accommodate thepossible static shift effects in the data. Error floor of
20% for the apparent resistivity and of 5% on thephase were assumed.
RESULTS
The inversion result of TM mode data is shown in
Figure 3, respectively. By these levels of error on thedata, the fit of the computed model response to theobserved data was satisfactory (Figure 4). The
resistivity and layer thickness of the obtainedmodels depend on the mode used for the inversion.
The normalized root mean square (rms) misfit wasaround 3 for obtained models.

C4

C2

C3
R3

R1
C1

Figure 2. Variation of Swift’s skew values(dimensionality factor) for all sites along the profile.

R2

Figure 3. The resistivity model resulting from theinversion of TM mode data. C; conductive, R;
resistive features.

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Figure 4. TM-mode data and model responses ofthe 2-D inversion.
The resulting model and data misfit for joint TE andTM-mode data is shown in Figure 5 and Figure 6.

C3

C2
R3
R1
C1

R2

Figure 5. The resistivity model resulting from thejoint inversion of TM and TE mode data.

Conductive bodies show variable thicknesses alongthe 2D section, passing from a few hundred meters
above R3 to about 3000m at C2. Below these
conductors there is an increase of resistivity withdepth along the whole profile, except under sites 8,
13 and 14, where the resistivity is always lower thana few tens of ohm-m in depth (<10 ohm-m). The
opposite ends of the profile are characterized by ahigh resistivity (1000 ohm-m) structure, whereas in
the middle of the profile the conductive feature, C4,(<10 ohm-m) is followed by a resistive layer R3(>100 ohm-m), which in turn is overlying a very
conductive structure C1 (<5 ohm-m).
The conductive structure is clearly constrained tothe central part of the profile. The uppermost 0.5–4
km of the obtained 2-D resistivity structures is fairly
like the previous results from 1998 MT data
modeling (Fanaee et al., 2011). There is a highly
conductive (<5 ohm-m) structure at depth of about
2.5 km in the middle of the profile. Most naturally
magmatic intrusions acting as a heat source for thegeothermal system, although there are notemperature data to confirm the presence of magmain Sabalan area. Another peculiar feature is the area
beneath sites 8,21 and sites 13,14, where the 2-D
model shows an abrupt transition to moderate
resistivity values down to depths of 2-3 km where
the deep conductive body begins. Most probably, analmost vertical fluidized or altered fault zone
connecting the shallow and deep conductors
together.
CONCLUSIONS

Figure 6. The TE and TM mode data and model
responses from the joint inversion results.
DISCUSSION
In our case study, the inversion provides thevaluable results. The prominent structures in theobtained resistivity models (Figure 3 and Figure 5)
are discussed below:
A resistive layer (>400 ohm-m) is recognized at the
top. At depth 300-1300 m there is a resistive feature(R3) among three separate conductive bodies (C1,C2, C3).

The magnetotelluric method and its ability to map
deep conductive features, can make a valuable rolein the reconnaissance of deep geothermal systems.
A pilot MT survey was carried out in the Sabalanarea, deploying seven broadband sites along anearly 10 km profile. 2-D inversion results show
conductivity models with stable features, identifying
the main geological units. Analysis of the different
skew parameters indicates that the impedances arewell described in terms of 2-D models. Some
deviations from 2-D behavior were noted for thedata of some frequencies and sites where misfits
were relatively large. The final 2-D model was stable
and not depending on the a priori or starting models.
Generally, the main result is that the twodimensional electrical resistivity model is perfectlycomparable to the structures found in literature asconceptual resistivity model of a high-temperaturegeothermal system (Berktold, 1983).
ACKNOWLEDGEMENTS
The authors would like to thank Siripunvaraporn andEgbert for academic use of their REBOCC code.
Also, we are grateful to Renewable Energy
Organization (SUNA) of Iran for preparing the data.

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sounding of geothermal zones, Journal of Appl.
Geophysics, 68 (4), 459-478.
Swift, C.M., (1967) A magnetotelluric investigationof electrical conductivity anomaly in thesouthwestern united states. PhD Thesis
Massachusetts Institute of Technology.
Talebi, B., Khosrawi, K., Ussher, G., (2005) Review
of resistivity surveys from the NW Sabalan
geothermal field, Iran. Proc. World Geothermal
Congress, Antalia, Turkey.
Tikhonov, A.N., (1950) On determining electricalcharacteristics of the deep layers of the Earth.
Doklady Akademii Sel'skohozaÃjstvennyh Nauk73, 295–297.
Wannamaker, P.E., Hohmann, G., Ward, S., (1984)
Magnetotelluric responses of three-dimensional
bodies in layered earths. Geophysics 49, 15171533.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Analysis of Geothermal Manifestation Distribution at Blawan-Ijen Geopark, East
Java, Indonesia based on Magnetotelluric and Gravity Data for Determining the
Recommendation of PLTP Location
Ibrahim, A1*, Hapsoro, C., A.,1 and Zulaikah S.1
1 Astronomy and Geophysics Research Group, Department of Physics, Faculty of Mathematics and Natural

Sciences, Universitas Negeri Malang, Jl. Semarang 5 Malang 65145 East Java, Indonesia,
alpanibrahim33@gmail.com
1cahyo.ajihapsoro.fmipa@um.ac.id
1siti.zulaikah.fmipa@um.ac.id

SUMMARY
Magnetotelluric (MT) and gravity investigations have been carried out at the Blawan-Ijen Geopark. BlawanIjen Geopark is located in Bondowoso-Banyuwangi, East Java, Indonesia. This investigation aims to find thesubsurface resistivity structure of the area that may be correlated with geothermal in the area of unknown
geothermal potential. MT surveys have been carried out in the area using a magnetometer located 3-5 km
from the survey area. On the other hand, a gravity survey has been carried out at the same location as the MT
survey site. The results of gravity data interpretation are used to support or check the results of MT datainterpretation. The result of interpretation of MT and gravity data shows that the subsurface structure of thearea correlates with the structure of the geothermal reservoir. The emergence of the Blawan fault is a way for
geothermal fluid to flow to the earth's surface in the form of gas or steam. The distribution flow of Blawan's
geothermal manifestations originates from the caldera of the older Ijen Mountains which spreads towards theKendeng Mountains. It is also known that the geothermal manifestations of Blawan follow the flow of the Banyu
Pahit river as evidenced by several hot springs along the river.
Keywords: Magnetotelluric, Gravity, Blawan-Ijen Geopark, Geothermal

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Interpretation and modeling of airborne and ground magnetometry data for ageothermal reservoir in the Abgarm region of Mahallat in Iran
Behrooz Oskooi1,2*, Andreas Junge1 ,Seyed Hossein Hosseini2, Banafsheh Habibian Dehkordi2 and Seyed
Masoud Ghiasi2
1

Institute of geosciences, Goethe University, Frankfurt, Germany

2

Institute of Geophysics, University of Tehran
boskooi@ut.ac.ir, junge@geophysik.uni-frankfurt.de,
Hosseini.hossein@ut.ac.ir, bhabibian@gmail.com, masoud.ghiasi10@alumni.ut.ac.ir

SUMMARY
Abgarm of Mahallat is a well known geothermal region in central Iran on which many geophysical surveys are
already conducted. To validate and compare the results with previous studies on this area, we take theopportunity to analyze the region by using magnetic and aeromagnetic methods simultaneously. Generally,
this study tries to estimate the profound expansion of the leading geothermal anomaly and its subbranches. The form of the anomaly was determined from aeromagnetic and ground magnetic grids, and inorder to investigate the anomalies thoroughly, 3D inverse modeling has been utilized. Using several methods
to estimate the depth of anomaly would approach us to the actual values. Hence, Euler’s depth estimation andpower spectrum depth estimation are applied to the aeromagnetic and magnetic data. As results, theinterpretation of both methods afforded the consonant and comprehensive interpretation that
perfectly accommodates previous studies on this region.
Keywords: Abgarm, Aeromagnetic, Geothermal, Mahallat, Magnetometry, 3D inversion.
INTRODUCTION
The earth is an extreme thermal energy resourcethat its heat is directed to the ground surface byvolcano eruptions and underground water. The heatis continuously transferred to the upper levels byhydrothermal liquids. Hot water through thefractures and porosities emerges as hot water
springs and geysers. A huge part of hot liquids aretrapped in porous rocks and crustal fractures andprovide the natural warm water resources, known
as geothermal resources (Huenges et al. 2010). By
studying geothermal anomalies, it turns out thatsuperficial and groundwaters carry heat to thesurface or shallower areas by penetrating to thedeeper layers; this heat circulation is caused by the
specific gravity of the cold and hot subsurface fluid(Dickson et al. 1995). Mahallat is one of theessential geothermal areas in central Iran. Some of
the geological evidence to back up this claim are
numeroushotsprings,
intrusive
granite,granodiorite on sedimentary beds, and obvious
alterations (Oskooi et al. 2016; Darijani et al 2014;
Geological specifications of Mahallat 2008; Kahak
and Golpayegan geological maps, 1995). Oskooi etal. (2016) have indicated that a structure with a 1.82
structure index shows a vertical cylinder; it is ageothermal spring formed by a significant mass ofmafic or ultramafic intrusive igneous rocks. Oskooi
and Darijani (2013) also confirm that the top of thecylinder anomaly is cone-shaped. In this study,

Abgarm of Mahallat hot springs were explored using
magneticandaeromagnetic
methods
simultaneously. In general, magnetic studies canhelp us study the geothermal resources. Mafic
intrusive bodies are closely associated withmagnetic rocks, which are highly susceptible. This
article locates the geothermal heat source byinterpreting the magnetic and aeromagnetic data
together.
Methodology
By using standard Euler deconvolution, we canestimate the position of the target body. Thisapproach utilizes three orthogonal gradients ofpotential quantities. Three-dimensional Euler’s
equation is shown as below (Reid et al. 1990):(𝑥𝑖 − 𝑥0 )

𝜕∆𝑇𝑖
𝜕𝑥

+ (𝑦𝑖 − 𝑦0)

𝜕∆𝑇𝑖
𝜕𝑦

+ (𝑧𝑖 − 𝑧0 )

𝜕∆𝑇𝑖
𝜕𝑧

=
(1)

−𝑁∆𝑇𝑖

where. 𝑥0 , 𝑦0, 𝑎𝑛𝑑 𝑧0 are the coordinates ofmagnetic source, T is the total magnetic anomaly𝜕 𝜕𝜕and , , 𝑎𝑛𝑑are derivatives in directions of x,
𝜕𝑥 𝜕𝑦

𝜕𝑧

y, and z, respectively. N belongs to the structuralindex (SI) that has been explained in the following.
Using deconvolution can obtain the source anomaly
specifications; because it is a procedure that uses
the output to gain input.
Spectral analysis is a proper approach for
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estimating the average depth of large-scale
magnetic or gravity anomalies that concentrates on
statistical specifications of sources. By taking thenatural logarithm of both sides of azimuthally
averaged power spectral equation, obtains:ln(𝛷 |𝑘|) = ln(𝐴) − 2|𝑘|𝑑 + 2 ln(1 − 𝑒 −|𝑘|𝑡 )(2)where, A is a constant value that depends onmagnetization direction and regional magnetic fields,
and k is wave number. Equation (3) in the highwave-number mediums, belongs to straight-linewith -2d coefficient. approximately. Thus, we canobtain half of angle coefficient, which is estimationof (d) by fitting a straight line on the mediocre andhigh wavenumber section of logarithm power
spectral curvature (Blakely 1995).
Li and Oldenburg (1996, 1998) provided
algorithms for inverse modeling of the magnetic andgravity data. Because of the comprehensiveness ofrecommended method, it is one of the useful andclassical methods for this purpose. The following
Equation 3 (the general form of 𝐴𝑥 = 𝐵), is principleof magnetic and gravity inversion modeling as has
been used in this paper (Li and Oldenburg 1994,
1996, 1998):(𝐺 𝑇 𝑊𝑑𝑇 𝑊𝑑 𝐺 + 𝛽𝑊𝑚𝑇 𝑊𝑚 )𝑚 = 𝐺 𝑇 𝑊𝑑𝑇 𝑊𝑑 𝑑𝑜𝑏𝑠 +𝛽𝑊𝑚𝑇 𝑊𝑚 𝑚0(3)

G is 𝑁 × 𝑀 matrix known as data kernel, 𝑑𝑜𝑏𝑠 =[𝑑1 𝑑2 𝑑3 … 𝑑𝑁 ]𝑇 is observed data (magnetic orgravity),𝑚 = [𝑚1 𝑚2 𝑚3 … 𝑚𝑀 ]𝑇ismodelparameter, 𝑊𝑑 is weighted data matrix as diagonal
matrix 𝑊𝑑 = 𝑑𝑖𝑎𝑔[1⁄𝜎1 … 1⁄𝜎𝑁 ] that 𝜎𝑖 is 𝑖th data
standard deviation, 𝑚0 is geological reference
model, and 𝑊𝑚 is a matrix for weight factor for
calculating 𝑚 length.
Geological Setting
Mahallat is located in the central volcanic zone of
Iran. This zone has been active through thecenturies and looks like a triangle in central Iran.
The western boundary of Mahallat is limited to The
Sanandaj-Sirjan metamorphic zone. The central
zone of Iran is restricted to Alborz and Makran zone
fromNorthern and southern boundaries,
respectively, and in the eastern border of thementioned zone can observe the Lut block. Minor
superficial-shearing erosion of the zone isnoticeable. Due to the extension of dolomite andlimestone units and their fractures, the zone
illustrates a good permeability. As mentioned above,
The Eocene sediments consist of conglomerates
that emerge in the southern section of the zone ashills. The most critical fault in the area is the
Mahallat fault with an east-west extension. This fault
near Delijan finds northeast-southwest, then northsouth direction. The outcropped formations of thearea are as follows: Shemshak formation (withJurassic shale and limestone lithology), cretaceous
Orbitolina limestone, Qom formation (with Marly Limestone lithology), and igneous granodiorite hasformed in the vicinity of sedimentary rocks (Hosseini
et al. 2020).
Results
In order to investigate the Mahallat geothermal zone
generally, which is a large-scale area, aeromagnetic
data of the mentioned area have been utilized. The
altitude of the flight is 400 to 500 meters with a flightspacing of 7.5 kilometers. The dimension of theregion is 18800 × 15500 𝑚2 and the residual
magnetic map's minimum and maximum magnetic
intensity are 18.8 and 525.8 nT, respectively. The
lack of a negative value for the magnetic intensity
states that the bottom part of the anomaly could be
profound, therefore, the surveying cannot detect thenegative pole of the anomaly. The RTP filter (Figure1) was exerted to simplify the interpretation andincrease the resemblance of the map and the actual
shape of the anomaly. The shape of the anomaly in
the RTP filter is very similar to the shape of the massin inverse modeling, shown in the following. In order
to investigate the sub-branches of the main
anomaly discussed in the aeromagnetic section, aground magnetic survey has been acquired. The
dimension of the surveyed zone is 4660*2600
square meters, and 10000 stations have been used.
Figure 1 indicates the residual aeromagnetic map of
Iran (with 7.5 KM flight spacing) and the studying
area. The red and blue rectangular in the zoom box
picture illustrates the geographical locations of theaeromagnetic and ground magnetometry. In order
to illuminate the main anomaly and sub-branches
anomalies positions, the aeromagnetic and groundmagnetometry RTP grids have been overlayed on
the geographical projection of the surveying zone.
The minimum magnetic field intensity is 47391.3,
and the maximum is 48988.1 nT. The inclination anddeclination angles are 52.4 and 4.4 degrees,
respectively. It is worth noting that the main intrusive
anomaly of the Mahallat, which has been

Figure 1. The residual aeromagnetic grid of Iran(the aeromagnetic and ground magnetometry
surveying zones and their RTP grids have beensuperimposed)
investigated in the aeromagnetic section, is in the

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northeast of the Abgarm area (ground magneticgrid).
Spectral analysis depth estimation is applicable forlarge-scale anomalies (Blakely 1995). Figure 2
shows the power spectrum function of Mahallat
aeromagnetic data.

Figure 2. Depth estimation by power spectrum
function.
As shown in Figure 2, the depth of the top of theanomaly is 1.4 km. This value fits well with previous
studies that have suggested a depth of 1280 meters.
(Oskooi et al., 2014). Figure 3a illustrates thelocated Euler depth estimation for structure index of
2 (cylinder). Conquerors of the solutions
demonstrate the depth of 1000 to 1500 meters and1500 to 2500 meters. In order to estimate thebasement depth, the standard Euler was acquired.
Euler's method depends on the shape of theanomaly that interferes with the differential equation
as the structural index. Due to the area dimension
and the anomaly expansion,400 × 400 𝑚2 window
was considered for depth estimation in its center.
According to the obtained inverse modeling fromground magnetometry data in the following, astructural index of 1 was presumed. Due to the
Figure 3b, three depth ranges have been
considered. White circles indicate 250 meters
depths and lesser, purple circles are related to 250
to 350 meters, and yellow points are more than 350
meters depths representative.
Figure 3. (a) Standard Euler’s depth estimation forstructural index 1 (ground magnetic data), (b)located Euler depth estimation for structure index 2(aeromagnetic data).
Figure 4a indicates 3D inverse modeling, which has
been obtained from the Li-Oldenburg algorithm for
Mahallat aeromagnetic data. The minimum andmaximum of the susceptibility value are 0 and 0.03,
respectively. The upper section of the anomaly hascone-shape geometry, and prior studies in thisregion have provided the same shape in theirmodeling, which confirms the inverse modelingaccuracy (Oskooi et al. 2016). Li-Oldenburg
inversion modeling algorithm also has been utilized
for ground magnetometry survey of Abgarm region.
Figure 4b views the ultimate inverse modelingobtained from the Li-Oldenburg algorithm. The
Minimum and Maximum values of the susceptibility
are 0 and 0.1, respectively. In order to present themodel nicely, a depth of 2000 meters has been
considered.
Discussion and conclusions
This study continues the previous studies in the
Mahallat geothermal region that simultaneously
benefits from a multi-method interpretation. The
proliferation of the studies in this zone facilitatesvalidation by matching the outcomes with priorones. As mentioned before, first, the aeromagnetic
data was investigated. Due to the Mahallat
geothermal expansion, exploitation of the long flightspacing aeromagnetic data would be passable.
Afterward, the power spectrum depth estimationand Euler’s depth estimation have been utilized toobtain the aeromagnetic data anomaly upper depth.
The upper depth of the anomaly in 3D inversion
modeling is 1500 meters approximately in the power
spectrum, depth estimation is 1.4 kilometers, andEuler’s depth estimation is 1000 to 1500 meters. In
the following, the Abgarm zone of the Mahallat
ground magnetometry has been used. The groundmagnetometry surveying was supplementary data
through the main Mahallat geothermal zone. First,
we applied Euler depth estimation on the data, andthe upper sector depth of the anomaly is about 250
meters. In order to study the Abgarm-Mahallat area
perfectly, 3D inverse modeling has been employed.
The upper depth of the modeling shows 250 meters,
which matches Euler’s depth estimation.
The obtained depths in this study is in accordancewith prior studies. For instance, Hosseini (2020),
according to the 2D inverse modeling sections ofMT, the depths of 250 to 1500 meters as
granodiorite lava proposed. In addition, the coolingvolcanic mass (heat source) has been set in thedepth of 3 to 4 kilometers (Hosseini et al. 2020). As
mentioned before, this study is a supplementary
investigation in the Mahallat region. It utilizes
aeromagnetic and ground magnetic data and theirinverse modeling as verification implement, and theresults indicate perfect analogy.
ACKNOWLEDGEMENTS
Authors would like to express their sincere thanks to
the Institute of the Geophysics, University of Tehran
and Goethe university, for all supports and utilities
that were provided freely for fulfilment of thisresearch.
REFERENCES
Blakely R., Potential Theory in Gravity andMagnetic Applications. Cambridge
University Press, 1995.

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Oskooi et al., 2022, Interpretation and modeling of Mahallat Geothermal reservoir

Dickson, M. H., & Fanelli, M. (2013). Geothermalenergy: utilization and technology.
Routledge.
Hosseini, S. H., Habibian Dehkordi, B., Abedi, M.,& Oskooi, B. (2021). Implications for a
Geothermal Reservoir at Abgarm,
Mahallat, Iran: Magnetic andMagnetotelluric Signatures. Natural
Resources Research, 30(1), 259-272.
Huenges, E., & Ledru, P. (Eds.).
(2011). Geothermal energy systems:
exploration, development, and utilization.
John Wiley & Sons.
Oskooi, B., Mirzaei, M., Mohammadi, B.,
Mohammadzadeh-Moghaddam, M., &Ghadimi, F. (2016). Integrated
interpretation of the magnetotelluric andmagnetic data from Mahallat geothermal
field, Iran. Studia Geophysica etGeodaetica, 60(1), 141-161.
Oskooi, B. and Darijani, M. (2014). 2D inversion of
the magnetotelluric data from Mahallat
geothermal field in Iran using finite element

approach. Arabian Journal of
Geosciences, 7(7), 2749-2759.
Oskooi, B., Darijani, M. and Mirzaie, M., 2013.
Investigation of electrical resistivity andgeological structure on the hot springs in
Markazi province of Iran using
magnetotelluric method. Bollettino diGeofisica ed Applicata 54, 245-256.
Reid A.B., Allsop J.M., Granser H., Millet A.J. andSomerton I.W., 1990. Magnetic
interpretation in three dimensions using
Euler deconvolution. Geophysics, 55, 8091.
Li Y. and Oldenburg D. W., 3-D inversion of gravity
data, Geophysics, vol. 63, no. 1, pp. 109119, 1998.
Li Y. and Oldenburg D. W., 3-D inversion ofmagnetic data, Geophysics, vol. 61, no. 2,
pp. 394-408, 1996.

Figure 4. Inversion model of (a) Abgarm magnetometry data, (b) and Mahallat aeromagnetic data .

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

New insight of the hydrothermal system beneath Tolhuaca volcano (South Chile)
revealed by magnetotelluric observations.
M. Pavez 1, D. Diaz 2, V. Goldberg3, H. Brasse4, I. Budach5, G. Kapinos 6 and E. Schill1,7- Institute for Nuclear Waste Disposal, maximiliano.pavez@part ner.kit.edu
2University of Chile - Department of Geophysics
3Karlsruhe Institute of Technology - Institute of Applied Geosciences
4Freie Universität Berlin - Fachrichtung Geophysik
5Geothermie Neubrandenburg GmbH
6 Bundesanstalt für Geowissenschaften und Rohstoffe
7Technical University Darmstadt - Institute of Applied Geosciences

1Karlsruhe Institute of Technology

SUMMARY
On the NW flank of the Tolhuaca volcano an active geothermal system has developed with reservoir
temperatures estimated of 220° - 300°C, characterized by several surface thermal manifestations with
fumaroles, solfataras and hot springs. The interplay between heat-fluid-rock interaction processes isfundamental in the development of geothermal systems . However, the heat source of the geothermal system
are poorly constrained. The work presented here address this issue by studying the electrical properties of theactive Tolhuaca geothermal system in the Andes of southern Chile. Using newly recorded magnetotelluric datain the surrounding of Tolhuaca, we present a 3-D model of electrical resistivity of the crust. We usedtemperature measurements in deep wells and geochemical analyses of borehole fluid samples and new water
samples to constrain present-day fluid conditions. The magnetotelluric model reveals different electricalstructures below the western flank of Tolhuaca. The derived model endorses a previous study that drew theconclusion that there was highly conductive region associated with a low permeability clay-cap directly under
the NW flank of the Tolhuaca volcano. Additionally, the electrical resistivity model shows a shallow conductive
anomaly, ~3km below the Tolhuaca volcano, connected with a sub-vertical anomaly of intermediate resistivity.
As we found no indications of a deep conductor, such as those observed in other high enthalpy geothermal
systems, we conclude that the shallow magmatic deposit that is cooling but still hot enough to provide the heatsource of the geothermal system. This magmatic compartment would have been fed from deep crustal zones
by a sub-vertical dipping basaltic-andesitic mush column, which would act as a preferential pathway for the
ascent of hydrothermal fluids.

Keywords: magnetotellurics, electrical structures, hydrothermal system, geothermal reservoir

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Magnetotelluric imaging of a shallow groundwater system in central Zagros, Iran1

M. Montahaei1,
Institute of geophysics, University of Tehran, Tehran, Iran, mmontaha@ut.ac.ir

SUMMARY
The results of an AMT exploration carried out in the Mond river basin, Bushehr province, southwest Iran arepresented in this study. We aim to find the overall conductivity structure of the geological features controllingthe aquifer in a basin where subsurface structure is little known. Dimensionality analysis of the data was
performed by the phase tensor and the WAL invariants and distortion effects were removed employing theGroom-Baily decomposition method. Distortion corrected data were then modeled by a 2D smooth inversion
approach and the results were further explored by applying sensitivity analysis to find the range of relevant
models representing shallow aquifer system in the study area.
2D smooth inversion resolves a shallow conductive layer, extending throughout the model and a resistive
basement detached by a moderately conductive zone. Another conductive anomaly exists at deep part
beneath the NW of the profile.
A different class of resistivity model was derived using a distinct regularization approach which seeks thesmoothest variation away from a starting model. The result shows the possible brine circulation between
water-filled fractures constituting the conductor in the deep part of the model and the shallow alluvial aquifer
layer.
Keywords: magnetotellurics, electrical conductivity, aquifer, Zagros

INTRODUCTION
Tectono-sedimentary basin evolution controls
groundwater resources in alluvial aquifer systems.
The Sena- Shonbe aquifer system, one of the 64
alluvial aquifers comprising the Mond river basin insouthwest Iran, where only scarce information
about subsurface is available. The Mond river isamong the most important rivers originating in thehigh elevation areas of the Zagros region, generally
flowing NE-SW for ~600 km to the Persian Gulf.
The geology of the Mond river basin is complex andstrongly affected by tectonic events in the Zagros
Fold and Thrust Belt, a subdivision of the Zagros
collision zone.
Here, we investigate an AMT data set obtained
along a profile in Sena- Shonbe aquifer system. We
applied different methods to unravel thedimensionality of the subsurface resistivity
structure and a 2D inversion approach for further
interpretation of the measured AMT data.
Dimensionality and Directionality Analysis
We applied the phase tensor (Caldwel et al., 2004)
and Groom- Baily (McNise and Jones, 2001)
decomposition approaches for dimensionality anddirectionality analysis of the measured data. At
short periods, phase tensor ellipses are circles
representing an electrically layered earth. At longer
periods they represent a quasi 2-D geo-electric
structure.

the orientation of phase tensor ellipses at periods,
where principal phase splits are garter than 3˚ arepresented in figure 2. The results reveal a trend of
about 30˚ for the regional strike direction.
2-D inverse modeling of MT data
Two-dimensional isotropic inversions of distortion
corrected MT data were run using the WingLink
software (Rodi and Mackie, 2001). Figure 3 shows
the result of an inversion constrained to fit within
3.5% of apparent resistivity and 1˚ of phase data forboth TM and TE modes. Model misfits are
acceptable (RMS=1.48) with the general trends ofthe apparent resistivity and phase curves
reproduced at all sites.
In the next step, we applied a-priori inversions tovalidate main features recovered in figure 3. all the
conductive or resistive structures have been
removed from the inversion. The outcomes werethen used as starting models and allowed to evolve
through the inversion procedure, where the closest
model to the starting model was looked for. This
strategy confines the starting district of theinversion and spotlights undetected or poorly
constrained features (Schwalenberg et al., 2002).
The experiment results (Figure 4) suggest that themain features in Figure 3 are data supported andtheir shapes do not change significantly through thesensitivity studies.
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Montahaei, M., 2022, MT imaging of an Aquifer in Zagros

Figure 3. Final two-dimensional model produced
from AMT data along profile A8 in central Zagros.
The model used the TM and TE mode
impedances as well as tipper data and reached
an RMS value of 1.48.

CONCLUSIONSIn the course of this work, we outlined the results ofdimensionality analysis and inversion results of an
MT data set from central Zagros, Iran. The
inversion model contains a thick conductive layer
extending throughout the model, a deep conductor
beneath the NW of the profile. Sensitivity tests
support the existence of the narrow dipping
conductive zone that connects the upper
conductive layer and deeper conductor.

ACKNOWLEDGEMENTS
Figure 1. Pseudosection of phase tensors fromthe AMT survey, filled with the β skew angle.


1.2 10-4

Period [s]4.6 10>3

principal

5

The author wishes to acknowledge Iran Office ofVice-presidency for Science and Technology, Officeof drought, erosion and environmental affairs forkindly providing the field data used in this work.

4

-4

3

2.2 10-3

REFERENCES

2
1

10-2

5
-5

4.6 10-2

-1
-2

0.25

-3
1

3

5

7

9

11

13

15

17

19

-4

Station Number

Figure 2. Pseudosection of phase tensor
azimuths for the AMT survey. Azimuths are
only shown for tensors with a split in theprincipal phases greater than 3˚.

Caldwell, T.G., Bibby, H.M. & Brown, C., 2004. The
magnetotelluric phase tensor, Geophys. J. Int., 158,
457–469.
McNeice, G. W., & Jones, A.G. (2001). Multisite,
multifrequency
tensordecompositionofmagnetotelluric data. Geophysics, 66, 158–173.
Rodi, W., and R. L. Mackie (2001), Nonlinear
conjugate
gradients
algorithm
for
2-D
magnetotelluric inversions, Geophysics, 66, 174–
187.

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Montahaei, M., 2022, MT imaging of an Aquifer in Zagros

Figure 4. Inversion results where the closest to starting model was sought. (a, b) show the starting
models, where the resistors (R1, R2) or the conductors (C2, C3) have been removed, respectively. (c,d) inversion results of (a, b). (e, f) inversion results where tear zones are introduced throughout theregions containing the resistors (R1, R2) and the conductors (C2, C3).

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Large-scale mineral system study in Finland using 3D magnetotellurics
P. K. Mishra1 , J. Kamm1 , C. Patzer1 , U. Autio1 , K. Vaittinen2 , K. Muhumuza3 , M. Yu. Smirnov4 ,G. Hill3 and D-REX WG5
1
Geological Survey of Finland, pankaj.mishra@gtk.fi
2
Boliden Kevitsa Mining OY
3Institute of Geophysics Czech Academy of Sciences, Prague, Czech Republic
4Luleå University of Technology, Sweden

S UMMARY
The study of mineral systems has been demonstrated to be a paradigm shift in mineral potential assessment ofmineral belts in cratonic environments by revealing lower crustal structures associated with metal concentrationand emplacement in the upper-most crust. Magnetotellurics is a powerful tool for imaging these lower crustal
structures and their connections to surficial interest zones. However, it has largely been applied in a twodimensional fashion. In complicated Precambrian environments, 3D magnetotellurics is effective for productionof more stable and less ambiguous conductivity models. Within the framework of the D-REX project we haveexamined three regions across the Fennoscandian shield and here we present results from the Pyhäsalmi
mineral system, a part of the Rahe-Ladoka zone, Finland, on a regional scale and in 3D.

5

D-Rex Working Group (D-REX WG):

Luleå University of Technology (LTU): Maxim Yu. Smirnov, Jirigalatu, Thorkild M. Rasmussen, Tobias Bauer,
Oskar Rydman
Institute of Geophysics Czech Academy of Sciences (IG-CAS): Graham Hill, Kenneth Muhumuza, Svetlana
Kovachikova, Jorge Puente, Nazia Hassan, Nooshin Najafipour
Geological Survey of Finland (GTK): Jochen Kamm, Pankaj Mishra, Cedric Patzer, Uula Autio, Jarkko Jokinen,
Jouni Luukas
Earth Science Institute Slovak Academy of Sciences (ESI-SAS): Jan Vozar , Jozef Madzin, Lenka Ondrasova,
Peter Vajda
Boliden Minerals AB: Tobias Hermansson, Kirsi McGimpsey, Janne Kaukolinna
Boliden Kevitsa Mining OY: Katri Vaittinen
LKAB: Niklas Juhojuntti, Harald Van Den Berg
Geological Survey of Norway (NGU): Sofie Gradmann, Jomar Gellein
Keywords: magnetotellurics, mineral systems, data inversion, Fennoscandia, metal endowments

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Deep mineral exploration using semi-airborne electromagnetics: Investigation of a graphite
depositW. Mörbe1, P. Yogeshwar1, B. Tezkan1, P. Kotowski2, A. Thiede2, M. Becken2, A. Steuer3, H. Petersen3 ,M. Schiffler4, R. Stolz4, R. Rochlitz5, T. Günther5
1University of Cologne, Institute of Geophysics and Meteorology, Germany, moerbe@geo.uni-koeln.de
2University of Münster, Institute of Geophysics, Germany
3Federal Institute for Geosciences and Resources (BGR), Hannover, Germany
4Leibniz-Institute for Photonic Technology (Leibniz-IPHT), Jena, Germany
5Leibniz-Institute of Applied Geophysics (LIAG), Hannover, Germany
The overarching goal of the collaborative project DESMEX-II is the development of methods for efficient
exploration of deposits at great depths (~1000 m). Within the framework of the project, a large scale semiairborne controlled source electromagnetic (CSEM) survey was conducted over a graphite deposit in eastern
Bavaria, Germany. Due to the presence of graphite, an additional focus of this survey is the investigation of
induced polarization effects on electromagnetic data and subsequently the development of suitableinterpretation schemes.
At the ground, several horizontal electrical dipole transmitters with lengths between 1-3 km were deployed,
utilizing a rectangular current function with a base frequency of 11.9 Hz. Sensors installed in a helicopter-towed
bird measure the EM field on flight lines with a dense spacing and within several overlapping flight areas,
covering offsets of several km to the ground-based transmitter. In addition, multi component magnetic as wellas electric field measurements utilizing a low transmitter base frequency were conducted at the ground.
Additional measurements of electric field data inline with the transmitter deliver complimentary information.
After data processing, high quality transfer functions for frequencies between several Hz up to 8 kHz could beobtained.
Here, we will present the concept and first results from the conducted survey, including 2D and 3D inversion
results of the semi-airborne EM dataset using multiple transmitters. The resulting inversion models exhibit
strong conductivity contrasts. Shallow regions of high conductivity can be correlated with the occurrence ofgraphite and agree with high frequency helicopter borne EM measurements. The applicability of performing
2D inversion of single flight line data for interpretation is investigated. Effects of topography are analyzed withsynthetic modelling studies and show a considerable large influence for high frequency data. Obtained groundbased electric field data is evaluated in time and frequency domain and is utilized to identify effects of induced
polarization on the EM dataset and to improve the overall modelling resolution of the semi-airborne CSEM
data.
Keywords: DESMEX, semi-airborne CSEM, 3D Inversion, Induced Polarization

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Abstract, 25th EM Induction Workshop, Kuşadası, Turkey, September 13-19, 2020

The value of full tensor magnetotellurics, gravity and electrical resistivity
tomography for Lithium prospecting. A case study in Argentina.
A. Curcio1, E. Chanampa2, L. M. Cabanillas3 and R. D. Piethe2
1Proingeo SA, acurcio@proingeo.com.ar
2Litica Resources (a Pluspetrol Mining Company), echanampa@litica.com; rpiethe@litica.com
3Independent Consultant, lmcabanillas@gmail.com

SUMMARY
The energy transition drives the energy sector to renewable energy and electrification, being the criticalminerals key players in the industrial development map. They comprise rare earth elements and 35 otherelements including lithium that holds the 60% of its world reserves in the so-called lithium triangle located inArgentina-Bolivia-Chile.
The low electrical resistivity, variations in salt concentrations, low acoustic impedances, and dynamics of thehydrogeological system, makes brine monitoring a complex geophysical exploratory problem. So, the objective
is to find a suitable combination of geophysical techniques that fit the lithium exploration objectives, which are
the characterization of the salt flat in depth, fluid detection, basement delineation, definition of the main
structures and main faults and detection of semi-freshwater aquifers that contribute to its recharge and thatare key to the water balance of the endorheic basin, which has the resource in solution. For this purpose, theevaluation of several prospecting methods in different salt flats was executed, concluding that full tensor
magnetotellurics, electrical resistivity tomography and gravity comprises a toolkit that fit the objectives set. The
results are validated with production and exploration wells and a methodology through pseudo wells will be
discussed. Finally, similarities between lithium and hydrocarbon industry as well the exploration frontiers will
be discussed.
Keywords: Lithium, multiphysis, magnetotellurics, electrical resistivity tomography, gravity.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Whole-lithosphere architecture of a mineral system and signatures of the sources
and pathways of ore-forming fluids
Matthew Joseph Comeau1, Michael Becken2 and Alexey V. Kuvshinov3
1Institut für Geophysik, Universität Münster (WWU), Münster, Germany, matthew.comeau@uni-muenster.de2Institut für Geophysik, Universität Münster (WWU), Münster, Germany, michael.becken@uni-muenster.de3Institute of Geophysics, ETH, Zürich, Switzerland, kuvshinov@erdw.ethz.ch

SUMMARY
Mineral systems can be thought of as a combination of several critical elements, including the wholelithosphere architecture, favorable geodynamic/tectonic events, and fertility. There are open questions
regarding the source of ore-forming fluids, the depth of genesis, and their transportation through the upper
crust to discrete emplacement locations. In this study, we investigate an Au–Cu metal belt located at the marginof an Archean-Paleoproterozoic microcontinent in central Mongolia. We explore three-dimensional models of
the electrical resistivity and shear-wave velocity throughout the lithosphere. Directly beneath the metal belt,
narrow, vertical, finger-like low-resistivity features are imaged within the resistive upper-middle crust and areconnected to a large low-resistivity zone in the lower crust. A broad low-resistivity zone is imaged in thelithospheric mantle, which is well aligned with a zone of low shear-wave velocity. In the upper-middle crust, theresistivity signatures give evidence for ancient pathways of fluids, below the metal belt, constrained bystructure along a tectonic boundary. In the lower lithosphere, the resistivity and velocity signatures areinterpreted to represent a fossil fluid source region. We propose that these signatures were caused by a
combination of factors, particularly those related to refertilization and metasomatism of the lithospheric mantle
by long-lived subduction at the craton margin, and discuss several possibilities.
Keywords: mineral exploration; fertilization; fluid pathways; electrical resistivity; shear-wave velocity

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

3-D Magnetotelluric Forward and Inversion of the Chicontepec oil basin.
O. Avila1, F. Corbo2, and C. Castro3
1Posgraduate, Center of Geosciences-UNAM, oavila@geociencias.unam.mx
2Center of Geosciences-UNAM, fercorbo@geociencias.unam.mx
3Institute of Geosciences, Goethe University Frankfurt, castro@geophysik.uni-frankfurt.de

SUMMARY
According to the National Hydrocarbons Commission (CNH), in the Chicontepec Paleocayon, belonging to theoil province of Tampico – Misantla, that is one of the largest proven and possible oil reserves in Mexico. For
this reason, it is important to improve the geological/structural knowledge in the area to have greater precision
to determine the best areas to be exploited. This Paleocanyon has complex geology comprised of significant
volumes of clay-stone sediments produced by multiple turbidite flow events, forming the reservoir-producingrock.
In this investigation, a 3-D resistivity model was generated from the inversion of 83 broad-spectrum
Magnetotelluric sites (BMT), measured during an average acquisition time of 1 day, distributed along 5 profiles.
The MT stations were acquired in different campaigns since 2019 using Phoenix MTU5-A dataloggers. The
time series were processed following a robust single-site approach using the FFMT software package (Castro
et al., 2020), Furthermore, we included the analysis of the electromagnetic response in surveys close to drilledwells, to constrain the conductivity distributions.
The 3-D inversion started from a homogeneous half-space, including main topographic features within the
inner and outer mesh, and cells with fixed resistivity representing the high conductivity of the Gulf of Mexico.
model was developed in a semi-homogeneous space with the local topography and the proximity to the Gulf
of Mexico. The algorithm applied was ModEM (Egbert and Kelbert, 2012; Kelbert et al. 2014), which follows
an inversion scheme based on nonlinear conjugate gradients (NLCG).
We present a resistivity model which reveals the geometry of the Paleocanyon both in extension and in-depth,
geological structures, and geoelectrical anomalies embedded in the sedimentary deposits that can beassociated with the presence of fluids, oil, and/or gas.
Keywords: Magnetotellurics, Oil, Gas, Chicontepec, Inversion,

Acknowledgements
This investigation was possible with the support of the 1787 CONACYT FC project, and the students thatcollaborated with the fieldwork in the different campaigns.
References
Egbert, G. D., Kelbert, A, 2012. Computational recipes for electromagnetic inverse problems. GeophysicalJournal International, 189(1), 251-267.
Kelbert, A., Meqbel, N., Egbert, G. D., Tandon, K., 2014. ModEM: A modular system for inversion ofelectromagnetic geophysical data. Computers & Geosciences, 66, 40-53.
Castro, C.D., Hering, P. Junge. A., 2020. FFMT: A Matlab-based toolbox for Magnetotellurics (MT). RAUGM
2020 Reunión Mexicana de la Unión Geofísica Mexicana, Guadalajara, México.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Sedimentary copper mineral systems: Large scale resistivity footprints in the
Adelaide Rift Complex, South Australia
B. Kay1, G. Heinson2, K. Brand3, S. Thiel4 and G. Boren5
1University of Adelaide, ben.kay@adelaide.edu.au
2University of Adelaide, graham.heinson@adelaide.edu.au
3Beurea of Meteorology, kate.e.brand01@gmail.com
4Geological Survey of South Australia, stephan.thiel@sa.gov.au
5University of Adelaide, goran.boren@adelaide.edu.au

SUMMARY

A significant challenge for the global mineral exploration industry is to identify the deep signature of worldclass mineral deposits and the conceptual understanding of terrane scale fertility under cover at which mineralsystems operate (Griffin, Begg, & O’Reilly, 2013; Groves & Santosh, 2015; Tassara et al., 2017). The
fundamental caveat of the mineral systems approach is that ore deposits are part of a much larger system,
evident at a variety of temporal and spatial scales (Hronsky & Groves, 2008; McCuaig & Hronsky, 2014). It is
this scale dependency that is fundamentally important in exploration targeting, as the direct detection of ore
deposits at the project scale is very difficult due to upper crustal heterogeneity, but potentially easier to predictat lithospheric scales (Hronsky & Groves, 2008).
In the sedimentary copper mineral system, it’s generally agreed that the first order control is their locationadjacent to failed rift basins and passive margin settings (Hitzman et al., 2010), that typically form during the
breakup of the super-continents. The basinal architecture allows the deposition of oxidised syn-rifted red beds,
sometimes with mafic or bimodal volcanics, which act as the source for the leaching of metals (Hitzman et al.,2010). Post rift marine and lacustrine sediments deposited later can produce areas of contained organic rich
reductants necessary to form a chemical trap for the precipitation of sulphides, sometimes with large lateralextent (Hitzman et al., 2010). Evaporite sequences above the permeable post-rift sediments act as ahydrological seal, and occasionally as a source for downward moving brines through evaporite dissolution,
which allows the possibility of long lasting intra-basinal fluid flow systems within which convective cells candevelop with additional heat (Hitzman et al., 2010).
The Adelaide Rift Complex, itself part of the larger Adelaide Superbasin, is a large Neoproterozoic to middle
Cambrian sedimentary system composing the Adelaide Rift Complex (ARC), the Torrens Hinge Zone, theStuart Shelf, and the Coombalarnie Platform (Lloyd et al., 2020; Preiss, 2000). The stratigraphy of the ARC
comprises five major successive rift cycles, evident by associated faulting, minor volcanism and distinct
depositional sequences (Preiss, 2000; Lloyd et al., 2020), with the development of the ARC commencing as
Laurentia began to rift from Australia-East Antarctica with Rodina.
In this study, we collect broadband MT data from 82 sites across the Northern Mount Lofty Ranges in the
Adelaide Rift Complex. Sites are arranged at 10 km intervals in a rectangular grid covering 100 km N-S and
80 E-W. We supplement our newly collected MT sites with a further 80 long-period MT and broadband MT
sites from previous surveys in the region and invert the entire dataset using a 3D inversion algorithm. The
resulting 3D resistivity model reveals a localised elongated upper crustal conductor (~ 5 Ohm.m) spanningfrom the Burra to Kapunda Copper deposits which is constrained by structural dynamics from basin inversion
during the Delamerian Orogeny. We argue that that this crustal conductor, is essentially the large-scale
footprint of a sedimentary copper mineral system where highly saline fluids were sourced from evaporitic
dissolution from the Callana Group strata.

Keywords: Sedimentary copper mineral systems, camp-scale, mineral exploration

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Crustal geoelectrical distribution of Kalgoorlie gold camp mineral system (Western
Australia)
1,2

2

P. Piña-Varas , M.C. Dentith
Department Dinàmica de la Terra i de l'Oceà, Universitat de Barcelona, Barcelona, Spain
2
Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, 35 Stirling
Highway, Crawley, Western Australia 6009.
1

SUMMARY
The East Yilgarn craton (Western Australia) is a highly mineralized area that contains several large gold
deposits, including the giant Kalgoorlie gold camp. Structural controls on gold deposition have resulted in thegeneral acceptance in the Yilgarn craton covered by the concept of the “Mineral Systems”, according to which
major faults between the fundamental crustal blocks are expected to control the movement of metal-bearing
fluids within the system. For mapping such deep structures under cover, deep penetrating geophysicalmethods are required. Here we present the preliminary results of the broadband magnetotelluric experiment
performed across the world class Kalgoorlie gold camp. The survey, with more than 100 MT sites with 5 km
spacing, encompasses important orogenic gold deposits including the Golden Mile/Mt Charlotte, Kanowna
Belle and Paddington. Data analysis shows a complicated 3D geoelectrical structure and therefore,
impedance and tipper data were inverted accordingly using the modEM code. The resulting crustal
conductivity distribution shows a series of conductive zones associated with known orogenic gold deposits.
These zones appear to merge at depth and link to the conductive zone in the lower crust that trends oblique tothe surface geology. The data suggest that orogenic gold mineral systems can be mapped into the deep crustusing MT methods; and such surveys represent a valuable tool for exploring at the district scale for suchdeposits.

Keywords: 3D Magnetotelluric, Mineral system, Mineral exploration, Western Australia

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Geophysical signature of the sedimentary/basement transition zone fromseismic and CSEM. Analysis from a shallow analogue of the Rhine Graben
F. Bretaudeau 1,∗, M. Darnet 1, J. Porté 3 C. Lerouge 1, S. Neeb 1, J.F. Girard 2, J.M. Baltassat 1,
N. Coppo 1, C. Dezayes 1
1BRGM (French Geological Survey) f.bretaudeau@brgm.fr
2 Strasbourg University, ITES-CNRS, UMR7063, France
3 Formerly 1 & 2, now at SINTEF Industry

SUMMARY
Exploiting high temperatures geothermal resources in sedimentary and basement rocks, rifts or flexural basinsto produce electricity is now possible because of the development of binary geothermal power plant technology.
However it remains challenging because the presence of fluid and permeability at 4–5 km depth is necessary.
A multi-scale and multi-disciplinary approach to increase our knowledge of the transition zone between thesedimentary cover and the basement have been undertaken to provide fundamental knowledge for theassessment of its geothermal potential. We report out the results of a study performed on an exhumed
transition zone in the Ringelbach area in the Vosges Mountain, on the flank of the Rhine graben. In thisanalogue of a deeply buried transition zone of the Rhine Graben, Triassic sandstones are still present on the
top of the fractured and altered granitic basement providing the unique opportunity to study in-situ the physicalproperties of this transition zone. We focused here on both electrical (from CSEM) and seismic properties of
the transition zone as they are the main physical parameters usually assessed with the help of geophysicalmethods during the exploration phase of a geothermal project. We show that altered porous and potentiallypermeable granite targeted in deep geothermal exploration has a clear signature on both electrical conductivity
and seismic measurements that can be measured at core scale, borehole scale, and are still visible withsurface geophysical methods such as refraction and reflection seismic and Controlled Source EM at a few
hundred meter depth. The results suggest that best discrimination between permeable and non permeable
rocks may be provided by the joint interpretation of both resistivity and seismic velocities.
Keywords: Geothermal exploration in sedimentary basins, CSEM, transition zone, multi-physics / multi-scale

Figure 2: Seismic refraction velocity model and shallow
seismic reflection showing 1) reflection on the top of theunaltered granite, 2) very large velocity variations
associated to level of alteration and correlated with the
known faults.

Figure 1: 3D CSEM resistivity model showing 1) a
200m thick conductive layer above the fresh granite
associated with granite alteration, 2) very large
variations of alteration marked by blocks with very highvariation of resistivity.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Combined 3D inversion of MT and CSEM-data from Malmberget northern Sweden.
O. Rydman1, M.Yu. Smirnov2, and H.V.D. Berg3 N. Juhojuntti4

1Luleå university of technology, Sweden, oskar.rydman@ltu.se

2Luleå university of technology, Sweden, maxim.smirnov@ltu.se
3LKAB, Sweden, harald.van.den.berg@lkab.com
4LKAB, Sweden, niklas.juhojuntti@lkab.com

SUMMARY
During 2020-2022 both MT and CSEM surveys have been conducted around Malmberget in northern Sweden.
Malmberget is the location of one of Europe’s largest underground iron mines with iron oxide-apatite ore andmafic-intermediate metavolcanic host rock. In the area two different CSEM surveys were conducted, one in
the summer of 2021 with a 4km long grounded dipole source and 150 measurements with 250m spacing in a
3x4km2 rectangle, and another one done in the winter-spring of 2022 with a 1x1km2 loop source with 100
measurements with 250m spacing in a 2x2 km2 area. In the 2021 summer survey all five field components
where measured (Ex,Ey,Hx,Hy,Hz) but in the 2022 winter-spring survey only the magnetic fields (Hx,Hy,Hz)
were acquired due to weather limitations. The Magnetotelluric survey spans the whole area and consists ofroughly 130 1km spaced measurements in a 10x15km2 rectangle. These measurements were done in thesummers of 2020,2021 and some during the winter-spring of 2022. CSEM surveys were processed to estimate
univariate transfer functions between field components and current recording in frequency range 8 – 3000Hz.
This approximates to a depth of investigation of about one to two km. The data are jointly inverted using theMR3Dmod code and the results compared to single inversions and a 3D geological model. The surveys wereconducted in cooperation between Loussavaara-Kiirunavaara Aktiebolag (LKAB) and Luleå university oftechnology (LTU) and are part of two different projects. One focusing on Common Earth modelling (CEM) on
deposit scale and the other being (D-Rex) which focuses on connections between regional and deposit scale
exploration.
Keywords: Magnetotellurics, Controlled source electromagnetics, Combined inversion, 3D inversion,
multiresolution grid

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Experience of the solution of engineering and environmental tasks
using the CSRMT method
A. Saraev1, A. Shlykov2 and B. Tezkan3

1 St. Petersburg State University, a.saraev@spbu.ru

2 St. Petersburg State University, a.shlykov@spbu.ru
3 Cologne University, tezkan@geo.uni-koeln.de

SUMMARY
We have an experience of application of the controlled source radiomagnetotelluric (CSRMT) sounding methodin the solution of engineering and environmental tasks in remote regions. Here the radiomagnetotelluric (RMT)
cannot be successfully applied because of limited possibilities of measurements of radio transmitter’s signals.
As a source in the CSRMT method we use a horizontal electrical dipole, which ensures measurements of fieldcomponents in a frequency range of 1-1000 kHz at a significant distance (up to 3–4 km) from the source. The
extended frequency range of the CSRMT method compare to the RMT one (10-1000 kHz, depth ofinvestigations up to 30-50 m) permit us to study deeper horizons of sections up to 100-150 m. An example ofthe study of a landfill of industrial wastes in the Vyborg area (Leningrad region) using the CSRMT method is
considered. The high conductivity and a relative large thickness of the wastes did not allow us to map thebottom of the landfill by the RMT method. Also, a small amount of signals from radio transmitters reduced theinformative value of the RMT method when studying the top part of the section (first meters). Results of the
CSRMT method show its high efficiency in the study of the top and deep parts of the landfill. An example ofapplication of the CSRMT method is obtained in the area of a railway bridge construction in the Northern partof Siberia (Yamalo-Nenets region). The aim of this survey was the estimation of the depth of solid sands
serving as a basement for bridge piles. The resistivity model derived from the CSRMT data shown a goodcorrelation with the existing boreholes information. The presented results were obtained with the support of
the Russian Science Foundation, project No 21-47-04401.
Keywords: Controlled source radiomagnetotellurics, Waste site, Bridge construction

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Joint 3D inversion of nearshore and land MT and CSEM data in coastal areas of
volcanic islands: application to the Bouillante geothermal field
S. Védrine1,2, P. Tarits2, F. Bretaudeau1, S. Hautot3, M. Darnet1
1BRGM, 3 Av. Claude Guillemin, Orléans 45060, France, s.vedrine@brgm.fr
2Laboratoire GEO-OCEAN UMR6538, UBO, IUEM, Rue Dumont d'Urville, 29280, Plouzané, France,

pascal.tarits@univ-brest.fr
1BRGM, 3 Av. Claude Guillemin, Orléans 45060, France, f.bretaudeau@brgm.fr
3IMAGIR Sarl, 1 rue des Ateliers F, 29290 Saint Renan, France, sophie.hautot@imagir.eu
1BRGM, 3 Av. Claude Guillemin, Orléans 45060, France, m.darnet@brgm.fr

SUMMARY
A large-scale magnetotelluric (MT) and controlled-source electromagnetic (CSEM) acquisition campaign was
conducted around the exploited Bouillante geothermal field in Guadeloupe in the Lesser Antilles,
supplemented by a regional airborne transient electromagnetic survey (AEM). The target is thecharacterization of the geothermal reservoir and the associated clay cap, generally identified by its highly
conductive electrical signature, typical of a conventional volcanic geothermal system in an andesite-type
geological environment. MT allows targeting the deepest parts of the subsurface, but the natural signal on
which it relies is on average weak at low latitudes and can be much weaker than anthropogenic noise, making
its use with dense acquisition challenging close to inhabited areas. CSEM allows completing and densifying
the data coverage in the most urbanized areas thanks to the use of high power active current sources and lessdemanding measurement systems. Moreover, MT and CSEM 3D modeling, as well as inversion, in the coastal
areas of volcanic islands can be challenging due to numerical errors induced by the presence of the sea/landinterface and large variations in nearshore bathymetry and topography. These errors must be estimated andcorrected to best invert the field data. Here we present the workflow and results of the 3D CSEM and MT
modeling and inversion independently. The resistivity model obtained with CSEM is consistent with the known
geology and the 3D MT model, but provides improved resolution as well as new information on the extensionof the reservoir nearshore. We also discuss the complementarity between MT and CSEM in this context, andhow to perform their joint inversion in order to benefit from the high resolution and sensitivity of CSEM on thefirst 2 km and the ability of MT to provide deeper information.
Keywords: Joint 3D inversion, Volcanic islands, Geothermal exploration, MT, CSEMINTRODUCTION

French West Indies.
METHODS

3D exploration with electromagnetic methods incoastal areas of volcanic islands can be challenging
due to the high level of anthropogenic noise,
proximity to the sea/land interface, strong variations
in near-shore bathymetry and topography, andnear-surface heterogeneity. To overcome theselimitations, our goal is the multi-scale integration of
AEM, nearshore and land MT and CSEM to improve
the reconstruction of deep geologic structures in 3D
by inversion. The contribution of the CSEM method
is a key to overcome anthropogenic noise andincrease data coverage over urbanized areas while
the AEM method aims to constrain the first 200
meters of our model. Finally, MT can reach the
deepest part of the subsurface. In order to study
how to integrate the different EM data, we apply our
methodology to data from a geothermal exploration
campaign carried out around the Bouillante
geothermal field operated in Guadeloupe, in the

The nearshore and land MT and CSEM acquisition
campaign was conducted in the coastal area of
Bouillante, near the geothermal power plant. The
acquisition setup is shown in Figure 1 with: land
CSEM transmitters, nearshore and land CSEM
receivers, nearshore and land MT sites and AEM
flight lines. In total, during the 2 weeks ofacquisition, 5 CSEM transmitters, 70 CSEM
receivers and 21 MT sites were deployed in thefield. In addition, we obtained persmission to use
MT data acquired by ORMAT (Owens et al. 2018)
for inversion increasing the number of MT sites by
54. This dataset is very dense and diverse and also
provides a unique opportunity to develop our multimethod integration of MT, CSEM and AEM.

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Vedrine, S. et al., 2022, Joint 3D inversion of coastal MT and CSEM data in volcanic islands

12 GN iterations among with bathymetry and the
topographic relief map, the ”Plateau” fault(Calcagno et al. 2012), and two geothermal wells.
This first result is in good agreement with both theprevious MT 3D inversion from ORMAT and our new
3D MT model. A clay cap is inferred on the shallow
part of the model. A low resistive zone is inferredalong the ”Plateau” fault crossed by a production
well. A resistive structure is inferred not far north ofthe production well, and remains to be interpreted.
Finally, from -2000 m below sea level, a low resistivezone is inferred that could be related to thetemperature increase, but a sensitivity study
remains to be done to confirm its existence.
Although this result is interesting, it should be
improved by combining it with the new MT 3D
inversion which includes new nearshore marine
sites and with the AEM logs.
DISCUSSION

Figure 1. Bouillante survey area. land CSEM
transmitters in red lines, land CSEM receivers in red
triangles, land MT sites in green and nearshore
CSEM receivers and MT sites in yellow. Projected
UTM 20 N, WGS 84.
The 3D modeling of CSEM is done with a mixed
finite element and finite difference approach using
the open-source code custEM (Rochlitz et al. 2019),
and POLYEM3D (Bretaudeau et al. 2021)
respectively. The 3D inversion of CSEM data isdone with the inversion code POLYEM3D, whereas
the 3D inversion of MT is done by the code
MINIM3D (Hautot et al. 2007).
RESULTS
We present a first CSEM 3D inversion result. We
used a finite difference modeling grid of about 1.4
million cells. We inverted the real and imaginary partof the electric field in the north and east direction for
5 land transmitters, 70 nearshore and land receivers
and for 6 frequencies (between 32 s and 32 Hz). A
total of 81,000 parameters were inverted using theGuauss Newton minimization algorithm and using ajacobi-like preconditoner to avoid sensitivity buildup
around transmitters and receivers and ensure
convergence to a smooth solution. The starting
model was an isotropic homogeneous medium of 20
Ω.m incorporating nearshore bathymetry variations
with seawater fixed at 0.25 Ω.m and topography
variations. Figure 2 shows the inverted model after

The methodology for joint inversion of MT and
CSEM data can be based on several strategies,
such as cooperative, successive or joint inversion.
Choosing one or the other inverted model as a prioror starting model to constrain the inversion of thother method is key to improving the physical andgeological consistency of our final inverted
resistivity model. A pitfall could be to perform a joint
inversion from a blank starting model without further
thought, and face minimization problems because
MT and CSEM, although based on the same
physics and inverting the same parameter, havedifferent resolutions and sensitivities to subsurface
anomalies. In the coastal areas of volcanic islands,
one may choose to first constrain the deep
geological structures by performing a MT inversion,and then add another layer of refinement by
inverting the CSEM data. On the other hand, the MT
inversion may benefit from the AEM and CSEM datato constrain the near surface and intermediate
depth. Ultimately we plan to perform a joint inversionof the MT and CSEM data with the prior constraintof the AEM data, and adapt the relative weight ofeither method during the inversion process.
CONCLUSIONS
We have shown that CSEM can lead to results ingood agreement with previous investigations of thearea, and furthermore highlight new features thatstill need to be rigorously interpreted geologically.
This dataset is a unique opportunity to develop amulti-method integration and define the best joint
inversion methodology between MT, CSEM and
AEM. We will likely begin by performing successive
inversion tests to evaluate the best case scenario of

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Vedrine, S. et al., 2022, Joint 3D inversion of coastal MT and CSEM data in volcanic islands

whether MT or CSEM should be inverted first. Then,we will try to adapt our inversion code to perform
joint inversion with adaptive weights between bothmethods.
ACKNOWLEDGEMENTS
This work was conducted under the TEC project
funded by the European program Interreg
Caribbean. We thank our partners ADEME, OECS,
Region Guadeloupe, MAPPEM Geophysics and thecomputing center occigen of CINES. We particularly
thank ORMAT for allowing us to use their MT data.
REFERENCES
Bretaudeau F., Dubois F., Bissavetsy Kassa S. G.,
Coppo N., Wawrzyniak P., & Darnet M. (2021).
Time-lapse resistivity imaging: CSEM-data 3-D
double-difference inversion and application to the
Reykjanes geothermal field. Geophysical JournalInternational, 226(3), 1764-1782.
Calcagno P., Bouchot V., Thinon I., & Bourgine B.
(2012). A new 3D fault model of the Bouillante
geothermal province combining land and offshore
structural knowledge (French West Indies).
Tectonophysics, 526, 185-195.
Hautot S., Single R.T., Watson J., Harrop N.,
Jerram D.A., Tarits P., Whaler K., & Dawe D.
(2007). 3-D magnetotelluric inversion and model
validation with gravity data for the investigation offlood basalts and associated volcanic rifted
margins. Geophysical Journal International, 170(3),1418-1430.
Owens L., Perkin D., & Garanzini S. (2018). 3D MT
Characterization of the Bouillante Geothermal
Resource.
Rochlitz R., Skibbe N., & Günther T. (2019).
custEM: Customizable finite-element simulation ofcomplex controlled-source electromagnetic
datacustEM: customizable 3D CSEM modeling.
Geophysics, 84(2), F17-F33

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Vedrine, S. et al., 2022, Joint 3D inversion of coastal MT and CSEM data in volcanic islands

Figure 2. 3D CSEM inversion result after 12 GN iterations, warm colors correspond to low resistivity areasand cold colors to more resistive areas. Variations in bathymetry and topography are shown on top of themodel. Two wells of the Bouillante geothermal plant are shown in white. The "Plateau" fault is shown in graytransparency (Calcagno et al. 2012). Projected UTM 20 N, WGS 84.

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

CSEM monitoring in Izu-Oshima volcano, Japan
Takao Koyama1 and Makoto Uyeshima2
1Earthquake Research Institute, The University of Tokyo, tkoyama@eri.u-tokyo.ac.jp
2Earthquake Research Institute, The University of Tokyo, uyeshima@eri.u-tokyo.ac.jp

SUMMARY
Izu-Oshima volcano is one of the most active volcanos in Japan. The most recent eruption occurred in 1986,
and the next event is expected in the near future. A monitoring of the volcanic activity is important to catch aprecursory signal for mitigation of the volcanic disaster. A CSEM monitoring is carried on in Izu-Oshima caldera
in order to detect the electrical conductivity change beneath the central cone. Two cables were deployed atsouth and east in the caldera floor to inject the electrical current into the ground. The length of south and west
cables is 650 m and 1,700m, respectively. Five receivers, vertical induction coils, were installed on the rim of
the central cone. The measurements are done by injecting a 50% duty cycle alternating current of one-second
period for one hour every night. Data analysis and some three-dimensional modeling reveals that the electricalconductivity model beneath Izu-Oshima caldera, which is the two-layer structure with a 1 Ωm conductive
column beneath the central cone. The top unsaturated layer is as resistive as 200 Ωm and the bottom saturated
layer is 50 Ωm. Running spectrum at five stations shows that every in-phase of the response function between
an injection current and observed vertical magnetic field is almost stable, and out-of-phase has a remarkableannual change with the same polarity in all of sites. This change cannot be explained by the volcanic activity,
but by a change of elevation of the boundary of the top and bottom layer beneath the caldera. It indicates it
may be due to the annual variation of precipitation. If the conductive column rises up, that is, the volcanic fluid
approaches to the surface before the next event, the changes of the response must show the opposite polarityof the in-phase data at some stations.

Keywords: Izu-Oshima volcano, CSEM monitoring, annual variation

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Magnetotelluric Images of Volcanic Zones in NE Japan Arc andCo-seismic Deformations during the 2011 Tohoku M9 Earthquake
Masuda, S.1, Ogawa, Y.1,2 and Ichiki M.3
1Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Japan
2Volcanic Fluid Research Center, Tokyo Institute of Technology, Japan
3Research Center for Prediction of Earthquakes and Volcanic Eruptions, Tohoku University, Japan

We have compiled wide-band magnetotelluric data collected at 590 sites over the last 30
years in NE-Japan. The period ranges are from 3ms to 2000s. Previous studies havefocused on specific volcanic or seismic zones and had restrictions such as twodimensional modeling methodology or three-dimensional modeling with 20kmx20km scale
limitations. After compilating all the datasets, we have found strong current-channeling due
to the elongated distributions of thick sedimentary layers, which introduce high threedimensionality data around them. We identified such strong channeling by the induced
electric field distribution and induction vectors for hypothetical event analyses using Hxand Hy inducting fields. This nature of the dataset requires complete three-dimensionalmodeling incorporating a large survey area with a 100 km x 100km scale.

We have used 410 sites in the central part of the survey area and inverted the resistivity
structure from the full impedances and induction vectors in 8 periods using WSINV3DMT
code. The most significant feature is the distribution of electrical conductors below 20 km
along the volcanic arc, which runs in the SSW-NNE directions with a 10 km width. Its
extension to the uppermost upper mantle depth shows resistivity of less than ten ohmm.
These conductors get shallower toward Quaternary volcanoes as plume-like conduits,
implying magmatic melt with potentially saline fluids in their upper part.

The distribution of those anomalous conductors at the volcanic zones has good spatial
correlations to the co-seismic crustal depressions of the M9 Tohoku earthquake in 2011
inferred by InSAR (Takada and Fukushima, 2013).

Keywords: magnetotellurics, 3D modeling, volcano, deformation

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256
Abstract, 25th EM Induction Workshop, Kuşadası, Turkey, September 13-19, 2020

First experience with high power EM towards the energy transition
K. Strack 1, S. Davydycheva 1, T. Hanstein 1, Y. Martinez 1, A.Y. Paembonam 1,2, V. Pronenko 3, M. Smirnov
1,4, P. Soupios 5, X. Xu 1
1
KMS Technologies, Houston, Texas, USA
2
Sumatera Institute of Technology, Indonesia
3
Lemi LLC, Lviv, Ukraine
4
Lulea Technical University, Sweden
5
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

SUMMARY
During the energy transition, key applications for electromagnetics (EM) (including magnetotellurics - MT andcontrolled source electromagnetics - CSEM) are CO2 monitoring, geothermal exploration and monitoring,enhanced oil recovery (EOR & EOR+) monitoring, and lithium exploration. The most important of these ismonitoring, which is required to measure time-lapse anomalies in the subsurface to be better than 0.5%. Thisrequires a completely different approach to hardware, survey design, workflow, and interpretation.
To maintain the accuracy and controllability of the hardware, special care must be taken about sensors, thecontinuous recording of transmitter performance, and the monitoring of changes with time and temperature.
We used the system in -200 C as well as +500 C with the entire range included in the system specification.
The survey design usually requires a 3D anisotropic model-based exercise, including on-site noise
measurements, well logs to define the resistivity variation before and after fluid injection, and petrophysicalanalysis. The results from this are optimized receiver spacing and the recording times for the magnetotelluric
and CSEM measurements. Even though it is not immediately included, it also impacts the processing andinterpretation design.
For monitoring CO2, geothermal or hydrocarbon reservoirs, we must always consider that for repeat
measurements, everything could be changed (equipment, environmental noise conditions, and geophysicalprocessing algorithms). Thus, we must minimize the impact of these on any baseline data to the point where
we almost exclusively rely on average statistics.
For effectiveness and efficiency, we usually acquire MT during the night and CSEM during the day unlessweather conditions demand differently. Thus, both methods and their results will be compared to the 3D
anisotropic well log for calibration and they must be consistent within their resolution capabilities. The casehistories from the US and Saudi Arabia used the Cloud to support near real-time quality assurance during theacquisition. We compare the inversion results with the 3D anisotropic log-based model response and include
the seismic horizons. If everything is done correctly, the match allows us to certify even the baseline
measurements based on the 3D model response. This significantly reduces the risk and provides us
confidence in further time-lapse measurements.

Keywords: Controlled source electromagnetics, energy transition, reservoir monitoring, CO2 monitoring

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Multidimensional inversion of transient electromagnetic data
for the exploration of clay pans in the Atacama Desert, Chile

B. Blanco-Arrué a, P. Yogeshwar a, B. Tezkan a, Y. Liu c, R. Peng c, V. Wennrich ba University of Cologne, Institute of Geophysics and Meteorology, Pohligstrasse 3, 50969 Cologne, Germany,b University of Cologne, Institute of Geology and Mineralogy, Zülpicher Str.49a, 50674 Cologne, Germany,c University of Geosciences, Institute of Geophysics and Geomatics, 430074 Wuhan, China.

Summary
The Atacama Desert along the Chilean Coastal Cordillera is a unique landscape to understandthe evolution of the Earth in hyper-arid and arid environments. Clay pans are crucial tounderstand the surface and subsurface processes in areas limited by water availability. This
work aims to provide the sedimentary architecture and bedrock topography of selected claypans through multidimensional inversion and interpretation of loop source transient EM data.
The investigated sites are study areas of the CRC 1211- Earth evolution at the dry limit,
established at the University of Cologne, Bonn and Aachen. In this context, our work
contributes to a better comprehension of the investigated sites.
A total of 116 soundings were carried out on a 3D grid in the Paranal clay pan during December
2019. The TEM data were processed, analyzed, and inverted to investigate the resistivity
distribution of the site. The resulting models show a maximum thickness of about 160 ± 10 m,
interpreted as layers of colluvial and fluvial sediments. The major stratigraphy and thebasement depth were confirmed with a drill core carried out in February this year. The shapeof the conductive sediments suggests the presence of a former channel that might be part of themain drainage network in the surrounding study zone. However, multidimensional effects canlead to misinterpretations of TEM data if the subsurface is not 1D. In those cases, the quasi-2D
resistivity distribution might be influenced. Therefore, a 2D inversion of the TEM data using
the recently developed TEM3DInv code is performed to derive a more accurate geometry of
the clay pan.
The TEM3DInv code is suitable for the time domain and is based on 3D Finite-Volume. The
algorithm uses a Gauss-Newton inversion scheme coupled with a preconditioned conjugate
gradient method to avoid explicit Jacobian calculation in each iteration. The performance andaccuracy were tested using synthetic models that are representative of the PAR clay pan. Since
the code is implemented for 3D, a very large smoothing in the y-direction is used to force a 2D
subsurface reconstruction. Typically, the inversion converges in around six iterations, and thedata is fitted well with a RMS of about 2. The derived 2D models are consistent and resolve
the basement of around ~160 m depth. Thus, the 2D inversion provides an independent andimproved solution compared to the 1D models. Finally, a 3D inversion of the whole 3D grid
TEM shall be derived, although the computational resources are significant.
Keywords: Transient electromagnetic method, clay pans, sedimentary deposits, electricalconductivity.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Characterization of a landfill using magnetotellurics: the Garraf case
A. Martí1, P. Queralt1, A. Marcuello1, J. Ledo2, G. Mitjanas1, P. Piña-Varas1, A. Freixes3, J. Solà3, P. Pons3
Institut Geomodels. Universitat de Barcelona, Spain annamarti@ub.edu
Physics of the Earth and Astrophysics, Faculty of Physics, Universidad Complutense de Madrid, Spain
3
Geoservei Projectes I Gestio Ambiental Sl, Spain
1

2

SUMMARY
Landfills are a highly environmental impact solution to waste disposal. In the Garraf Massif (Catalan Coastal
ranges, SE Spain), a urban waste disposal landfill had been in operation between 1974 and 2006, where morethan 26 milions of metric tonnes were deposited. This landfill was projected over a karstic terrain, which had
important consequences over the underwater circulation system, among others. ERT profiles were acquired
and imaged part of the infill and relevant pipes and man-made impermeabilization layers but were not able topenetrate below the landfill original vase. In 2019 and 2020 we performed a MT study over the landfill and its
surrounding, with the goals of characterizing the electrical resistivity of the infill and imaging the electricalstructures below the original topography previous to the landfill operations. The 2D and 3D resistivity models
confirmed the highly conductive nature of the leachates and allowed us to quantify the depth below which it
had penetrated below the original vase, proving the filtrations produced through the original permeabilizations,enhanced by the karstic rocks.
Keywords: landfill, leachates, electrical resistivity, magnetotellurics

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Abstract, 25th EM Induction Workshop, Çeşme,Turkey,September 11-17, 2022

Corrections for near surface effects contaminating MT data over a salt diaper, NorthWest Kashan, Iran
E. Zare1,
M. Montahaei2,
H. Esmaili Oghaz3

1
2

Institute of geophysics, University of Tehran, Tehran, Iran, elham.zare.99@ut.ac.ir
Institute of geophysics, University of Tehran, Tehran, Iran, mmontaha@ut.ac.ir
3
Natural Iranian Gas Storage Company for Nasr-Abad Area, Tehran, Iran

SUMMARY
In this study, we investigate an MT data set recorded over buried Nasr Abad salt diaper, in Qom Basin, west
Central Iran. Central Iran (CI), Zagros folded belt (ZFB), Sanandaj-Sirjan zone (SSZ), Alborz Mountain Range,
Kope- Dagh mountains, Makran and Sistan-Baluchestan are seven tectonic blocks, originated from
Arabian-Eurasian collision which constitute the Iran plateau. Many majestic salt extrusions (diapers, glaciers(Nmakiers)) in ZFB, Saveh-Qom area and Grate Kavir desert in Central Iran provide a natural laboratory totest different dynamic models of salt flow.
Due to its large dimension, Nasr Abad salt diapir has been selected as an ideal site for gas storage andindustrial waste disposal.
A challenging task arises from small scale conductive bodies distributed at surficial depths which generatedistortion effects contaminating measured MT transfer function. We present here a systematic study ofdimensionality analysis and decomposition of an MT data set, recorded at 25 stations along a profile in NorthWest Kashan, Qom basin, Iran.
The Bahr and WAL invariants confirm regional 1D and 2D structures with local galvanic distortion, at mostperiods. Based on the phase tensor ellipses, we argue that most of the MT data represent regional 1-D and
2-D structures with local galvanic distortions. The Groom Baily decomposition of MT impedance tensors data
reveals approximately period-independent distortion parameters and a set of smoothly varying regional strikedirections. 2-D inductive effects were also retrieved by removing distortion effects from the measured data.
Keywords: magnetotellurics, electrical conductivity, salt dipir, Central Iran

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Current use of Frequency-domain Electromagnetic Induction in precision
agriculture: Knowledge gained from six years of experiments in Portugal
Mohammad Farzamian1,2*, Fernando A. Monteiro Santos2, Nadia Castanheira1, Ana Marta Paz1, Francisco
José Martinez Moreno2, Tiago B. Ramos3, Maria Catarina Paz4, and Maria C. Gonçalves1
1INIAV, Instituto Nacional de Investigação Agrária e Veterinária, Oeiras, Portugal
2Instituto Dom Luiz (IDL), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
3Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
4CIQuiBio, Barreiro School of Technology, Polytechnic Institute of Setúbal, Lavradio, Portugal

SUMMARY
Sustainable Agricultural Intensification aims at reducing the environmental footprint of agricultural production
by promoting a sustainable use of limited resources to limit the use of agrochemicals, salt accumulation, landdegradation and greenhouse gas emissions while increasing productivity and profitability. Such an emphasis
requires efficient field-assessments to evaluate continuously the performance of the implemented
management strategies. Traditional soil sampling methods, which require boreholes for soil sampling andlaboratory analysis, cannot provide a comprehensive answer to this problem as they provide only limited spatial
coverage and may therefore lack representativeness at the management scales. Furthermore, they are highly
time and work consuming, resulting in costly surveys.
Frequency-domain Electromagnetic Induction (FDEM) provide a non-invasive and cost-effective technology
for soil survey and monitoring based on innovative sensors, advanced algorithms for 2D and 3D tomographic
imaging, and new technologies for field surveying allowing to assess soil properties in the management scale.
Measurements obtained using FDEM can be related to several soil attributes, such as soil mineral composition,
clay content, moisture, salinity, organic carbon, and cation exchange capacity (CEC), which are all relevant for
agriculture. This technique provides also an opportunity to monitor the processes relevant for agricultural
production from the analysis of time-dependent change of water content and soil salinization to the study ofsoil–root plant interactions.
During the last six years, we conducted several experiments in different regions of Portugal within two national
and European projects, SOIL4EVER and SALTFREE, evaluating the application of the FDEM method in plot
and field assessments of soil properties. The proposed methodology in these series of experiments consistsof 4 main steps: 1) use of time-lapse FDEM surveys to measure the soil apparent electrical conductivity (ECa)and its changes during the experiment period; 2) inversion of time-lapse ECa data to assess the spatiotemporal
distribution of the soil electrical conductivity (σ); 3) calibration process consisting of a regression between σ
and different soil properties including moisture content, electrical conductivity of the saturated soil paste extract(ECe), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), clay content, CEC, and pH;4) conversion of spatial-temporal distributions of σ into soil properties using calibration equations andproperties that are strongly correlated with σ.
Our results indicate that FDEM can be used as strong tool in precision agriculture to monitor soil salinity, sodicity,and moisture content variability, groundwater fluctuation, and to map soil texture and CEC. A high-precision
determination of soil properties using FDEM is not always straightforward and feasible. This is because σ is acomplex function of several soil properties, which can vary significantly over time and space. The soil salinity
dominates the FDEM signal in wet saline soils, and we were able to assess soil salinity dynamics with highprecision in our experiments. Clay content, CEC, and water content influence the FDEM signal in non-saline
soils, allowing these properties to be predicted.

Acknowledgements
This work was developed in the scope of SOIL4EVER “Sustainable use of soil and water for improving crops
productivity in irrigated areas” project supported by FCT, grant no. PTDC/ASPSOL/28796/2017.
Keywords: Precision agriculture, Soil electrical conductivity, inversion, efficient field assessment

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

An electrical resistivity model of the San Pedro – Ceboruco graben:
3-D inversion studies and comparisons between standard
and advanced Magnetotelluric transfer functions
C. Castro1, A. Junge1, H. Eysteinsson2, P. Hering1, L. González-Castillo3 and L. Ferrari4
1 Goethe-Universität Frankfurt am Main, castro@geophysik.uni-frankfurt.de
1 Goethe-Universität Frankfurt am Main, junge@geophysik.uni-frankfurt.de
1 Goethe-Universität Frankfurt am Main, phhering@geophysik.uni-frankfurt.de2 Reykjavik Geothermal, hjalmar@rg.is
3 Universidad de Granada, lgcastillo@ugr.es
4 Universidad Nacional Autónoma de México, luca@geociencias.unam.mx

SUMMARY
A large set of Magnetotelluric (MT) surveys has been carried out along the Tepic-Zacoalco Rift (TZR) for
geothermal exploration and to determine the deeper electrical conductivity distribution of the subsurface.
The available data set was carefully analyzed and the time series of those low-quality stations werereprocessed using a multivariate robust remote reference approach (Hering, 2019), improving the estimationof the frequency-dependent MT response functions, especially for Tippers.
Here, we perform a dimensionality analysis of the data using the MT Phase Tensor (PT, Caldwell et al., 2004)
and the Complex Apparent Resistivity Tensor (CART, Brown, 2016, Hering et al., 2019). Both parameters havedistinct features and sensitivities, which can be directly related to the conductivity distribution of different
geological units. The comparison highlights the benefits of the interpretation of MT data sets using the novel
CART. A spatially-constant phase split within the PT and coincident small induction vectors were observed
between 101 – 102 period range, followed by a decrease in the phases for the longer periods, associated withan increase of the electrical resistivity.
Subsequently, 110 MT broadband stations were used to derive a high-resolution resistivity model of the San
Pedro-Ceboruco (SPC) graben. 3-D inversion models derived from Impedance tensor and Tipper (Z and T)
are compared to those resulting from PT and CART inversions. The model results reveal improvements for theadvanced MT transfer functions, which result in a better resolution of vertical and horizontal resistivity gradients.
The resistivity model reveals a high conductivity ascent path, which connects the Ceboruco volcanic edifice toa mid-to-lower crust mush zone within the Tepic-Zacoalco Rift. Furthermore, the observed constant phase split
and the small induction arrows coincide with the electrical anisotropy zone previously identified in the Ceboruco
volcano by Hering et al. (2021).
Keywords: Magnetotellurics, Complex Apparent Resistivity Tensor, Electrical Anisotropy

REFERENCES
Caldwell TG, Bibby HM, Brown C (2004). The magnetotelluric phase tensor. Geophys. J. Int. 158, 457–469.
doi: doi.org/10.1111/j.1365-246X.2004.02281.x.
Hering P (2019). Advances in Magnetotelluric Data Processing, Interpretation and Inversion, Illustrated by aThree-dimensional Resistivity Model of the Ceboruco Volcano. PhD Thesis. Goethe-Universität Frankfurt am
Main, Germany.
Hering P, Brown C, Junge A (2019). Magnetotelluric apparent resistivity tensors for improved interpretations
and 3-D inversions. J. Geophys. Res. Solid Earth 124, 7652–7679. doi: 10.1029/2018JB017221.
Hering P, González-Castillo L, Castro C, Junge A, Brown C, Márquez-Ramírez, VH, Pinzón JI, Gutiérrez QJ(2021, in press). Tectonic controls on magmatic systems: Evidence from a three-dimensional anisotropic
electrical resistivity model of Ceboruco Volcano. Journal of Volcanology and Geothermal Research, 107382.
doi: doi.org/10.1016/j.jvolgeores.2021.107382.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The use of the ‘’floating’’ S-plane for effective interpretation of airborne TEM data
V. Hallbauer-Zadorozhnaya1 and E. Stettler1,2
AeroPhysX (Pty) Ltd, South Africa, valeriya.hallbauer@gmail.com
2
University of Witwatersrand, South Africa, stettlere@gmail.com
1

SUMMARY
B-field TEM data due to a pulsing ground loop and collected with a DJI 600Pro hexacopter with MagArrowmagnetometer as well as helicopter borne VTEM dB/dt data was modelled with the ‘floating’ S-Plane
interpretation technique. The former stems from the Flat Mine North copper deposit and the later wasrecorded at the Hondekloof nickel occurrence, both in South Africa. Since interpretation routines for B-Field
recordings are not well known the main objective of our research was to develop a single procedure for theinterpretation of B- and dB/dt airborne TEM data. For the drone survey a transmitter (Tx) loop 1000x800 m2
in size, on the ground and a MagArrow magnetometer below the drone was used as receiver to record boththe magnetic and TEM variations. Soundings were recorded both inside and outside the Tx loop (up to ~500m from all sides) and are 15m apart resulting in a total of about 1000 TEM soundings. After digital separationof the magnetic and electromagnetic field components a modified method for calculating the apparent
longitudinal conductivity S (Stau) was applied. Firstly, the parameters of an equivalent conducting S-planewith longitudinal conductivity at a fixed depth, are determined. Then the theoretical emf curve was calculated
for known parameters of the “floating” plane. To comply with the conditions of the near zone, the calculationof theoretical curves was carried out for the in-loop configuration but simulated by using Tx loops withsmaller dimensions. Then Sas a function of depth H was calculated according to the well-known usualtechnique and converted into a resistivity voxel showing possible deeper ore bodies.
For the VTEM data at Hondekloof the classical method of calculation SH)was used, taking into account theexcess of the transmitter-receiver configuration above the ground. At the site, exposed ore bodies arereflected in zones of increased longitudinal conductivity. Numerous anomalies have been identified, possibly
associated with unexplored reserves of nickel ores.
Keywords: drone, airborne TEM, S-plane copper and nickel deposits

INTRODUCTION
During the first two decades of the 21st century
transient airborne electromagnetic surveying
became widespread. Presently we are witnessing
the development of a new branch ofelectromagnetic exploration with the use of
airborne TEM surveying by drone. Drones, also
referred to as Unmanned Aircraft Systems (UAS),
Unmanned Aerial Vehicles (UAV) or Remotely
Piloted Aircraft (RPA), signify pilotless aircraft. The
UAS technology has several advantages over a
conventional
airborne
platformtocollect
geophysical data such as resolution, accuracy andcost but off-course presently have limited
endurance and weight carrying capabilities. In
comparison to ground surveys a drone can collecthundreds of thousands of sounding points in 2-3
days, weather permitting.
Airborne TEM have brought with them new
problems in processing and interpretation of thedata. Although the primary processing is today
quite well established the modelling algorithms fora 1D inversion for every sounding can still take along time and only visualize along the flight lines.

This is too time consuming an approach to be used
for a quick interpretation. Then heterogeneities in
the upper part of the subsurface section can mask
conductive bodies because changes in flight
altitude can drastically change the amplitude ofsignals. In contrast to drone surveying neither ahelicopter nor a fixed wing can fly at the same
constant altitude above surface and changes inflight altitude can result falsely or in not identifying
a conductive object. All this must be taken into
account
when
creatingageneralinterpretation/modelling algorithm.
We have carried out experimental work using a
50% duty cycle pulsing ground loop and a DJI
600Pro copter with a Geometrics MagArrowmagnetometer suspended below sampling at 1
KHz to collect B-field decay TEM values. This data
as well as dB/dt measurements from a VTEM(GETECH) survey were interpreted with the‘floating’ S-plane method based on a modificationof the Stau (S) technique. The ‘floating’ techniquealso subdivides a subsurface section into a certain
number of layers, but only identifies the conductive
ones together with their depths.
The
specifications
of
the
drone
and
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Hallbauer-Zadorozhnaya V. et al., 2022, The use of the ‘’floating’’ S-planemagnetometers are given by Smit et al. (2022) andare outside the scope of this abstract.
INTERPRETATION VIA MODIFICATION OF THE
CALCULATION OF APPARENT LONGITUDINALCONDUCTANCE S

The so-called method of differential transformationof emf signals into curves of apparent longitudinal
conductance as function of time and depth was
proposed by Sidorov and Tikshaev (1970) The
Smethod has certain advantages. Its use allows
determining the conductance of the section anddetermining the depth where Sincreases
noticeably. Usually increasing conductance isassociated with the presence of a low resistive
object (bodies and/or layers) in the section.
According to Smythe's (1950) regardless of theposition of the observation point at each moment in
time, the amplitude of the potential vector Ax (and,accordingly, Bz and emf e(t), will be equal to theamplitude of a certain “floating” conductive planelying at a depth h with conductivity S equivalent to
the total longitudinal conductivity of the entire
section up to depth h. Dividing up the transmitter
loop in small segments AB we have for a dipole (ora short electric line AB):
Bz=(I/4r2) (1/[r2+4m2]3/2)),(1)e(t)=(IQ4Sr3)(1/[r2+4m2]3/2))=KF(m)/S,(2)

where I is an electrical current (in A), Q is theeffective surface area of the receiver loop (in m2), r
is the distance between of center of AB and theobservation point M, π is themagnetic permeability, z0 is distance between the
S-plane and M, m=z0/2r+h/r+t/Sr, K=IABQ/4r3.
Note that the St method can only be used for the‘near’ zone condition, while with our setup we use
either a big loop around which we integrate along
small segments AB or if use is made of a long
grounded line AB we integrate along the line andas the airborne receiver position Q can be asignificant distance removed from AB it is thenmore likely to correspond to the transition zone
between the ‘near’ and ‘far’ zone.
The parameters of the “floating” plane at eachsounding are determined as follows:
1. After separating the ambient magnetic field andthe TEM signal, the smoothing of the TEM signals
to suppress noise;
2. From eq.2 the emf contains two unknowns S
and m, while Bz contains only m. The
transformation of the emf into Bz is performed bynumerical integration, taking into account theadditional time sequence;
3. Comparing the calculated values Bz withtheoretical ones for the same setup (with fixed h,we get the parameters of the “floating” plane m(t)at each time;
4. Now for a given function m(t) we calculate thetheoretical curve Bz for a new setup, i.e. for a small
loop-in-loop configuration (AB=25 m) located on the
ground (z0=h).
5. Differentiating the theoretical Bz with respect to time
t we obtain e(t)for “floating” plane as a function ofm(t)6. Subsequent smoothing of the emf signals.
7. Transformation of the signals into the S(H),according to (Sidorov V.A. Tikshaev V.V., 1970).
The depth of the “floating” plane can be calibratedusing available drilling data. However many yearsof experience in using the St method shows that inmost cases the empirical formula H(t)=0.5mr canbe applicable.
RESULTS AND DISCUSSIONO’KIEP. RESULTS OF DRONE TEM.
The Flat Mine North (FMN) Cu ore body is one ofmany that constitutes the O'Kiep Cu district inextreme northwest South Africa. The FMN mineclosed in 1995 due to low Cu prizes. Cu bearing
sulphides occur in lumpy noritic intrusives in highly
resistive granitic-gneissic basement material andare characterized by low or reduced resistivity
values for larger Cu orebodies and TEM IP effects
when disseminated. Figure 1 shows a 3D geoelectric model constructed from a TEM survey
measuring the B-field decay and data collectedwith a 1kmx1km single loop transmitter with 75A,
50% duty cycle, 250ms on 250ms off waveform.
The dipole moment was 750 000 Am2. The receiver
was a Geometrics MagArrow magnetometer
hanging 4m below a DJI 600Pro hexacopter
recording at a 1 KHz rate. The B-field data was notconducive to being modelled directly with a platelike algorithm (Barnett,1984, Smit et al.,2022) andconverted from B-field to dB/dt for ‘floating’ SPlane modelling. On the northern side of themodel, the conductive body is dipping as clearlyseen and confirmed by numerous historic
boreholes that discovered the copper body in the
distant past. However the results indicate better
conductors below the previously mined ore andshow that previous drill holes without the foresightof geophysics stopped too short. These better
conductors’ start at a depth of 350m plus and showthat if they are not due to geology containing
graphite could signify much better ore at depth
going down another 350m. It also shows the deep
penetration advantage of recording the B-field.
HONDEKLOOF. RESULTS VTEM USING HELICOPTER.

Historic VTEM data conducted by GETECH at the
Hondekloof Ni occurrence site in extreme central
western SA was made available to us for revaluation of the data. The total number of

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Hallbauer-Zadorozhnaya V. et al., 2022, The use of the ‘’floating’’ S-planesoundings were about 330,000. The sounding
density was 5m along flight lines, 25m between
flight lines. The highly conductive nickel sulphides
occurrences are confined to a ring-sharped
structure surrounding a highly resistive granite–
gneiss dome. Figure 2 shows a 3D resistivity
model (=H/S) where the colour bar displays thelog of the resistivity and. areas of low resistivity are
shown in blue. The massive bodies of Ni sulphides
have a resistivity of 1Ohm.m and less.
Because of clear TEM IP evidence it is believedthat some resistive zones are associated with thepresence of disseminated NiS particles, which arealso of exploration interest. In these sections, theTEM curves are distorted by the effects of induced
polarization.

took 8 hours on an off the shelve i5 laptop (for the
Hondekloof site).
As a result, three-dimensional images of geoelectric sections of the study area wereconstructed by gridding across flight lines. Allavailable boreholes agree with the 3D geo-electric
construct which show the potential of extra ore
beyond that encountered in the boreholes.

CONCLUSIONS

Sidorov VA, Tikshaev VV (1970) Interpretation oftransient electromagnetic signals registered innear zone. Exploration Geophysics, M. Nedra. 42:
pp.45-54 (in Russian).

The data from both the drone B-field survey carriedout at FMN (O’Kiep) and at the Hondekloof sitethat was surveyed according to the classic
helicopter VTEM technique measuring db/dt, themodified Stau method was applied forinterpretation of the TEM data. New potential
reserves of copper at FMN and nickel deposits at
Hondekloof were discovered at two sites in South
Africa. The total number of TEM points in bothsites are 1000 and 330,000, respectively. Full
preparation and calculation of files for visualizationof flight line subsurface sections for 143 profiles

REFERENCES
Barnett CT (1984) Simple inversion of time domain
electromagnetic data. Geophysics 49(7):925–933.
https:// doi. org/ 10. 1190/1. 14417 38

Smit JP., Stettler EH, Price ABW, Schaefer MO,
Schodlok MC, Zhang R (2022) Use of drones inacquiring B-field total-field electromagnetic data formineralexploration.
MineralEconomics
doi:10.1007/s13563-021-00292-1.
Smythe VR 1950 Static and Dynamic Electricity.
McGraw Hill Book Company Inc.

e 1: 3D geo-electrical model, Hondekloof Ni occurrence.

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Hallbauer-Zadorozhnaya V. et al., 2022, The use of the ‘’floating’’ S-plane

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Local to Regional Scale 3D Study around Gällivare, Sweden based on Integration of
3D Magnetotellurics with other Geophysical Data
Jirigalatu1 , Maxim Yu. Smirnov2 , Thorkild M. Rassmussen3 , Oskar Rydman4 , Jan Vozar5 , Tobias Bauer6 ,Jingyu Gao7 , Svetlana Kovachikova8 , Niklas Juhojuntti9 , Tobias Hermansson10 , Kirsi McGimpsey11 , Harald
Van Den Berg12 , Graham Hill13 , Jochen Kamm14 and DREX WG
1Luleå tekniska universitet,jirigalatu.jirigalatu@ltu.se2Luleå tekniska universitet,3Luleå tekniska universitet,4Luleå tekniska universitet,
5
Earth Science Institute of Slovak Academy of Sciences,6Luleå tekniska universitet,
7
Chinese Academy of Sciences,8
Institute of Geophysics of Czech Academy of Sciences,
9
LKAB,
10
BOLIDEN,
11
BOLIDEN,
12
LKAB,
13
Institute of Geophysics of Czech Academy Of Sciences
14
Geological Survey of Finland

S UMMARY
Geologically, Norrbotten has historically been one of the most active mining areas in Europe. Norrbotten hosts
a world-class iron ore mine (in Kiruna), the largest copper mine (in Aitik) in Europe, which is also the largest
gold producer in Sweden, together with other mines scatted in the county. Specially, Gällivare and Kiruna areprobably the most productive mining areas of Fennoscandia. As a result, the areas have been extensively surveyed with mainly passive and non-invasive geophysical techniques such as ground and airborne gravimetry,
aeromagnetics, and magnetotellurics. Therefore, it can be beneficial to understand the mechanism behind for
its extreme enrichment in Norrbotten by using multi-scale multi-physics observations and the knowledge of theexisting mineralization and polymetallic deposits. During summer 2021 regional and local scale magnetotelluric data were collected in Sweden around Gällivare. Area of about 100x100 km2 was covered with averagesite spacing of 5 km. Additional deposit scale measurement around Gällivare and Pyhasalmi were measured
to compliment the regional data set as well as all previously available geophysical data. Extra CSEM data werealso acquired.
Original data are processed and analysed to derive impedance and tipper transfer functions using conventional
robust remote reference processing. We will continue to work with data and apply multivariate techniques which
we expect will improve estimates of transfer functions at problematic sites and provide additional information
to be used in inverse modelling. Overall the data quality is from excellent to good with some few exceptions
related to instrumental and measurements problems.
Keywords: mineral systems, geophysics, magnetotellurics, data processing inversion

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

MTHEK Project: MagnetoTelluric Assessment of the HEKla Volcano
Duygu Kiyan1, Ásdís Benediktsdóttir2, Gylfi Páll Hersir2, Magnús T. Guðmundsson3, Christopher J.
Bean1, Colin Hogg1, Þorsteinn Jónsson3 and Jón Einar Jónsson2
1Dublin Institute for Advanced Studies, duygu@cp.dias.ie
2Iceland GeoSurvey, asdis.benediktsdottir@isor.is
3University of Iceland, mtg@hi.is

SUMMARY
Hekla is one of Iceland’s most active and dangerous volcanoes having had 18 summit eruptions in the last
1,100 years, with the most recent eruption in 2000. Hekla volcano has been studied extensively using various
geodetic methods. The most recent deformation studies (InSAR) in relation to the 2000 eruption of Hekla haveaddressed the proposed location of a deep-seated magma reservoir at approximately 10 km (Sturkell et al.,2013), where other studies indicate a greater depth (Ofeigsson et al., 2011). A regional-scale magnetotelluric(MT) survey (Eysteinsson and Hermance 1985) conducted in 1982 suggested the estimated depths for themagma chamber beneath the volcano to be between 5 and 24 km. The objectives of the MTHEK project areto (1) study the present electrical resistivity structure of the volcano through exploiting deep-probing broadband MT data and (2) identify low-resistivity zones at depth, which may be a proxy for melt accumulation andmigration pathways and thus, may constrain the location of the proposed magma reservoir. The geoelectricalmodels obtained will enable us to highlight potential real-time electromagnetic monitoring locations which may
complement the current real-time seismic/deformation monitoring. In September 2020, broadband MT data
were acquired at 20 stations around and to the south of Hekla volcano. The horizontal electric field components
were recorded using 50-60 m dipoles, and the three components of the magnetic field were measured using
induction coils. MT data were collected for a minimum of 13 hours up to a maximum of 53 hours. A distantremote reference site (approximately 100 km away) was recording during the whole survey. The period rangeof good quality data obtained is about 300 Hz to 1,000 - 2,000 s. In addition, at 17 MT stations central looptransient electromagnetic (TEM) data were collected using a transmitter loop of 200 m × 200 m and a 1 m 2
receiver loop with 100 windings (effective area 100 m 2). The TEM data are used to correct the MT data forstatic shift effects caused by near surface inhomogeneities or steep topography. State of the art dataprocessing and analysis methods were applied to the data. Preliminary inversion models will be presented andprovide a first idea of the resistivity structure of the volcano.
Keywords: Magnetotellurics, Electrical conductivity, Hekla Volcano, Iceland

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Calculating geoelectric fields using a lithospheric resistivity model of the Iberian
Peninsula
R. Hafizi1, A. Martí1, P. Piña-Varas 1, G. Mitjanas1, J. Campanyà2, A. Marcuello1, J. Ledo3, P. Queralt1
1 Institut Geomodels, Dept. Dinàmica de la Terra i de l’Oceà, Universitat de Barcelona, Barcelona, Spain,raha.hafizi@ub.edu
2 Department of Build Environment, South East Technological University, Ireland
3 Physics of the Earth and Astrophysics, Faculty of Physics, Universidad Complutense de Madrid, Spain

SUMMARY

As part of the IBERGIC-CAST (Modelling and forecasting of Geomagnetically Induced
Currents in Spain) multidisciplinary project, funded by the Spanish Research Agency, we
aim at improving the geoelectrical resistivity models of the lithosphere below the Iberian
Peninsula, and use them to create the geoelectric field hazard maps for different
geomagnetic storm scenarios.
In this work we present the modelling of the geoelectric fields using Magnetotelluric transfer
functions computed from measured time series, and calculated from the most recent
resistivity model of the Iberian lithosphere. Three-dimensional inversion of Magnetotelluric
data and forward modeling give us the required impedances for geoelectric calculations for
the Iberian Peninsula. The interstation function analysis also provides information about thedifference of the electrical underground structure between the sites. For the geomagnetic
field, we used data from geomagnetic observatories and hypothetical scenarios. Nearestsite and Spherical Elementary Currents Systems (SECS) were considered to interpolate
geomagnetic fields from magnetic observatories to the sites of interest. The results areanalyzed in views of the underlying geology and the coast.
The outcome of this study can be used for developing hazard maps of the geoelectrical fieldof Iberia for specific events or averaged years.

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Abstract, 25th EM Induction Workshop, Çeşme,Turkey,September 11-17, 2022

An assessment of galvanic distortion effects contaminating MT data from Central
Iran
M. Sajedi1,
M. Montahaei2,
H. Esmaili Oghaz3

1

Institute of geophysics, University of Tehran, Tehran, Iran, mehrdadsajedy13@gmail.com
2
Institute of geophysics, University of Tehran, Tehran, Iran, mmontaha@ut.ac.ir
3
Natural Iranian Gas Storage Company for Nasr-Abad Area, Tehran, Iran

SUMMARY
Salt extrusions (Diapir, dome and glaciers) have a high electrical resistivity contrast with their surrounding
sediments and consequently are good exploration targets for electromagnetic (EM) methods. However,diffusive EM fields, employed in MT exploration technique, have restrictions in resolution and sensitivity andcannot model the earth at the full scale. Furthermore, galvanic distortion effects caused by shallow and small
scale lateral inhomogenities contaminate measured EM fields and cause unreliable imaging of subsurface
electrical resistivity.
In this study, we investigate dimensionality, directionality and distortion analysis of an MT dataset from 35
stations recorded along a profile in Nasr Abad region, west central Iran. The region is composed of five saltdiapirs, developing close to the Abshirin Shurab fault zone which is a dextral strike slip fault with a NNW-SSE
strike direction in the west Central Iran.
Considering the limitations of different dimensionality analysis method, we applied a combined approach,comprising a subsequent use of the WAL invariants, phase tensors and the Groom-Baily (BG) decomposition
methods. At the first step we classified dimensionality and distortion effects at different period bands. Then,for the bands where MT data appear to arise from a superimposed model of 3D/2D structures, we switched to
the GB decomposition method for distortion effect removal and retrieving regional impedances. At the firststep, the application of rotational invariants showed that there is no sign of electrical anisotropy indimensionality pattern and the regional conductivity structure can be considered predominantly as 2D withsome superimposed distortions from local 3D conductivities. 2-D inductive impedance responses were also
retrieved by removing distortion effects from the measured data.
Keywords: magnetotellurics, electrical conductivity, salt diaper, Central Iran

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Towards a new 3D conductivity model of the British Isles:
Revisiting MT data from Isle of Skye, Scotland
Aideliz Montiel-Álvarez1, Juliane Hübert2 and Kathy Whaler3
1School of GeoSciences, The University of Edinburgh, UK, a.montiel@ed.ac.uk
2Geomagnetism, British Geological Survey, UK, jubert@bgs.ac.uk
3School of GeoSciences, The University of Edinburgh, UK, kathy.whaler@ed.ac.uk

SUMMARY
Potential ground-level impacts of space weather on infrastructure include so-called Geomagnetically Induced
Currents (GICs). These currents result from electromagnetic induction during geomagnetic storms and candamage power networks, pipelines, and railways. Estimating geoelectric fields at the Earth’s surface is a criticalelement to evaluate the hazard and model GICs. Although the increase in computational capabilities has led
to a wide range of GIC estimation techniques, the accurate incorporation of the Earth’s electrical conductivity
is probably the least explored aspect. However, large-scale magnetotelluric (MT) surveys are emerging as asuitable way to fill this gap and improve GIC modelling.
The SAGE (SWIMMR Activities in Ground Effects) project, led by the British Geological Survey, is one of elevenprojects within the UK Strategic Priorities Fund (SPF) Space Weather Instrumentation, Measurement,
Modelling, and Risk (SWIMMR) program. As part of the SAGE project, we are acquiring long-period
magnetotelluric (LMT) data across Great Britain. These new sites, in addition to legacy datasets, will be used
to build a 3D conductivity model of the British Isles. The legacy data include a broadband MT survey in the Isleof Skye that we used as a mini-scale pilot experiment to analyze effects and modelling parameters expected
to be crucial in the model of Great Britain (GB). Although the ocean depth is shallow around the Isle of Skye(up to 250m), and in contrast to the argument of negligibility in the previous study, we show that the coasteffect is significant. Another aspect related to the coast effect that has been recently investigated is the impact
of conductive sediments. We show that adding sediment conductivity variation improves Skye’s model, andwe discuss the potential impact on the GB model. Finally, we investigated the effects of varying parameters
including resistivity, extension, and cell dimension of the starting model as well as damping and covariance forthe 3D inversion. The results from this study not only allowed us to build a new, robust 3D model of the Isle ofSkye but set the bases to optimize the future modelling of the British Isles.

Keywords: 3D conductivity model, parameter test, British Isles

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Geophysical Imaging of the Roter Kamm Crater in the Sperrgebiet National Park,
Namibia, using TEM and AMT
H. Nienhaus1 , P. Yogeshwar2 , W. Mörbe3 , B. Tezkan4 , B. Lushetile5 and M. Melles6
Institute of Geophysics and Meteorology, University of Cologne, h.nienhaus@uni-koeln.de2
Institute of Geophysics and Meteorology, University of Cologne, yogeshwar@geo.uni-koeln.de3Institute of Geophysics and Meteorology, University of Cologne, moerbe@geo.uni-koeln.de
4Institute of Geophysics and Meteorology, University of Cologne, tezkan@geo.uni-koeln.de5
Geological Survey of Namibia, Bufelo.Lushetile@mme.gov.na
6Institute of Geology and Mineralogy, University of Cologne, mmelles@uni-koeln.de
1

S UMMARY
The Roter Kamm Crater is a 3.7-million-year-old meteoritic impact crater in the Sperrgebiet National Park insouthern Namibia. The Sperrgebiet National Park, officially Tsau kKhaeb (Sperrgebiet) National Park, is anational park and former diamond mining area in southern Namibia. Since 1908 the public has had no accessto the area and even when it was proclaimed a national park in 2008 most of the restrictions remained, leaving
the environment mainly undisturbed and unexplored.
The geophysical exploration of the Roter Kamm Crater can lead to valuable information about its internal structure, as only a very limited number of geophysical studies had been carried out at this site. Prior gravimetryand (airborne-)magnetic measurements indicate a bowl-shaped anomaly underneath the crater, which was
expected for an impact crater with a diameter of 2.5 km. However, the estimated depths of the basement vary
between 100 m and 400 m.
To be able to image the basement in the crater in this wide range of possible target depths, two electromagnetic
methods were selected: the Transient Electromagnetic (TEM) and the Audiomagnetotelluric (AMT) method.
TEM is an active time domain method and has already proven its capability imaging sedimentary deposits. It
is suited for investigations in shallow (up to 200 m) subsurface. AMT, on the contrary, is a passive, frequency
domain method that can reach penetration depths up to a few kilometres. Both methods are sensitive to good
conductive structures.
Within two weeks of fieldwork at the Roter Kamm Crater 153 TEM and 15 AMT soundings were carried out
along three profiles. We used the TEM-FAST 48 (AEMR) for all TEM measurements with a coincidence-loop
setup for quadratic transmitter/receiver loops with an edge length of 50 m (58 soundings) and 100 m (11 soundings). Additionally, TEM measurements were carried out in a fixed-loop-setup using 4 large transmitter loops(edge length: 200 m). At each transmitter loop 11 soundings were conducted in profile and perpendicular to
profile direction with a maximum offset of 400 m. We used the ZT-30 Transmitter (Zonge) with the SMARTem24(EMIT) + 3D LF induction coil triple (Geonics Limited)/KMS 820 (KMS Technologies) + 2 TEM-3 induction coils(Zonge). For the AMT measurements the SPAM MK IV system from the Geophysical Instrument Pool Potsdam(GIPP) was used to measure the horizontal electric and magnetic fields.
The 1D inversion models of the TEM data showed a good conducing anomaly in a depth of 100 m, but was
not capable of imaging its lower boundary. The 2D inversion model of the AMT data, on the other hand, hasa good resolution in higher depths and could resolve the basement at a maximum depth of over 300 m in thecentre of the Roter Kamm Crater. Overall, AMT and TEM are in very good agreement with each other andshow the expected bowl-shaped anomaly in the impact. These results proved, that both methods complement
each other well and it was necessary to use AMT for resolving the basement within the crater and to use TEM
for resolving structures within the upper 100 m of sediments.
Keywords: Transient Electromagnetics, Audiomagnetotelluric, Sedimentary Deposit, Impact Crater

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The site selection procedure for a high-level radioactive waste repository inGermany: Future application of electromagnetic methods for exploration activities
Dennis Rippe1, Katharina Bairlein1, and Frank Meier1
1Bundesgesellschaft für Endlagerung mbH (BGE) Peine, Germany, dennis.rippe@bge.de

SUMMARY
According to the 'Repository Site Selection Act' (a German federal law called Standortauswahlgesetz –StandAG), the BGE, Germany’s federal company for radioactive waste disposal, has been assigned to
implement the site selection procedure for a German high-level radioactive waste repository in a deep
geological formation with best possible safety conditions for a period of at least one million years. The site
selection procedure is an iterative process consisting of three phases with increasing level of investigation
while the considered area becomes smaller in the process. Starting with an empty, so-called white map ofGermany, the BGE completed Step 1 of Phase I in September 2020 with the submission of the sub-areas
interim report, which identified 90 individual sub-areas with favorable geological conditions for safe disposal ofradioactive waste. According to the site selection act, the host rocks claystone, rock salt and crystalline rockare considered. In the current Step 2 of Phase I, regions for upcoming surface exploration will be identified.
Surface exploration will take place in Phase II, including geophysical surveys, geological mapping,hydrogeological investigations, borehole drilling and tests. Surface exploration programs will be developed
based on comparison of existing data and the demand for additional information to assess the disposal system.
Electromagnetic (EM) surveys can provide information of the subsurface rock properties relevant to the safetyof a high-level radioactive waste repository. Due to the large variety of possible geological settings (different
host rocks, depths, cap rock geology, etc.) it is necessary to consider various methods and their combinations
for the surface exploration. In addition, surface conditions might influence the selection of methods, e.g.
airborne EM in areas that are difficult to access. The BGE is involved in research regarding application of novel
sensors, analysis and inversion tools for EM methods and combination of EM and other geophysical methods.
Keywords: Radioactive waste disposal; Site selection; Surface exploration

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th

Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Donbas geoelectrical structure
I.I.Rokityansky, A.V.Tereshyn
Subbotin Institute of Geophysics of the National Academy of Sciences of Ukraine, Kiev, Ukraine
E-mail: rokityansky@gmail.com

SUMMARY
Donbas was formed by Late Devonian rifting of the East European Craton. Then subsidence andsedimentation formed 15-km thick Carboniferous deposits. The next long event was folding.
Then inversion lifted the folded Donbas and subsequent erosion exposed the Carboniferous
coal-bearing strata. Dominated strike of the Donbas structures is WNW-ESE. Deep electricalconductivity was studied by the methods of magnetic variation profiling (MVP) andmagnetotelluric sounding (MTS). The MVP data at long (5-180 min) periods reveal the intense
Donbas electrical conductivity anomaly (DECA) which runs along the main anticline of the folded
Donbas and continues in DDB and Karpinsky swell (Figure 1). Interpretation conductedaccording to ideas described in (Rokityansky 1982). DECA parameters obtained by MVP: Profilegraphs of the anomalous field give an estimate of the maximum possible depth of the anomalous
currents center h=18±2 km. Frequency response maximum Т0≈3600 s yields the total
8
longitudinal conductance G = (8±2)×10 S×m. Mapping the anomaly axis (Figure 1) and effective
width L=36 km estimation also reliably made by MVP. 70 MTS at periods 0.1-3000 s yield: theupper ≈0.5 km thick layer have, as a rule, resistivity in the range of 5—50 Ohm·m. Deeper low
resistivity objects are located in two conductive stripes, which upper edge varies from 0.3 to 5
km. The stripes are parallel to the DECA axis and considered as part of DECA. A very large
value of G leads to assumption that the anomalous body extends to considerable depth (Figure22). DECA axis spatially coincides with intense (up to 90 mW/m ) deep heat flow anomaly. This
fact suggests that the nature of the DECA lower part can be a partial melting. Geoelectric results
support the idea of the modern tectonic activation in Donbas developed by V.V. Gordienko.
Keywords: magnetotelluric sounding, magnetic variation profiling, folded Donbas

Methods
Anomalous currents in a conducting body arise due
to local electromagnetic induction in this body anddue to conductive redistribution of currents inducedin the enclosing media on territories with the size ofan external source. Conductive type usually
dominates. Its anomalous field is the product of
decreasing function of period (it is the impedanceof the normal cross-section) by a non-decreasing
function V (0<V<1) that describes the degree of the

Figure 1. Donbas anomaly axis – dashed line.
Arrows – induction vectors for periods 30-90 min.
Rhombus – sites where impulses (±700 A, 800 kV,
duration 100 s) from 473 km long line Volgograd –Donbas (grey line between stars) were recorded.

conducting body filling with anomalous currents.
Frequency response of such product has amaximum TO which is directly related to integral
longitudinal conductance G and open possibility toestimate it (Rokityansky 1982, p.247-256, 294-297)
REFERENCE
Rokityansky
II(1982)
Geoelectromagnetic
investigation of the Earth's crust and mantle.
Berlin-Heidelberg-New York: Springer Verlag 381p.

Figure 2. Model of the DECA cross-section
assuming the well-conducting zone of partiallymolten rocks existence: 1 - surface of crystalline
basement; 2 - two conductive stripes, oblique
shading highlights resistivity less than 1 Om∙m; 3 zone of (partial) melting; dashed line - isotherm
O
600 C according to V.V. Gordienko.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Imagery down to one kilometer depth by airborne electromagnetics: Newconstraints for geological and hydrogeological modeling in volcanic contexts
Anne Raingeard1, Pierre-Alexandre Reninger1, Aurélie Peyrefitte1, Guillaume Martelet1, Bertrand Aunay2,
Arnauld Malard3, Frédéric Dubois1
11BRGM, 3 Av. Claude Guillemin, Orléans 45060, France
2BRGM, 5 Rue Sainte Anne, Saint-Denis 97400, La Réunion, France
3ISSKA, Rue de la Serre 68/4eme étage, 2300 La Chaux-de-Fonds, Suisse

SUMMARY
We present an original approach to combine airborne magnetic data and five different airborne
electromagnetics data sets going from 3 000 NIA up to 1 000 000 NIA magnetic moments (three different
TDEM systems were used) in La Réunion island. A 3D geological model of the first kilometer bellow the Plaine
des Fougères was built and then used for 3D hydrogeological interpretation.
Keywords: Airborne electromagnetics, Time Domain ElectroMagnetics, volcanic island, geological modeling

INTRODUCTION
To bring water to the northern part of La Réunionisland (Indian ocean volcanic island), a new gallery
will be drilled bellow the Plaine des Fougères andconnected to the existing deep underground gallery
called ‘Salazie amont’, covered by up to 1000
meters of volcanic materials. County council of laRéunion (CD974) has asked BRGM to produce a
3D geological model that will be used to create a 3Dhydrogeological model, in order to preventhydrogeological risks related to the drilling of highly
permeable surrounding aquifers under highpressure. This work is presented in technical reportby Peyrefitte et al (2022).
METHOD
A first airborne electromagnetics (AEM) survey was
carried out in La Réunion island in 2014 using the
SkyTEM 304 system. It resulted in the imagery of
the first 300 m in depth over the whole Réunionisland. A second survey was flown in 2021 over the
Plaine des Fougères area, using the SkyTEM
306HP and the SkyTEM 312HP systems in order toextend the imagery, down to 1 000 m depth. During
both AEM surveys, airborne magnetic data werealso acquired. New deep bodies can now be imaged
and mapped for geological or hydrogeological
purposes. Figure 2 is an example of two SouthNorth resistivity profiles in the same area, illustrating
the gain in depth of investigation between SkyTEM
304 and SkyTEM 306HP systems. Combining bothdata sets, a high-resolution resistivity image of thePlaine des Fougères from the surface to 1000
meters depth was obtained. The processing
methodology of AEM data is presented in Reninger

et al (2020) and includes the use of the singularvalue decomposition and a manual editing of theresidual noise. A joint smooth SCI inversion(Viezzoli et al, 2007) was run considering all the
AEM data. Finally, a 3D resistivity model was
created by the interpolation of the 304 and 306HP
resistivity models for the near surface (the first 200m) and the interpolation of 306HP and 312HP
resistivity models for greater depths.
2D profile magnetic modeling was performed, using
the resistivity model as background preliminary
geometry and rock sample magnetic measurements
to constrain the geological magnetic responses.
Taking into account the volcanic history, 2D profiles
constrained by both AEM and magnetic modelling
lead to geologically realistic cross-sections (Anexample is presented in Figure 3). The resistivity
model provides the overall geometry of volcanic
layers and their alteration degree. Magnetic data
allows refining the geological model in depth,
highlighting distinct geological units within relatively
homogeneous resistivity layers.
RESULTS
Combining all the previous 2D information, 3D
geological modeling was carried out from -1000 m
to 1500 meters above sea level. Figure 1 displays aresult of the modelling, where we can see PN0 to
PN4 formations, classified by increasing age, andinformation about their alteration or saturation inwater.

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ACKNOWLEDGEMENTS
This project is funded by the county council of
Réunion (CD974) and the water office of la Réunion.
REFERENCES

Figure 1: 3D geological model of the Plaine desFougères.
This 3D model was then used for hydrogeologicalmodelling in Visual KARSYS (Malard et al, 2018), in
order to predict hydrogeological risk along several
possible gallery routes.
CONCLUSION
Following an original approach consisting in the
combination of high-resolution near surface anddeep AEM surveys with aeromagnetic data and rocksample measurements, a realistic 3D geology wasmodeled down to 1000 meters depth. This made it
possible to map the geometry of hydrogeologicalbodies within the first kilometer below the Plaine desFougères in order to reduce the risk during thedrilling of a deep gallery.

Peyreffite A, Reninger PA, Malard A, Raingeard A,
Aunay B (2022) BRGM-RP-71628-28: Aide àl’implantation d’une gallerie souterraine (GANOR):
acquisition
etvalorisation
dedonnéesgéophysiques héliportées pour la caractérisation
profonde géologique et hydrogéologique de laPlaine des Fougères. Tech. rep., BRGM, URL
http://infoterre.brgm.fr/
Reninger PA, Martelet G, Perrin J, Dumont M (2020)
processing methodology for regional AEM surveys
and local implications. Exploration Geophysics
51(1):143–154
Viezzoli A, Christiansen AV, Auken E, Sørensen K(2007) Spatially constrained inversion for quasi 3Dmodelling of aem data. ASEG Extended Abstracts
2007(1):1–4
Malard, A., Randles, S., Hausmann, P., Bucev, M.,
Lopez, S., Courrioux, G., Jeannin, P.Y., Vogel, M.,
2018. Visual KARSYS, a web-platform for the
documentation of karst aquifers including online
geological modelling, in: Delivering Subsurface
Models for Societal Challenges - 4th Meeting of theEuropean 3D Geomodelling Community, 21st to
23rd February 2018, Orléans, France. p. 39.

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Figure 2: South-North resistivity profiles acquired with 304 (a) or 306HP (b) systems in the Plaine desFougères area.

Figure 3: Example of an interpolated resistivity profile along the existing underground Salazie amont gallery,with geological interpretation. PN0 to PN4 represent old to recent volcanic formations.

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Electromagnetic Studies on The Qarun Protected Area, Fayoum-Province, Egypt
M. Mekkawi1, A. Ibrahim1 , A. Awad, A. Khalil and M. Ibrahim1
1National Research Institute of Astronomy and Geophysics (NRIAG)

11421 Helwan, Cairo-Egypt.

SUMMARY
SUMMARY
The main object of our study are using suitable and fast tools of the Geoscience to evaluate
the environmental impacts on the surrounding Qarun Protected Area (QPA) and natural resources. To
achievement this target, Nuclear Magnetic Ressonnance (NMR) and Audio Magnetotelluric (AMT) werecarried out on the suggested area for the first time in Egypt. In addition, land magnetic profile wasconducted using GEM magnetometer (Overhauser-19, Canada). The daily variation of magnetic field iscorrected using Misallat Magnetic observatory that is located at Kom Oshem village beside the Qarun
Lake (about 25 km). The results of NMR and AMT are conformed the depths of water table between 10
meters near Qarun Lake and 120 near Qatrani mountain.

Keywords: Nuclear Magnetic Ressonnance (NMR), Audio Magnetotelluric (AMT), Qarun Protected Area
(QPA)

QarounINTRODUCTION
Protected Area

(QPA)

is

established by the Egyptian national network ofProtected Areas, which as on March 2007, as
the Nature Conservation Sector of the Egyptian
Environmental Affairs Agency(EEAA).

It

contains several features and areas of highvalue. The landscape is varied, attractive andincludes some geological formations hostingfossil deposits of major importance site in thewhole African continent in terms of fossil
richness.

The

culturalheritageisalsoremarkable, with several archaeological sites ofprimary importance. A prominent feature of thearea are the Eocene sediments of Qasr el

Methods
We apply the NUMIS magnetic resonance which isthe first time in Egypt, for exploration of groundwater depth, its contents, porosity and permeability.
Beside that, the high frequency magnetotelluric
method to investigate the shallow subsurface
structure of the area surrounded the Qarun Lake.
Finally, the magnetic profile survey is conductedbeside the magnetic resonance and magnetotelluric
profiles figure (1).
The principle of Numis insturement is a pulseof current at a given frequency is transmitted into aloop. The signal produced in return by the H protons(water molecules) is measured within the same loop

Sagha and Gebel Qattrani, which represent
the most complete record of Palaeogene
mammals for all Africa, the fossil deposits of
Qatrani,fossilized

quarries

and

other

forest,ancient

outstanding

basaltfeatures.

(Egyptian Environmental Affairs Agency, 2007).
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Figure 1:Location map showing NMR and AMT
stations and magnetic profile (Google Earth).
Results
The magnetic profile was conducted every 25
m on the selected area and crossing the MT and
MRSstations

using

GEM

magnetometer(Overhauser-19, Canada), in orderFigure (3): The results of 1D inversion of AMT.

to see the shallow structures. The daily variation ofmagnetic field is corrected using Misallat Magnetic
observatory that is located at Kom Oshem village
beside the Qarun Lake (about 25 km).
The result of NUMIS magnetic resonance ofstations 8, 9, 10 and 11 are showing in Figure (2).
Also, the 1D-MT inversion of high frequency
magnetotelluric are shown Figure (3).
.

Conclusion
The magnetic resonance method (MRS) able toinvestigate the ground water aquifer with easy way
and measurements.-Also, evaluate the depth towater table, porosity (water contents) andpermeability of the layers- Correlated with the MT results which are good
agrement.
*Aquifer water-model interpretation is required tointegrate with the geological and remote sensing
data.

Figure 2: showing the results of Magnetic
resonance (MRS)

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Inferring the roots of volcano-geothermal systems in the Rotorua and Okataina
calderas with Magnetotelluric models
E.A. Bertrand1, P. Kannberg2, T.G. Caldwell1, W. Heise1, S. Constable2, B. Scott3, S. Bannister1, G.
Kilgour3, S.L. Bennie1, R. Hart1 and N. Palmer1
1
GNS Science, 1 Fairway Drive, Avalon, Lower Hutt, 5010, t.bertrand@gns.cri.nz
2
Scripps Institution of Oceanography, 8800 Biological Grade, La Jolla, California, USA
3
GNS Science, 114 Karetoto Road RD4, Taupo, 3384
SUMMARY
A combination of 365 land and 34 lake-bottom magnetotelluric (MT) measurements at ~2 km spacing are used
to investigate the structure of magmatic systems that drive high-temperature geothermal fields and volcanismin the northern part of the Taupo Volcanic Zone (TVZ), New Zealand. These data encompass the Rotorua
Caldera and Okataina Volcanic Centre, covering an area of 2,800 km2, and were inverted to create an imageof the 3-D electrical resistivity structure of the crust to a depth of 20 km. Below shallow layers of quaternary
volcanic deposits, the model is everywhere resistive, with the exception of a singular conductive zone (~500
km2) below ~8 km depth that we interpret to be relatively mafic magma, consistent with petrologic and othergeophysical data. Discrete fingers of low-resistivity (conductive) material rise from the margins of this inferredmagmatic zone and connect directly with the locations of high-temperature geothermal fields at the surface.
Notably, one of the conductive fingers rises beneath the Haroharo Dome complex within the Okataina Volcanic
Centre where 39 km3 of silicic magma has been extruded in the last 9,000 years. These recent domes, which
are electrically resistive, appear to cap and arrest the conductive finger (high-temperature fluids) at ~2 km
depth, with surface geothermal activity displaced to the northeast, and along lake shorelines at the margin of
the dome complex.
Keywords: Lake-Bottom Magnetotellurics, Taupo Volcanic Zone, Magma, Caldera, Geothermal

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The formation of geothermal systems in the context of magma-assisted continental
rifting: Magnetotelluric models from the Main Ethiopian Rift (MER)
L. Dambly1a, F. Samrock1, A.V. Grayver2, H. Eysteinsson3, M.O. Saar1
1ETH Zurich, Institute of Geophysics, Geothermal Energy and Geofluids Group, aluise.dambly@erdw.ethz.ch

2ETH Zurich, Institute of Geophysics, Earth and Planetary Magnetism Group

3Reykjavik Geothermal, Ltd., Reykjavik, Iceland

SUMMARY

The rise of geothermal development in Ethiopia has been strongly supported by magnetotelluric (MT)measurements. For placement of geothermal wells, the majority of MT studies focused on imaging the clay
cap structure of volcano-hosted high-temperature geothermal systems. Most recent studies have succeeded
to image the deeper magmatic feeding systems including the shallow magma reservoirs that drive geothermal
systems in the MER, by applying modern 3-D inversion codes and by inverting MT phase tensors that are freeof galvanic distortions.
To gain further insight into the geological setting of geothermal systems in the MER we constructed two newmodels obtained from 3-D inversion of MT data at Aluto and Corbetti volcanoes. Both geothermal systems play
an important role in the Ethiopian energy development strategy. Although they have been thoroughly studied,open questions remain concerning their formation in the geological context of magma-assisted rifting. At Aluto
the transcrustal distribution of magma was imaged by a local-scale MT survey, while at Corbetti evidence forthe presence of a shallow intrusion was found, yet the intrusion was not yet imaged in 3-D.
To obtain a multi-scale model of the regional transcrustal distribution of melt beneath Aluto volcano we analyze
two combined local- and regional-scale MT datasets. The datasets comprise regionally distributed stations
across the rift valley (33 stations, 2000 km2) and locally distributed stations measured at Aluto’s volcanic edifice(165 stations, 145 km2). In order to analyze the MT data we applied a modern 3-D inversion approach, basedon a combination of impedance and phase tensor inversion. Doing so, we succeeded to obtain a 3-D multiscale
subsurface model that images the lower crustal magma ponding zone in about 30 km depth beneath the rift.
Furthermore, the model shows that this zone is connected to the shallow upper crustal magmatic heat sourceof Aluto’s geothermal system via a structurally controlled transcrustal magma channel.
To shed light on the current state of the magma reservoir beneath Corbetti, we performed a 3-D phase tensor
inversion utilizing a local dataset (127 stations, 340 km2) covering the Corbetti caldera. In doing so, we could
image for the first time a shallow intrusion, approximately 10 km deep, that acts as a heat source for thegeothermal system of Corbetti. The location of this intrusion is in overall great agreement with previous
predictions by InSAR surface deformation observations and geodetic and gravimetric modelling studies.
Our studies demonstrate the capability of MT to provide holistic images of volcano-hosted high-temperaturegeothermal systems from small scales relevant for geothermal drilling to large scales showing their formationin the context of an actively evolving rift.

Keywords: volcano imaging, geothermal exploration, 3-D multiscale inversion, Main Ethiopian Rift

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Electromagnetic Study on The tenth of Ramadan City, Eastern Desert, Egypt
M. Mekkawi1, A. Ibrahim1 , A. Awad, A. Khalil and M. Ibrahim1
1National Research Institute of Astronomy and Geophysics (NRIAG)

11421 Helwan, Cairo-Egypt.

SUMMARY
SUMMARY
The main object of our study is evaluate the layers that contain fresh water at the tenth
Ramadan City, Eastern Desert.

Audio Magnetotelluric (AMT) were carried out on profile using G- EM3D

instrument of Geometrics, USA.
The results of AMT show three layers that contain watre during Quaternary (shallow aquifer), Eocene(intermediate Aquifer) and Olgocene ((deep Aquifer). The results of 1D and 2D AMT are comparing withhydrogeological model near the stufy area.
Keywords: Audio Magnetotelluric (AMT), 1D and 2D AMT, tenth Ramadan City . Eastern Desert.

INTRODUCTION
The study area is located at an altitude ofabout 180 to 200 meters above sea level and ischaracterized by its flat where the factors ofweathering in particular air play important roleover thousands of years other than the water
weathering, rain ages and wind weathering in
the dry ages. However, some medium-heighthills have been observed on the eastern borderlarge error bars especially at low frequency.
Results
The 1DMT results using (Bostick & Occam) codesand best fitting of Resistivities and phases areshown Figure (3). It shows the three layers of low
resistivity wet surface layers, shallow aquifer anddeep aquifer, belonging to Quaternary, Eocene andOligocene ages.

of the site as well as low-lying areas west andsouth of the site and are factors of topographicalchange due to human activity. The geological
map and the location of the study area isshowing in the (Figure 1).
MethodWe apply the AMT magnetotellric profile using GEM3D instrument of Geometrics, USA. The location
map showing the elevation and AMT stations on the
study area is showing in (Figure 2). The station
separation is approximately 100 m. Every station has
duration about 2 hours of the frequencies 10K, 4k, 1k
until 0.1 Hz. The results are processed to remove cultural

Figure 1: Geology map of the study areanoise using appropriate filter to enhance the AMT datasee (Figure 3). The AMT data seem good and have some

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Summary and Conclusion
In order to choose best place to drill well in the study
area, 2DMT smoothing method (Rebocc code) is
applied to see the horizontal and vertical change ofresistivity in the study area (Figure 4). The result iscorrelated with the hydrological result near the study
area which are good agreement with AMT result..
Aquifer water-model interpretation is required tocompare with drilling data..

Figure 2. Location map showing the elevation and AMT
stations on the study area.
.

Figure 4. 2DMT results and suitable place for drill
well.

Figure 2: showing the results of G-EM3D

Figure (3): The results of 1D inversion of AMT.

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Plate coupling at the northern Hikurangi margin: new results from magnetotellurics
W. Heise1, G. Caldwell1, T. Bertrand1, Y. Ogawa2, S. Bannister1, G. Archibald1, S. Bennie1, R. Hart1, N.
Palmer1, K. Seki2, M. Fukai2, K. H. Tseng 2, T. Nishizawa2, J. McGrath3
1 GNS Science, Lower Hutt, New Zealand, w.heise@gns.cri.nz
2 Volcanic Fluid Research Center, Tokyo Institute of Technology, Tokyo, Japan
3 School of Earth and Environment, University of Leeds, UK
SUMMARY
At the Hikurangi subduction margin along the east coast of New Zealand’s North Island, plate coupling changes
from weakly coupled in the northern part of the margin to locked in the south. Shallow slow slip events occur
quasi-regularly in the northern weakly coupled part of the margin. While the conditions that lead to slow slip
and changes in plate coupling are not fully understood, the presence of fluids within the subduction-interfaceshear-zone is believed to play an important role. This is supported by the correlation between the resistivity at
the depth of the plate interface inferred from MT data and the areal strain rate derived from GPS measurements.
52 new MT measurements have been done in the northernmost part of the Raukumara Peninsula in an area
without previous MT data coverage. 3-D inverse modelling of impedance tensor data and geomagnetic transfer
functions has been carried out at 322 at the northern half of the margin. The results data show that the dipping
conductor above the subduction interface imaged previously to the south is present beneath the entire
Raukumara Peninsula and correlates with the area of extensional strain rate consistent with weak plate
coupling. Relocated seismicity within 3km of the interface shows a paucity of seismicity in the conductive areasof the plate interface. This confirms that fluid and/or clay-rich sediments are consistent with an area with adecreased density of asperities and stored strain.
Keywords: subduction, magnetotellurics, plate coupling

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Investigation of Lithosphere Structure of Northwestern Anatolia with long-period
magnetotelluric: Part 1. acquisition data by using remotely controlled system andcomparison to previously collected broadband magnetotelluric dataİsmail Demirci1, N. Yıldırım Gündoğdu1, M. Doğukan Oskay1 and M. Emin Candansayar1
1Ankara University, Faculty of Engineering, Geophysical Engineering Department, Geophysical ModellingGroup, Ankara, TURKEY. E-mail: idemirci@eng.ankara.edu.tr

SUMMARY
In our previous study, we collected magnetotelluric (MT) data in broadband period ranges (T=0.0031s to 2000s)
on 2006-2011 to reveal the crustal structure of North-Western Anatolia. Due to the low resistivity structure of
the study area, the relationship between the crustal structure and the lithosphere could not be clearly definedin our previous project. Therefore, long period (T=0.00001s to 10000s) MT measurements are planned in orderto reveal the relationship of deep earthquakes in the area with the lithospheric mantle and to complete thedeficiencies in 3-D interpretation due to profile intervals. For this purpose, Long Period MT measurements
were collected with a station spacing of approximately 15 km along four South-North profile. The data collectedby new generation MT measurement system developed by Phoenix (MTU-5C) that enable us to remotely
control the data during measurements. This part of the study describes and discusses the effectiveness of
remotely controlled measurement system by using MT data collected in North-Western Anatolia. During the
MT data collection phase, real-time data transfer was made with the help of a modem using a cellular network,
which has recently been used in our country and in the world. Due to the very low signal amplitudes of longterm MT measurements, it is difficult to collect, especially in noisy area where settlements are located at
frequent intervals. Thanks to remotely controlled measurement innovation, data quality is controlled during
data collection. If the data quality is not good because of the unpredictable noise sources, the station location
can be changed or precautions can be taken. In this study, real-time analysis of data and initial data processing
results of measured data during long-period MT data collection will be presented. We also compare thebroadband data collected from the same stations between the years 2008 to 2011.
Keywords: Long Period, Magnetotelluric, Remotely controlled, Lithosphere, North-Western Anatolia

INTRODUCTION
Magnetotelluric method is one of the most preferredgeophysical electromagnetic methods in examining
the deep resistivity structure of the earth (between
0.1 km and 40 km depth). There are many studies
conducted in the last 15 years in the investigation of
the upper crust-lower crust boundary and thetectonic structures, suture belts and fault zones in
the crust using the magnetotelluric method (egBecken et al. 2011; Zeng et al. 2015). Similarly,
tectonic studies have been carried out in our country
to examine the features of the North Anatolian Fault
Zone, which is in the crust (first 20-30 km),especially in Western Anatolia (Tank et al. 2005;
Ulugergerli et al. 2007; Kaya et al. 2009, 2013; Tank,
2012). Again, the studies on the upper-lower crust
relationship, the suture belts and the fault zones inNorth-West Anatolia were carried out within thescope of our previous TÜBİTAK projects(Candansayar et al. 2008, 2010, 2012; Kaya 2010).
In recent years, many international projects havebeen carried out to investigate the relationshipbetween the lithosphere and the crust. Studies
carried out in the Tibet-Himalayan belt within thescope of the INDEPTH project have investigatedcontinent-continent collision and crustal melting in
the lithosphere (Unsworth 2010; Xie 2016). Similarly,
the study on lithosphere research in northern
Europe was carried out within the scope of the
BEAR (Baltic Electromagnetic Array Research)
project (Korja 2007).
In our country, there is only one study conducted in
the Arab-European collision zone to investigate thelithosphere (Türkoğlu et al. 2015). In Western
Anatolia, there is no study on the examination of thelithosphere. Within the scope of this study, long
period Magnetotelluric measurements were taken
for the first time in order to examine the lithospherestructure up to the first 100 km depth of the areasurrounded by the provinces of Zonguldak, Kocaeli,Uşak and Konya in Northwest Anatolia. During themeasurement, the data was transferred to a server
computer via the cellular network in real time. In thiscontext, in long-term MT data collection, a work flow
chart for remote data management and real-timedata analysis of these data and measured data in
the workspace will be presented.

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METHODIn the MT method, two components of the electricfield (Ex and Ey) and three components of themagnetic field (Hx, Hy, Hz) are measured as afunction of time. By taking the Fourier transforms ofthese measured E and H fields, the impedance
tensor and tipper tensor are estimated in thefrequency domain. In classical magnetotelluric
measurement systems, the measured electric andmagnetic field components are recorded on an SD
card (internal memory) located in the receiver unit
(Figure 1a). In this study, during the MT datacollection, the MT measurement system isconnected to a modem with a cellular network (GSM
operator) and the measurements are transferred tothe computer used as server in the office via thecellular network with a 10 minute packages. Data
quality is controlled by performing basic dataprocessing of these transferred data in real time.
Thus, during data collection phase, the data quality
can be examined continuously, the negative effects
on each component of the electric or magnetic fieldmeasurements can be intervened, shutdown of the
instrument due to overheating can be overseen andinstant information can be obtained.
In Long-Period magnetotelluric measurements, themeasurement time at a station in the field takes 1020 days to reach the desired period (T>10000 sec).
For this reason, a box arrangement has been
designed to ensure the safety of the Long-period MT
measurement system in the field and to be affectedby adverse weather conditions as little as possible.
The assembly in question includes the Phoenix
MTU-5C MT receiver system, the power supply(battery) and a modem for continuous transmissionof measurements via the cellular network
connection (Figure 2).
M AGNETOTELLURIC DATA ACQUISITION PHASE
Long-period magnetotelluric measurements werecollected along four parallel directions from whichbroadband MT measurements were taken within thescope of our previous TÜBİTAK project number
105G145. Thanks to the simultaneous monitoring of
the data, the measurement times at each Long
Period MT station varied between 10-20 days toreach the targeted data quality. Based on theanalysis of the broadband MT data measured in theprevious project, station positions with low culturalnoise were selected for the long-period MT dataacquisition phase. Thus, measurements that haveto be repeated and data losses were minimized.
During the 15 years after the project, the changes in
the residential area and energy transmission lines
were taken into consideration and the site
selections were made interactively in the light of all
this information. A comparison of previously

measured broadband MT and long-period MT
measured in 2020 is presented in Figure 3. In thiscomparison, it is seen that the apparent resistivity
and phases obtained from the non-diagonal
elements of the impedance are almost same in thesame period intervals. However, the apparent
resistivity and phase obtained from the diagonal
elements of the impedance are not very similar due
to the fact that these components amplitude arevery small amplitude and affected by noise andsome unforeseen deficiencies in the dataprocessing algorithm used before. Similarly, whencomparing Tipper amplitudes and phases, it is seenthat broadband data is scattered and long-period
data has a less scattered character. The mostimportant reason for this effect is that the coil usedin the measurement of the vertical magnetic fieldcannot be fully buried due to the fact that it was toolong in the previous equipment and so project.
CONCLUSIONSIn this study, for the first time, MT data covering
broad and long period bands (0.0001 sec < T <20.000 sec) were collected by real-time datatransfer and data control. The main advantage ofthis type of measurement is that the time series datatransferred to the host computer in packages within
10 minutes from the station installation can beanalyzed. Thus, installation errors can be seen in
the station setup and the error can be corrected. On
the other hand, while collecting data, especially if
the electric field is measured, rodents break thecables or for any reason, the problem in the cableconnections can be observed and this problem canbe resolved. The waiting time for long-period MT
data acquisitionf15 takes an average of 10-20 days.
In classical measurement systems, interruptions in
cables, battery depletion, etc. situations occur
frequently from the first day to the end. Thesesources of problems could have been noticed whengoing to the study area after 20 days. However, withthis new system, it can be intervened as soon as aproblem occurs.
On the other hand, the other experience gained inthis study is that the changes in the signal amplitude
according to the solar storms can affect themeasurements and cause an increase or decreasein the noise content among the MT data measured
at the same station after about 15 years. In thisstudy, it was concluded that the data measured at
the same station with an interval of 15 years weregenerally in harmony in accordance with theresistivity structure of the environment. Again, in thisstudy, it was observed that the low amplitude
components of the impedance (Zxx and Zyy) weregreatly affected by the noise, and therefore thesecomponents were not in good agreement when thedata measured at 15-year intervals were compared.

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The same problem was observed in tipper
amplitude and phase components. Therefore, in 3D
inversion, care should be taken to include the main
diagonal components of the impedance and thetipper components in the inversion according to thenoise content.
ACKNOWLEDGEMENTS
This study was supported by the Scientific andTechnological Research Council of Turkey(TÜBİTAK) under the project Grant No:119Y197.
We thank TÜBİTAK for their valuable support.
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Candansayar ME, Kaya C, Dikmen Ü, et al. (2010)
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Crustal Structure of Thrace Region Revealed by 2D
Inversion ofMagnetotelluric Data. Extended
Abstract, 21st EM Induction Workshop, Darwin,
Australia, July 25-31, 2012
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magnetotelluric surveying. Science, 274(5293), 1694.
Kaya C (2010) Deep crustal structure of northwestern partof Turkey. Tectonophysics, 489(1), 227-239.

Kaya T, Kasaya T, Tank SB, Ogawa Y, Tunçer MK,
Oshiman N, Matsushima M (2013) Electricalcharacterization of the North Anatolian Fault Zone
underneath the Marmara Sea, Turkey by ocean
bottommagnetotellurics. geophysicaljournalinternational, ggt025.
Kaya T, Tank SB, Tunçer MK, Rokoityansky II, Tolak E,
Savchenko T (2009) Asperity along the North
Anatolian Fault imaged by magnetotellurics at Düzce,Turkey. Earth, planets and space, 61(7), 871-884.
Korja T (2007) How is the European lithosphere imagedby magnetotellurics?. Surveys in Geophysics, 28(23), 239-272.
Tank SB, Honkura Y, Ogawa Y, Matsushima M, Oshiman
N, Tunçer MK, Işıkara AM (2005) Magnetotelluric
imaging of the fault rupture area of the 1999 Izmit(Turkey) earthquake. Physics of the Earth andPlanetary Interiors, 150(1), 213-225.
Tank SB (2012) Fault zone conductors in Northwest
Turkeyinferredfrom
audio
frequency
magnetotellurics. Earth, planets and space, 64(9),729-742.
Türkoğlu E, Unsworth M, Bulut F, Çağlar İ (2015) Crustal
structure of the North Anatolian and East Anatolian
Fault Systems from magnetotelluric data. Physics ofthe Earth and Planetary Interiors, 241, 1-14.
Ulugergerli EU, Seyitoğlu G, Başokur AT, Kaya C,
Dikmen U, Candansayar ME (2007) The geoelectricalstructure of northwestern Anatolia, Turkey. Pure andApplied Geophysics, 164(5), 999-1026.
Unsworth M (2010) Magnetotelluric studies of active
continent–continentcollisions. Surveys
inGeophysics, 31(2), 137-161.
Xie C, Jin S, Wei W, Ye G, Jing J, Zhang L, Xia R (2016)
Crustal electrical structures and deep processes ofthe eastern Lhasa terrane in the south Tibetan plateau
asrevealedbymagnetotelluric
data. Tectonophysics, 675, 168-180.
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180-189.

(a)(b)
Figure 1. a) Classical measurement system, (b) Remotely controlled measurement system
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Figure 2. a) MT measurement system box designed in accordance with the field conditions, b) the layout of
the system inside and c) the modem used.
(a)

(b)

(c)

(d)

(e)

(f)

Figure 3. Presentation of Long Period MT (station 1003) measured in 2020 and broadband MT (old 1011A)
measured in 2007 at the same point: (a) xy and yx component Apparent Resistivity curves, (b) xy and yx
component Impedance Phase curves, (c) xx and yy component Apparent Resistivity curves, (d) xx and yy
component Impedance Phase curves, (e) Txz and Tyz tipper amplitudes, (e) Txz and Tyz tipper phases. Red“circle and square” symbols indicate long period MT data, black “+ and x” symbols indicate broadband MT
measurements.
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3D lithospheric structure beneath the Marmara Sea by Magnetotellurics
T. Kaya-Eken1,2,3, Y. Ogawa2, Y. Usui1,4, T. Kasaya5, M. K. Tunçer6, Y. Honkura1,2, N. Oshiman7, M. Matsushima1,
W. Siripunvaraporn8.
1 Earth and Planetary Sciences, Tokyo Institute of Technology, Tokyo, Japan
2 Volcanic Fluid Research Center, Tokyo Institute of Technology, Tokyo, Japan
3 Department of Geodesy, Boğaziçi University, Istanbul, Turkey, tulay.kaya@boun.edu.tr

4 Earthquake Research Institute, The University of Tokyo, Tokyo, Japan
5 Japan Agency for Marine–Earth Science and Technology, Yokosuka, Japan
6 Department of Geophysics Engineering, Istanbul University-Cerrahpaşa, Istanbul, Turkey
7 Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan
8 Department of Physics, Mahidol University, Bangkok, Thailand

SUMMARY

The North Anatolian Fault (NAF), a 1500 km long transform boundary between the Eurasian and Anatolian
plates, is a seismically active zone in Turkey. During the 20th century, destructive earthquakes with magnitude
Mw>7 took place on this fault zone increasing the seismic hazard in the Marmara Sea. Several studies have
implied that segments in the Marmara Sea, not ruptured since 1766, are highly prone to generate a destructive
earthquake (>Mw7.2) in the near future. Therefore, a proper understanding of lithospheric structure of the NAF
Zone beneath the Marmara Sea is of utmost importance to the seismic hazard assessment studies. The energy
released after an earthquake depends on the accumulated stress along fault zones and shear fracture strengththat is controlled by the amount of pore fluid. The distribution of deep crustal fluids and connectivity of fluidfilled pores affect the electrical resistivity variation in the subsurface. Thus, electromagnetic methods, sensitive
to the electrical resistivity, are powerful tools for imaging subsurface fluids in the deep crust. We present a
study revealing the resistivity variation that reflects the rheological configuration of the lithosphere in relation
to the fluid dynamics in the crust and mantle structure of the study region. We have collected magnetotelluric
data at 25 sites in and around the Marmara Sea to elucidate 3D electrical resistivity distribution across the
NAF zone and to determine the brittle-ductile zones. To achieve this we performed a 3D inverse modeling on
magnetotelluric data using an unstructured tetrahedral mesh with the finite element method. The main findings
from this study include resistive–conductive boundaries marking the trace of the NAF; prominent shallow (downto 5 km) and deep conductors located between Çınarcık and Central Basins and beneath them, respectively;
a profound resistive structure below ~15 km consistent with the known seismic gap in this region.

Keywords: North Anatolian Fault Zone, Magnetotellurics, 3D modeling, Marmara Sea, Lithosphere.

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3D imaging of the subsurface electrical resistivity structure in West Bohemia/Upper
Palatinate covering mofettes and Quaternary volcanic structures by using
Magnetotellurics
A. Platz1, U. Weckmann1,2, J. Pek3, S. Kováčiková3, R. Klanica3, J. Mair1, B. Aleid1,2
Helmholtz-Zentrum Potsdam, Deutsches GeoForschungsZentrum, Potsdam, Germany,
aplatz@gfz-potsdam.de2
University of Potsdam, Institute of Geoscienes, Potsdam, Germany
3Institute of Geophysics of the Czech Academy of Sciences, Prague, Czech Republic

1

SUMMARY
The region of West Bohemia and Upper Palatinate belongs to the West Bohemian Massif. The study area issituated at the junction of three different Variscan tectonic units and hosts the ENE-WSW trending Ohře Riftas well as many different fault systems. The entire region is characterized by ongoing magmatic processes in
the intracontinental lithospheric mantle expressed by a series of phenomena, including e.g. the occurrence ofrepeated earthquake swarms and massive degassing of mantle derived CO2 in form of mineral springs andmofettes. Ongoing active tectonics is mainly manifested by Cenozoic volcanism represented by different
Quaternary volcanic structures. All these phenomena make the Ohře Rift a unique target area for European
intra-continental geoscientific research. With magnetotelluric (MT) measurements we image the subsurface
distribution of the electrical resistivity and map possible fluid pathways. 2D inversion results by Muñoz et al.
(2018) reveal a conductive channel in the vicinity of the earthquake swarm region that extends from the lower
crust to the surface forming a pathway for fluids into the region of the mofettes. A second conductive channel
is present in the south of their model; however, their 2D inversions allow ambiguous interpretations of thisfeature. Therefore, we conducted a large 3D MT field experiment extending the study area towards the south.
The 3D inversion result matches well with the known geology imaging different fluid/magma reservoirs at crustmantle depth and mapping possible fluid pathways from the reservoirs to the surface feeding known mofettes
and spas. A comparison of 3D and 2D inversion results suggests that the 2D inversion results are considerably
characterized by 3D and off-profile structures.
Keywords: Magnetotellurics, Ohře Rift, Conductive channel, Fluid/magma reservoir, Earthquake swarm

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Investigation of Deep Structure of Sultandağı Fault by Magnetotelluric, Gravity,
GNSS, and Tectonic studies; First ResultsÖzcan ÖZYILDIRIM1, İsmail DEMİRCİ2, Çağlar ÖZKAYMAK3,4, Özcan BEKTAŞ5, Can BAŞARAN1,3,
Ibrahim TİRYAKİOĞLU4,6, Doğukan Mert ÖZCAN7 and Ahmet YILDIZ1,3
1Afyon Kocatepe University, Geothermal and Mineral Resources Application and Research Center,ozyildirim@aku.edu.tr
2Ankara University, Faculty of Engineering, Department of Geophysical Engineering
3Afyon Kocatepe University, Faculty of Engineering, Department of Geological Engineering
4Afyon Kocatepe University, Earthquake Application and Research Center
5Cumhuriyet University, Faculty of Engineering, Department of Geophysical Engineering
6Afyon Kocatepe University, Faculty of Engineering, Department of Map Engineering
7Afyon Kocatepe University, Graduate School of Natural and Applied Sciences

SUMMARY
Afyon-Akşehir Graben is one of the most seismically active neotectonic structures in Anatolia. There areprevious scientific studies on the surface geometries, geomorphology, kinematics, and active tectonic featuresof the fault segments of the Sultandağı Fault, which is the most seismically active main edge fault of the graben.
However, studies on the geometries and depths of these faults are very few at the crustal scale. The
devastating earthquakes on the Sultandağı Fault in recent years show that segments of this fault are active,
the seismogenic zone has reached 15 kilometers, and the geometry of these faults should be investigated indetail at the crustal scale by geophysical, geological, and geodetic methods. The first results of the main
project, which includes developing a three-dimensional joint inversion algorithm for Magnetotelluric and Gravity
data, are described in this study. In the study, the magnetotelluric data measured along the lines selected
perpendicular to the main faults in the part covering the Sultandağı fault, which is the most importantseismogenic belt of the region, were solved with the two-dimensional inversion algorithm and two-dimensional
resistivity models were obtained. In addition, surface geology was used to analyze the active tectonic featuresof the fault segments on the Sultandağı Fault, and GNSS-based geodetic measurements were made to monitor
the current tectonic activities of the segments. Preliminary findings have shown that the NW-SE trending andNE dipping Sultandağı Fault, which is a dip-slip normal fault, was formed between Sultandağı and Ilgın districts
under NE-SW extensional forces. It also indicates that there are synthetic and antithetic fault branches
extending approximately parallel to the Sultandağı Fault in the graben and that the fault has a depth of at least
10 km with an average angle of 70°.
Keywords: Magnetotelluric, Inversion, Gravity, Tectonics, Sultandağı Fault

INTRODUCTION
Sultandağı Fault is a 90 km long dip-slip normal fault.
It can be traced along morphologically distinct
steepnesses that show a linear course between Çayin the west and Doğanhisar in the east, welldeveloped alluvial fans are typical along themountain front (Koçyiğit et al. 2000; Emre et al. 2011;
Tiryakioğlu et al. 2015; Özkaymak et al. 2017). The
northern margin faults located in the northeast of thegraben are represented by NE-SW trending dip-slip
normal faults from west to the east such as Bolvadin
Fault, Büyük-Karabağ Fault, Çukurcak Fault, andYunak Fault Zone (Emre et al. 2011; Özkaymak et
al. 2017). On the seismically active Sultandağı Fault,
during the instrumental period, many earthquakes
occurred. While the right lateral GPS sliding speed
determined on the Simav Fault is 3.9 mm/, the GPSbased opening speed value measured on the
Sultandağı Fault is 3.4 mm/year (Aktuğ et al. 2013).

When tectonic studies supported by geophysicalmethods are classified, strike-slip faults that
produce more earthquakes in the world are
generally examined (Stanley et al. 1990; Ogawa et
al. 2001; Tank et al. 2005; Türkoğlu et al. 2008;
Kaya et al. 2013). Normal faults, which are generally
located in the west of Turkey and cause Horstgraben structures, are mostly studied from ageothermal point of view (Erdoğan andCandansayar 2017). It is observed that Normal
faults are less frequently investigated in terms of
tectonic and seismicity than strike-slip faults such as
the North Anatolian and East Anatolian Fault
Systems with the studies conducted withelectromagnetic methods such as magnetotelluric.
However, the earthquakes that have occurred inrecent years show that the deep structures ofnormal faults in the Western Anatolia region should
be investigated in detail with geophysical methods.
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This study was carried out to fill this gap in theliterature.
In the study, 2D resistivity models were obtained byinverting the MT data obtained on the Sultandağı
Fault with the 2D MT algorithm using theunstructured mesh developed by Özyıldırım et al.
(2017). The models were interpreted comparatively
with the tectonic studies and gravity data in the field.
In addition, within the scope of this study, the current
slip rates, strain amounts, and directions of the fault
segments in the region are measured using the
GNSS technique; The geodynamic structure of theregion is evaluated by comparing the findings
obtained from the Geological and Geophysicalstudies carried out within the scope of the project.
METHODS
Along the Sultandağı Fault, it was planned tomeasure Magnetotelluric (MT) data as well as
tectonic and GNSS studies in the determined area
to view the deep structure of the part that intersects
with Afyon K25, K26, and L26 sheets of 1/100,000.
The distance between the MT lines is 3-3.5 km andconsists of 24 lines. In each line, 10 MT points were
determined with a distance of approximately 2.5 km
from each other (Figure 1).

Figure 1. The location of the study area withlocations of the MT stations on the Google Earthsatellite image, the active faults (red lines) definedin the region (Emre et al. 2011).
In Figure 1, the MT stations that were measured at
the study area and that will be measured in the
future are shown together. The yellow and green
stations show the measured data to be presented inthis study on the Sultandağı fault, which is asegment of the AAG during the period.
MT data acquisition and analysis
In this study, MT data were measured on lines 1924 from west to east(Figure 2).

Figure 2. Locations of The MT stations measured
data and designated MT lines for 2D inversion ofthese data
MT studies were carried out using “Metronix” brand“ADU07e” model receiver, “MFS06e and MFS07e”
coils, and “Pb-PbCl2” nonpolarized electrodes. The
measurement period of the data collected as a timeseries at each station varies between 24-48 hours.
Time series (raw) in four frequency bands (Sample
frequencies: "128Hz-more than 24h", "512Hz-2h","2048Hz-1h", "65536Hz-15min.") at each station
were measured. Electric (Ex, Ey) and magnetic fields(Hx, Hy) were obtained in the frequency domain bytaking the Fast Fourier transform (FFT) of the timeseries with the "ProcMT" software. Impedancevalues (Z(ω)), including the ratios of Electric andMagnetic fields, were obtained for a total of 63
frequencies, 9 frequencies in each logarithmic
period, in the range of 10kHz-10-3Hz.
Files with the extension "edi" containing impedancevalues, apparent resistivity, and impedance phasevalues were recorded for each station anddecomposition analysis was performed for global
strike direction selection for 2D inversion. G&B
decomposition was used as decomposition analysis(Groom and Bailey 1989). Analysis results areshown on the Rose diagram using three different
frequency bands (10000-1Hz, 1-0.1Hz, 0.10.001Hz) and all frequencies (10000-0.001) on thesame figure to see the dominant strike direction indifferent frequency bands. (Figure 3). In addition,
the dominant strike direction was found bydecomposing the data with the 'Strike' code of
McNeice and Jones (2001) and the 'FNIDEC'
algorithm developed in the master's thesis of thefirst author for the selected 3 frequency bands andall frequencies (Black arrows: Strike results, Redarrows: FNIDEC results in Figure 3). At the end of
the analysis, the Strike was chosen as 5o for 2D

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inversion. By rotating the Impedance Tensor to 5o
Strike, the Zxy component of the diagonal elements
was accepted as the TE-mode and the Zyx
component as the TM-mode impedance.

Tectonic and GNSS studies
Tectonic studies were generally carried out in theform of field studies. In the study, data on thesurface geometries, segmentation features,
kinematic analyzes, and top/bottom block geologyof the Sultandağı fault were collected. In this way,
kinematic analysis studies were carried out in orderto determine the kinematic properties of the region
and the stress regime history. It was planned tomake a GNSS measurement to determine thecurrent tectonic movements of the faults in theregion. GNSS measurement is one of the common
geodetic measurement methods used to determine
fault movements today. Source-fault parameters
were tried to be obtained from the velocity fields tobe obtained within the scope of the study.
RESULTS

Figure 4. Presentation of G&B decomposition
results in the rose diagram, with global strike angle
values found from Strike (Black arrows) and
FNIDEC (Red Arrows) codes
Gravity data
Measuring, correcting, mapping, filtering, modeling,
and inversion Gravity data are the processing stepsof the gravity method. This method is effective,
especially in determining the density differences likeas mines, sedimentary basins, fault zones, andburied geological structures. Due to the ill-definitionof the Gravity method, modeling is performed rather
than inversion in the literature, and deep andsuperficial tectonic edges are determined by filteringstudies. Within the scope of this study, gravity data
formed a basis for MT studies. Gravity data given in
Figure 5, were obtained from the General
Directorate of Mineral Research and Exploration ofTurkey (MTA).

Figure 5. The gravity anomaly map of the study
area and the locations of Lines determined for 2D
gravity modeling.

The rotated MT data to the Global Strike wereinverted by the inversion algorithm that use theunstructured mesh developed by Özyıldırım et al.
(2017), and the resistivity models were obtained
along the determined 6 lines. The resistivity modelobtained from the data measured along Line-24 ispresented in Figure 5c. Measured gravity data on
the MT Line-24 were modeled two-dimensionallywith the "Wingling" software. The gravity model on
Line-24 is presented in Figure 5b. In Figure 5a, theMT station locations measured on Line 24 and thelocations of active faults determined by tectonic
studies in the region are shown. Detailed tectonic
studies carried out within the scope of the study
were interpreted with 2D density and resistivity
models and the faults were drawn on the models(Figure 5b-5c). In GNSS studies, unlike otherstudies, the amount of slip on the main faults wascalculated by using the fault depths and slopes
obtained from the MT studies as inputs.

Figure 6. a. Line 24’ de bulunan MT istasyonlarının
konumları (red lines are active faults), b. 2D density
model and c. 2D resistivity model on Line-24.

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CONCLUSIONSIn the geological field studies carried out within thescope of the study, surface data such as thegeometry, kinematic analysis, and active tectonic
features of the Sultandağı fault were collected.
Preliminary findings obtained from the studies
carried out between Sultandağı and Ilgın districts
indicate that segments of the Sultandağı Fault
operate in dip-slip normal fault character, areshaped under NE-SW directional extensional forces,
and are typical with linear trending mountain front,
especially between Sultandağı and Akşehir. The dip
angles measured from the NW-SE trending andnortheastward dipping fault planes vary between 65°
and 75°.In addition, MT data collected within thescope of geophysical studies were evaluated withthe 2D inversion algorithm developed by Özyıldırım
et al. (2017), and resistivity models were obtained.
Preliminary evaluations were made for the gravity
data measured along the relevant Lines, anddensity sections were obtained for fault zones andburied geological units and structures. Obtained
resistivity and density models were interpreted in
the light of tectonic studies. The preliminary findings
indicate the existence of synthetic and antithetic
fault branches extending approximately parallel tothe main fault buried within the ceiling block of the
Sultandağı Fault and the fault has a depth of at least
10 km with an average angle of 70°.
ACKNOWLEDGMENTS
This study is supported by the Scientific andTechnical Research Council of Turkey, TÜBİTAK,under grant no: 121Y01. We thank TÜBİTAK for its
support.
REFERENCES
Aktuğ B, Parmaksız E, Kurt M, Lenk O, Kılıçoğlu A,
Gürdal MA and Özdemir S (2013) Deformationof central anatolia: GPS implications. Journal of
Geodynamics 67: 78-96
Emre Ö, Duman TY, Özalp S, Olgun Ş and Elmacı
H (2011) 1:250.000 scale active fault map seriesof Turkey, Afyon (NJ 36-5) Quadrangle. Serial
number: 16. General Directorate of Mineral
Research and Exploration, Ankara, Turkey.
Erdoğan E and Candansayar ME (2017) The
conductivity structure of the Gediz Graben
geothermal area extracted from 2D and 3D
magnetotelluric inversion: Synthetic and fielddata applications. Geothermics 65: 170-179
Groom RW and Bailey RC (1989) Decomposition ofmagnetotelluric impedance tensors in thepresence of local three‐dimensional galvanic
distortion. Journal of Geophysical Research-

Solid Earth 94(B2): 1913-1925.
Kaya T, Kasaya T, Tank SB, Ogawa Y, Tunçer MK,
Oshiman N and Matsushima M (2013) Electricalcharacterization of the North Anatolian Fault
Zone underneath the Marmara Sea, Turkey byocean bottom magnetotellurics. GeophysicalJournal International 193(2): 664-677.
Koçyiğit A, Ünay E and Saraç G (2000) Episodic
graben formation and extensional neotectonic
regime in west Central Anatolia and the Isparta
Angle: a case study in the Akşehir-Afyon Graben,
Turkey. Geological Society of London Special
Publication 173: 405-421.
McNeice, GW and Jones AG (2001) Multisite,
multifrequency
tensordecompositionofmagnetotelluric data. Geophysics, 66(1): 158173
Ogawa Y, Mishina M, Goto T, Satoh H, Oshiman N,
Kasaya T and Matsushima M (2001)
Magnetotelluric imaging of fluids in intraplate
earthquake zones, NE Japan back arc.
Geophysical research letters 28(19): 3741-3744
Özyıldırım Ö, Candansayar ME, Demirci İ andTezkan B (2017) Two-dimensional inversion ofmagnetotelluric/radiomagnetotelluric data byusing unstructured mesh. Geophysics 82(4):E197-E210
Özkaymak Ç, Sözbilir H, Tiryakioğlu İ and Baybura
T (2017) Bolvadin’de (Afyon-Akşehir Grabeni,
Afyon) Gözlenen Yüzey Deformasyonlarının
Jeolojik, Jeomorfolojik ve Jeodezik Analizi.
Türkiye Jeoloji Bülteni 60(2): 169-189
Stanley WD, Labson VF, Nokleberg WJ, Csejtey JB
and Fisher MA (1990) The Denali fault system
and Alaska Range of Alaska: Evidence forunderplated
Mesozoic
flyschfrommagnetotelluric surveys. Geological Society ofAmerica Bulletin 102(2): 160-173
Tank SB, Honkura Y, Ogawa Y, Matsushima M,
Oshiman N, Tunçer MK and Işıkara AM (2005)
Magnetotelluric imaging of the fault rupture areaof the 1999 Izmit (Turkey) earthquake. Physics
of the Earth and Planetary Interiors: 150(1-3):
213-225
Tiryakioğlu İ, Baybura T, Özkaymak Ç, Sözbilir H,
Sandıkçıoğlu A, Erdoğan S, Yılmaz İ, Uysal M,
Yılmaz M, Yıldız A, Dereli MA, Yalçın M,
Dumlupınar İM, Yalım H and Ertuğrul O (2015)
Sultandağı Fayı Batı Kısmı Fay Aktivitelerinin
Multidisipliner Çalışmalarla Belirlenmesi. Harita
Teknolojileri Elektronik Dergisi 7(1): 7-16
Türkoğlu E, Unsworth M, Çağlar İ, Tuncer V andAvşar Ü (2008) Lithospheric structure of the
Arabia-Eurasia collision zone in eastern Anatolia:
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weakening by fluids?. Geology 36(8): 619-62

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Integrated geophysical modeling of 2D/3D data in the Western Carpathians
J. Vozár1, V. Bezák1, M. Bielik1, L. Ondrášová1

1 Earth Science Institute of the Slovak Academy of Sciences, Bratislava, Slovakia, geofjavo@savba.sk

SUMMARY
We present crustal and lithosphere studies within Slovakia with magnetotelluric (MT) method, which werecombined with other geophysical data.
The first part of research was focused on interpretation of MT measurements along the 2T seismic profile,which from north to south crosses all the basic tectonic units of the Outer and Inner Western Carpathians. The
3D MT model from data distributed along the 2T profile gives a qualitatively new information about physicalproperties of the crust along the profile and in its immediate vicinity. The new phenomena are highlighted inthe 3D MT model, such as whole crustal conductivity zones at the boundary of physically contrasting blocks,
namely the Carpathian conductivity zone (CCZ), the Pohorelá shear zone and the Zdychava fault zone. These
identify major crustal blocks and structures typical for the Western Capathians. In comparison to older 2Dmodel we see a significant difference, particularly in the case of the interpreted CCZ, which was not visible in
the older 2D model. We assume that it reflects significant fault zones at the European Platform and Inner
Western Carpathian junction. The model´s resolution and precision were supported by the integrated
geophysical modelling, based on the crustal joint inversion with gravity data and geophysical-petrologicalthermally-self consistent mantle modelling.
Three MT profiles were modelled, which are distributed across the CCZ, farther to the East from the area of
2T profile. The dominant features are mostly middle-crust wide conductive zones, which are revealed on all
profiles, although not always in the same position. Such structures have no equivalent in other profiles incentral (2T) or western Slovakia, The only exception is the southern part of 2T profile, which was interpreted
as a young crust alternated by volcanic activity or hydrothermal fluids. The conductive zones on our three
profiles are also genetically young and we associate them with Neoalpine tectonic processes in the Neogene.
Further we continue with studies of dynamic processes at the contact of the Outer and Inner Carpathians. In
a series of works, we focused on MT models across the Klippen Belt (KB) and the goal of these studies wasto compare the deeper structures of the KB in its western and eastern sections. In the western section thetectonics is dominated by the Flysch Belt (FB), which overthrusts the KB. On the other hand, in the easternsection the situation is opposite - the KB is thrusted on the FB. By later processes the southern part of KB was
cut off by steep faults. The contrasting tectonic style of the western and eastern sections of KB reflects thedifferent kinematics of the Inner Western Carpathians (IWC) and the European Platform (EP) collision in thewestern and eastern part. While in the western part the transpression tectonics dominated, in the eastern part
there were frontal thrusts on the EP. Only then the transpression movements along the BP began.
Keywords: magnetotelluric, tectonics, the Western Carpathians

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Abstract, 25th EM Induction Workshop, Kuşadası, Turkey, September 11-17, 2022

Magnetotelluric investigation of the Denizli graben in the Western Anatolian
Extensional ProvinceÜmit Avşar1, Erşan Türkoğlu2

1

İstanbul Technical University, Mining Faculty, Geophysical Engineering Department, 34469, İstanbul,
Turkey, avsaru@itu.edu.tr
2 Saudi Aramco, EXPEC Advanced Research Center, 31311, Dhahran, Saudi Arabia

SUMMARY

The Eastern Medditerranean region is known as a natural laboratory for earthsciences due to having tracks of
opening and closing oceans (northern and southern tethys), subduction, obduction phenomenes inpalaeotectonic era, and having active subduction, colision, and related tectonic structures in Neotectonic
period. The most important structure of the region is Africa and Eurasia convergence which was a single
subduction zone in east-west trending but it has been evolving into different tectonic parts through thegeological time. One of the most significant of these tectonic fragments in the neotectonic period is called as
Eastern Aegean arc which has a 40 mm/year subduction rate with high dip angle beneath Aegean sea –Western Anatolia, and also migration in the south-southwestward direction give rise to retreating of the slab.
Furthermore, recent tomographic studies indicate that intersection area of the Aegean and Cyprus arc (justeast of Aegean arc) which located beneath the Western Anatolia is defined as a slab tear that causes
asthenospheric upwelling in SW Anatolia. Therefore, an extensional regime, accompied with volcanism, hasbeen well established in Western Anatolia. And currently the region is dominated by horst graben systems andassociated boundary faults. The aforementioned tectonic conditions which make the Western Anatolian grabensystems candidates as geothermal potential regions. Since it provides the environment where three necessarycomponents (heat Source, a reservoir with high porosity and permeability, liquid cycle system) of a geothermal
system can be found. Thus it becomes important to reveal the structure of the graben systems. Magnetotelluric
data were collected over a frequency range of 10,000 Hz to 0.001 Hz in Denizli graben-horst systems locatedin Western Anatolian Extensional Province. 3D MT inversions were performed to image the resistivity structureof the region.

Keywords: Denizli-Graben, Magnetotelluric, 3D-Inversion

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Investigation of Lithosphere Structure of Northwestern Anatolia with long-period
magnetotelluric data: Part 2. comparison to the 2D inversion of broadband and long
period magnetotelluric data
M. Emin Candansayar1, İsmail Demirci1, N. Yıldırım Gündoğdu and M. Doğukan Oskay1
1Ankara University, Faculty of Engineering, Geophysical Engineering Department, Geophysical ModellingGroup, Ankara, TURKEY. E-mail: candansayar@ankara.edu.tr

SUMMARY
In our previous study, we collected Magnetotelluric (MT) data along seven South-North and two East-Westdirectional profiles in North-Western Anatoli between 2007 and 2010. The data were measured broadband
frequency (T=0.0031s to 2000s) with the Phoenix MTU-5A system on 997 stations. The station interval wasapproximately 3 to 5 km. In this study, the data including AMT, broadband, and long-period MT data (T=0.0001s
to 10.000s) were measured in four parallel lines from which broadband MT data were previously measured.
The new MT data sets were measured with 15 km station intervals under the TUBITAK project (grant no.
119Y197). We used the new generation Phoenix MTU-5C system for a long period of MT measurement thatallows us remote network data transfer via cell modem. This system enables us to make quality assessmentsof the measurements while the receivers are still active in the field. In order to obtain long-period MT data up
to 10.000 seconds period, measurements were taken at each station for 10-18 days. The measurement was
terminated after making sure that the data was obtained in a period of 10.000 seconds. Broadband MT datameasured in 2010 and long-period MT data measured in 2020 and 2021 at the same station were compared.
It was seen that the two data groups in the same period range were broadly similar. We individually interpreted
long-period MT data collected along with four profiles. 2D resistivity models were obtained up to a depth of
150 km. The new models and the previous models obtained from 2D inversion of broadband MT data inversion
are compared for the first 40 km according to the tectonic structure of the region. The main tectonic zones canbe distinguished in both models. For the first time, in this presentation, preliminary results will be given about
the lithosphere structure of northwest Anatolia up to a depth of 150 km.
Keywords: Lithosphere, Nort-Western Anatolia, Long Period, Magnetotelluric, Remotely controlled, 2D
inversion
ACKNOWLEDGEMENTS
This study was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK)project under Grant No:119Y197. We thank TÜBİTAK for their valuable support.

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Magnetotellurics reveals a hidden caldera and its relation to regional tectonics in
the Cappadocia region, central Anatolia, Turkey
Ö. Hacıoğlu1, A. T. Basokur2, N. Meqbel3, H. I. Arslan2, T. Efeçınar5
1Karadeniz Technical University, ozlem.hacioglu@ktu.edu.tr
2Lemnis Geosciences, basokur@ankara.edu.tr
3National Observatory Rua General Jose Cristino, meqbeln@gmail.com
2Lemnis Geosciences, halilibrahimarslan@gmail.com
5Era Enerji Ltd., tevfik.efecinar@eraenergy.com.tr

SUMMARY
The Neogene-Quaternary volcanism in central Anatolia is characterized by ignimbrite sequences, andassociated calderas have been partly dismantled and buried as a result of tectonic, volcanic, erosional anddepositional processes, which cannot enable to identify of these structures from the surface expressions. To
search the location of a concealed caldera, one of the probable ignimbrite source vents, magnetotelluric dataacquired at 60 stations in the period range from 0.001s to 1000 s were used to derive an upper crustal threedimensional electrical resistivity model in the Cappadocia region, central Anatolia, Turkey. The resistivity model
provides constraints on the geometry and location of a buried caldera situated in the Çiftlik basin, which ischaracterized by a low resistivity (<10 Ωm) region coinciding with a caldera-like feature that is interpreted as aburied caldera complex (i.e., Çiftlik caldera) and attributed to the probable source area for the ignimbrites. The
collapse and the burying process of the caldera complex have been linked with the transtensional tectonics of
the Cappadocia region. The caldera margins are also compatible with the Quaternary fills of the Çiftlik basin
and are bounded by faults related to the Cappadocian Volcanic Province fault system. Locating a buried
eruption center beneath the Quaternary deposits may contribute to identify the source of Miocene-Pliocene
ignimbrite emplacements.
Keywords: magnetotellurics, volcanism, caldera, Cappadocia

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Estimating the melt fraction of magma reservoirs using MELTS andmagnetotellurics
D. Cordell1, S. Naif2, J. Troch3, and C. Huber4
Georgia Institute of Technology, dcordell6@gatech.edu
2Georgia Institute of Technology, snaif@gatech.edu
3Smithsonian Institution National Museum of Natural History, trochj@si.edu
4
Brown University, christian_huber@brown.edu
1

SUMMARY
Magnetotelluric data image the bulk resistivity of the subsurface which can be used to infer magma reservoir
conditions beneath volcanoes. The bulk resistivity of magma depends primarily on the melt volume fraction,
temperature, and dissolved water content. These variables are coupled via thermodynamic phase equilibria
and saturation relationships yet mixing relations for bulk resistivity implicitly treat them as independent. Here,
we use a parameterization of the rhyolite-MELTS thermodynamic modelling software to constrain relationships
between melt fraction, temperature, dissolved water content and bulk resistivity for rhyolitic magmas. Thismethod results in magnetotelluric interpretations which are (1) thermodynamically consistent, (2) independentof assumptions of temperature and water content derived from past eruptive episodes, and (3) able toinvestigate saturated melts containing crystal, melt, and volatile (i.e. aqueous fluid) phases. The utility of themethod is demonstrated with three case studies of silicic systems at Mono Basin, Newberry Volcano, and theLaguna del Maule Volcanic Field (LdMVF). The moderately-conductive feature at Mono Basin can be explainedby under-saturated partial melt (6-15 vol%) at <775°C, indicating relatively stable magma storage conditions
since the last eruption. However, the relatively resistive feature at Newberry Volcano requires lower
temperatures (<750°C) than previous estimates, suggesting that the system is saturated and has cooled since
the last eruption (or that previous interpretations at 850°C are significantly out of equilibrium). The highly
conductive feature at the LdMVF cannot be explained by saturated or under-saturated melt and requires
additional non-magmatic conductive phases. These results demonstrate the potential of this new method to
reduce uncertainty in magma storage parameters derived from magnetotelluric data and highlight the need for
additional coupling strategies between petrology, geophysics, and thermo-mechanical models to better
understand magmatic systems.
Keywords: magnetotellurics, MELTS, petrology, magma reservoir, volcano geophysics

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Resistivity Models of Southwestern Canada:
New insights into lithospheric structure, magma bodies, and geothermal systems
C. Hanneson and M.J. Unsworth
University of Alberta, Department of Physics, cedar@ualberta.ca

SUMMARY

Three new 3-D resistivity models allow a fresh look at the lithospheric structure of southwestern Canada. The
largest of these models is centred on the southeastern Canadian Cordillera and spans hundreds of kilometres.
Another model is focused on the central Garibaldi volcanic belt, from Mount Garibaldi to the Bridge River
Cones. The final model surrounds the Mount Meager volcanic complex, an area that is currently in the earlystages of geothermal development.

The Cordillera model has a horizontal resolution of 5 km and extends to a depth of 400 km. It is the firstregional-scale 3-D resistivity model of the southern Canadian Cordillera. This study focuses on crustal-scale
resistivity anomalies including the southern Alberta-British Columbia conductor and the Canadian CordilleranRegional conductor. It also investigates the transition from thin Cordilleran lithosphere to thick cratonic
lithosphere, providing new insight into the deep structure of western North America.

The Garibaldi model has a horizontal resolution of 1.5 km and extends to a depth of 40 km. This 3-D resistivity
model is preliminary, but it includes conductors below Mount Meager and Mount Cayley. Additional fieldwork
at Mount Cayley is scheduled for August 2022 and an update will be provided at the 25th EM Induction
Workshop.

The Mount Meager model has a horizontal resolution of 0.25 km and extends to a depth of 20 km. Following
two field campaigns in 2019 and 2020, this study builds on magnetotelluric results from the 1980s and early
2000s. This 3-D resistivity model provides a regional view around the geothermal target, including a potential
magma body and associated deep hydrothermal systems.

This abstract gives an overview of these three studies, along with key results. Together, they present an
exciting new view of the geologic structure and geologic evolution of southwestern Canada.

Keywords: Canadian Cordillera, Garibaldi Volcanic Belt, Mount Meager, Magnetotellurics, Geothermal

INTRODUCTION

The Juan de Fuca plate, offshore southwestern
BritishColumbia(BC)andnorthwestern
Washington, subducts beneath the North American
plate at the Cascadia subduction zone, as illustrated
in Figure 1 at the end of the abstract. The tectonic
convergence rate between these two plates isapproximately 40 mm/year (Kreemer et al. 2014). In
contrast to the Juan de Fuca plate, the Explorer
plate to the north is subducting at a rate of only 520 mm/year (Hutchinson et al. 2020).

Dehydration of the subducting slab releases
volatiles into the overlying mantle of the North
American plate, lowering its melting point andcreating a region of partial melt, which leads tovolcanism at the surface (Stern 2002). The chain of

volcanoes resulting from this subduction is called
the Cascade volcanic arc. The northern segment of
the arc, from Glacier Peak in Washington to
Silverthrone Caldera in BC, is the Garibaldi volcanic
belt (GVB; Figure 2).

Mount Baker is the most voluminous volcanic
complex in the GVB, Glacier Peak is the mostrecently active with an eruption in the mid-1700s
and Mount Meager is the most-recently active in
Canada with a major eruption ~2400 years ago(Hickson 1994). The most recent volcanism in the
GVB has been rhyolitic-to-dacitic at Mount Meager,
dacitic at Mount Cayley and Mount Garibaldi, andandesitic at Mount Baker and Glacier Peak (Hickson
1994). This illustrates a change from felsic
composition in the north to intermediate silica
content in the south.

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The resistivity model has a horizontal resolution of
0.25 km and extends to a depth of 20 km. ModEM(Kelbert et al. 2014) was used in all three studies.

Figure 2. Map of southwestern BC andnorthwestern Washington with the Canada-U.S.A.
border (black line), population centres (squares),volcanic centres (triangles) and two study areas:
central GVB (pink) and Mount Meager (purple).

BC has a range of significant geothermal resourcesin settings that include volcanic systems, faulthosted systems, and hot dry rock. Volcanic systems
are found in the GVB, including at Mount Meager
and Mount Cayley, and fault-hosted systems arefound near the southern Rocky Mountain Trench(SRMT). Both fault-hosted geothermal systems andhot dry rock resources are found in the Columbia
Mountains.

DATA AND METHODS

Figure 3. MT stations (red dots) used in the 3-D
inversion to create the resistivity model centred on
the southeastern Canadian Cordillera. Politicalboundaries (black lines), morphogeologicalboundaries (blue lines), major faults (red lines),
thermal springs (yellow dots), and major volcanic
centres (white triangles) are also shown. CB =Coast belt; IB = Intermontane belt; OB = Omineca
belt; FB = Foreland belt; SRMT = southern Rocky
Mountain Trench.

The study centred on the southeastern CanadianCordillera used magnetotelluric (MT) data from 331
locations (Figure 3). These data included 110
Lithoprobe sites, 22 EarthScope USArray sites, and19 sites from other studies. The additional 180 MT
soundings were collected by the University ofAlberta between 2002 and 2018. The 3-D inversion
used impedance and tipper data at 18 periods,
logarithmically spaced between 1 and 18,000 s. The
resistivity model has a horizontal resolution of 5 km
and extends to a depth of 400 km.

The study focused on the central GVB used MT datafrom 77 locations (Figure 4). The 3-D inversion used
impedance and tipper data at 24 periods,
logarithmically spaced between 0.017 and 800 s.
The resistivity model has a horizontal resolution of
1.5 km and extends to a depth of 40 km.

The study focused on Mount Meager used MT datafrom 66 locations (Figure 5). These data included 2,
30, 22 and 12 soundings from 1982, 2001, 2019 and2020, respectively. The 3-D inversion used
impedance and tipper data at 29 periods,
logarithmically spaced between 0.0025 and 1,000 s.

Figure 4. MT stations (red dots) used in the 3-D
inversion to create the resistivity model focused on
the central Garibaldi volcanic belt.

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Figure 5. MT stations (red dots) used in the 3-D
inversion to create the resistivity model surrounding
the Mount Meager volcanic complex.

RESISTIVITY MODELSOne horizontal slice from each of the three 3-D
resistivity models is shown in this abstract (Figures
6, 7 and 8). The resistivity models will be shown inmore detail at the 25th EM Induction Workshop.

Figure 6. Horizontal slice of the 3-D resistivity
model centred on the southeastern CanadianCordillera. MT sites (black dots), thermal springs(yellow dots) and surface traces of major faults (redlines) are also shown.

Figure 7. Horizontal slice of the 3-D resistivity
model focused on the central Garibaldi volcanic
belt. MT sites (black dots) and volcanic centres(triangles) are also shown.

Figure 8. Horizontal slice of the 3-D resistivity
model surrounding the Mount Meager volcanic
complex. MT sites (black dots) and thermal springs(yellow dots) are also shown.

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CONCLUSIONSREFERENCES

The resistivity model centred on the southeastern
Canadian Cordillera exhibits low resistivity in the
middle and lower crust of the Omineca belt. This is
likely caused by interconnected saline fluids andpossibly partial melt in the lowermost crust anduppermost mantle. Additional results, including
detailed interpretations and deep lithosphericstructure, will be presented at EMIW 2022.

Hickson CJ (1994) Character of volcanism,
volcanic hazards, and risk, northern end of the
Cascade magmatic arc, British Columbia andWashington Stake. Geol Surv Can Bull
481:231. doi:10.4095/203253

The resistivity model focused on the central
Garibaldi volcanic belt is preliminary, but it exhibits
low resistivity below Mt Meager and Mt Cayley.
Additional data will be collected at Mt Cayley thissummer. An update will be provided at EMIW 2022.

The resistivity model focused on Mt Meager exhibits
low resistivity beneath the volcanic complex in thedepth range 5-10 km, possibly caused by a magma
body. Additional results, including detailed
interpretations, will be presented at EMIW 2022.

ACKNOWLEDGEMENTS

Funding was provided by an NSERC Discovery
Grant to M.J. Unsworth and an award from the
Future Energy Systems program at the University ofAlberta. A Geoscience BC scholarship was awarded
to C. Hanneson in 2021.

Hutchinson J, Kao H, Riedel M, Obana K, Wang K,
Kodaira S, Takahashi T, Yamamoto Y (2020)
Significant geometric variation of the subducted
plate beneath the northernmost Cascadia
subduction zone and its tectonic implications as
revealed by the 2014 MW 6.4 earthquake
sequence. Earth & Planet Sci Lett 551:116569.
doi:10.1016/j.epsl.2020.116569

Kelbert A, Meqbel N, Egbert GD, Tandon K (2014)
ModEM: a modular system for inversion ofelectromagnetic geophysical data. Comput &Geosci 66:40. doi:10.1016/j.cageo.2014.01.010

Kreemer C, Blewitt G, Klein EC (2003) A geodetic
plate motion and global strain rate model.
Geochem Geophys 15:3849.
doi:10.1002/2014GC005407

Stern RJ (2002) Subduction zones. Rev Geophys
40:3. doi:10.1029/2001RG000108

Figure 1. Map of southwestern Canada and northwestern U.S.A. with political boundaries (black lines),morphogeological boundaries (blue lines), tectonic plate boundaries (red lines), volcanoes (triangles), andthree study areas: (1) centred on the southeastern Canadian Cordillera (red), (2) focused on the central
Garibaldi volcanic belt (pink), and (3) surrounding the Mount Meager volcanic complex (purple). Exp. =Explorer plate, N.A. = North American plate, SRMT = southern Rocky Mountain Trench, and WCSB = Western
Canada Sedimentary Basin.
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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Constraining the size and state of magma reservoirs through a quantitative
approach combining MT, lab measurements and petrological modelling
F. Samrock1, A.V. Grayver2, O. Bachmann3, Ö. Karakas3, L. Dambly 1, M.O. Saar1
1

ETH Zurich, Institute of Geophysics, Geothermal Energy and Geofluids Group, fsamrock@ethz.ch
2
ETH Zurich, Institute of Geophysics, Earth and Planetary Magnetism Group3
ETH Zurich, Institute of Geochemistry and Petrology, Magmatic Petrology and Volcanology GroupSUMMARY

Magnetotelluric measurements are a powerful tool to image the subsurface under active volcanic regions. 3D models, computed from magnetotelluric data, nowadays provide detailed multi-scale images of the electricalconductivity distribution. Since electrical conductivity is predominantly controlled by the presence of melt andfluid phases, conductivity models proved to be highly suitable for mapping the distribution of melt and forconstraining melt fractions. Melt fractions can be estimated by combining laboratory models for melt electricalconductivity and mixing laws to derive the bulk electrical conductivity of multiphase systems. Melt electricalconductivity depends on the composition of the melt, the amount of dissolved water as well as temperatureand pressure conditions. However, estimates of melt fractions are often based on arbitrary combinations ofthese parameters, and they do not consider the dependance of melt interconnectedness on melt fraction.
Here we present an interdisciplinary approach to interpret electric conductivity models from three volcanoes in
the Main Ethiopian Rift. We use rhyolite-MELTS to model magma crystallization and storage conditions
constrained by petrological analyses of on-site erupted products. To derive the electrical conductivity of melt
during fractional crystallization we derive an expanded melt electrical conductivity model by interpolating
between existing models for rhyolite, dacite and andesite melt. Thereby we obtain a generalized model that
describes the electrical conductivity of melt in dependence on the SiO2 and H2O content, pressure andtemperature. These parameters are given by rhyolite-MELTS. Decreasing melt connectedness withdiminishing melt fraction is considered by varying the cementation exponent, m, in the generalized Archie’s
law whilst taking into account conservation of connectedness. Furthermore, we describe the magma reservoirs
as three-phase systems consisting of crystals, melt and magmatic volatiles.
The results show that this approach enables us to constrain the current state of magma reservoirs in terms ofmelt fraction, temperature, and free volatile abundance. The latter is of eminent importance whendiscriminating between the two major mechanisms that drive volcanic unrest: magma on the move or increased
degassing of a crystallizing magmatic system, so-called second boiling.
With this study we demonstrate the great capability of the presented interdisciplinary solution approach thatcombines geophysical observations, petrological probes, and laboratory models to capture the current stateof volcanoes.
The outcome is of major importance when it comes to realistic volcanic hazard assessment and geothermalenergy applications that require a detailed understanding of magmatic heat sources that sustain geothermal
reservoirs.
Keywords: 3D inversion, volcano imaging, volcano deformation, interdisciplinary geophysical and petrologicalmodelling

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022th

3-D model of the deep structure of the Yenisei-Khatanga regional trough1

2

2

D. Yakovlev , G. Slinchuk and A. Yakovlev
STC Nord-West, stcnordwest@gmail.com, Moscow, Russia
2
Lomonosov Moscow State University, Moscow Russia

1

SUMMARY
This work is devoted to the study of the deep structure of the Yenisei-Khatanga regional trough (YKRT)in Northern Russia, where more than 25,000 MT soundings have been performed over the past 20 years overan area of 1200x400 km along a network of profiles. At present, two theories of the deep structure of thisregion and the origin of the trough are most popular: the “over-rift” and “eclogitic” hypotheses.
3-D inversion of MT data was performed using the ModEM code. From the entire data array, about 800
MT sites were selected over a 20x20 km network. Based on the results of 3-D inversion, a three-dimensional
resistivity model of the deep structure of the Yenisei-Khatanga regional trough was obtained. Within thewestern half of the YKRT, there are no high-resistivity basement rocks; a sublatitudinal conductive zone isobserved, which in general can indicate rifting processes. This testifies in favor of the “over-rift” theory of theorigin of the western part of the YKRT. The eastern half of the YKRT at depths of more than 10 km has ageoelectric structure similar to the Siberian platform and similar structures in the northwestern andsublatitudinal directions. This suggests that rift processes did not occur here. The origin of the eastern part of
the trough is rather explained by the “eclogitization” of the lower crust.
Keywords: 3-D inversion, deep structure, Yenisei-Khatanga regional trough

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Compaction-driven fluid localization and stagnation can explain
lower crustal low-resistivity zones
Matthew Joseph Comeau1, Michael Becken2 and Alexey V. Kuvshinov3
1Institut für Geophysik, Universität Münster (WWU), Münster, Germany, matthew.comeau@uni-muenster.de2Institut für Geophysik, Universität Münster (WWU), Münster, Germany, michael.becken@uni-muenster.de3Institute of Geophysics, ETH, Zürich, Switzerland, kuvshinov@erdw.ethz.ch

SUMMARY
We present electrical resistivity models that show discrete zones (width of ~25 km, vertical extent of <10 km)
of low resistivity (<30 Ωm) in the lower crust (>20 km depth), from magnetotelluric data collected across theintracontinental Bulnay region, Mongolia. The top of these features is located approximately 5 km below thebrittle-ductile transition zone and they are imaged as laterally extended (tube-like) features over 300 km long,parallel to the Bulnay fault zone, and perpendicular to the far-field compressive tectonic stress. Such features
may be caused by unaccounted-for electrical anisotropy. However, when anisotropy is considered in themodeling, the features remain. We investigate an alternative explanation: we show evidence for a conceptualhydrodynamic model of fluid localization and stagnation by thermally activated compaction. We demonstratethat this is compatible with the observed low-resistivity zones, which are consistent with the presence of saline
metamorphic fluids. Based on the thermal structure of the crust, the conceptual model predicts the size andshape of the zones: the fluid domains should have a vertical extent of ~9 km and their centres should be <9km below the brittle-ductile transition zone. The conceptual model gives plausible values for the activationenergy for viscous creep (270-360 kJ/mol), suggesting that the mechanism is dislocation creep. The electricalresistivity models constrain the lower crustal viscous compaction-length to be ~25 km, in this region. This
length scale is consistent with independent estimates of hydraulic and rheological properties. The model canbe used to independently constrain the lower crustal effective viscosity, the fluid salinity, and the porosity.
Overall, the results imply tectonic deformation and compaction processes, rather than lithological-structural
heterogeneity, control the regional lower crustal fluid flow.
Keywords: fluid; compaction; anisotropy; brittle-ductile transition; resistivity

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The Curnamona Cube, new data and insights
B. Kay1, G. Heinson2, K. Brand3, S. Thiel4 and G. Boren5
1University of Adelaide, ben.kay@adelaide.edu.au
2University of Adelaide, graham.heinson@adelaide.edu.au
3Beurea of Meteorology, kate.e.brand01@gmail.com
4Geological Survey of South Australia, stephan.thiel@sa.gov.au
5University of Adelaide, goran.boren@adelaide.edu.au

SUMMARY

The Curnamona Province is a Paleoproterozoic to Mesoproterozoic domain that hosts the Broken Hill Pb-Zn
ore body and a number of IOCG type Cu-Au deposits, but is mostly covered in Neoproterozoic to Quaternary
cover from a few tens of meters to thousands of meters with much of the province remaining unknown. Threedimensional resistivity modelling of 55-km gridded long-period magnetotelluric data collected as part of theAustralian Lithospheric Architecture Magnetotelluric Project (AusLAMP) previously revealed the presence of
the Curnamona Conductor that exhibits extremely low resistivities <1 Ω.m at upper-crustal depths (<15 km)
which has a lateral extent of >200 km in a north–south orientation. The conductor appears to dip to the west,
centred under the large igneous province Benagerie Suite Volcanics with resistivity ∼10–100 Ω.m in a broader
lower crustal region.
Here, we present 3D resistivity models of the Curnamona Cube, with approximately 200 magnetotelluric
stations over the Curnamona Province. The AuScope funded program, the Curnamona Cube, was designed
to collect approximately 150 broadband magnetotelluric and ambient noise tomography stations tocharacterise the crust and upper mantle from the surface down to several hundred kilometres in such a way
as defining a cube of the physical properties of the Curnamona Province. This cube is designed to provide aphysical model of the Curnamona Province in terms of its electrical resistivity and seismic velocity covering an
area of around 400 x 400 x 400 km’s.

Keywords: Curnamona Province, Undercover, Curnamona Cube, Magnetotelluric, Ambient Noise
Tomography

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

First Magnetotelluric imaging of the northern Zagros orogenic belt
(Preliminary report on measured data and processing techniques)
Sh. Zhian1, A. Junge1 and B. Oskooi1
1Goethe-Universität Frankfurt am Main, zhian@geophysik.uni-frankfurt.de
1Goethe-Universität Frankfurt am Main, junge@geophysik.uni-frankfurt.de
1Goethe-Universität Frankfurt am Main, email contact

SUMMARY

The Zagros orogenic belt, is a young and active orogen extending from the northwest to
southeast in the western half of Iran, which, together with the Iranian plateau hold a recordof the long-standing convergence history between Eurasia and Arabia across the NeoTethys, from subduction/obduction processes to present-day collision (Agard et al. 2011).
On how this collision is accommodated in the northern part, currently there are two seismic
models (Rahmani et al. 2019, Motaghi et al. 2017), proposing different geodynamic
processes (lithospheric thickening or delamination) within the lithosphere andasthenosphere. An initial 2-D MT study based on these two seismic results indicates MT isa promising method to distinguish between different conductivity models within thelithosphere and upper asthenosphere beneath the Zagros.
To study the lithospheric structure and discriminate between the proposed seismic models
in the northern Zagros, an MT survey was carried out at 22 sites (22 long-period and 22
broad-band measurements) along two parallel 450-km profiles in winter and spring 2022,with an approximately northeast and southwest direction from Tehran to Ilan provinces,crossing the Zagros.
Here, we present first results from the acquired data, an electrical conductivity model basedon data processing using the robust processing technique implemented in Frankfurt
software package, FFMT. The Matlab based programs follow the multi-station approach by
Egbert 3 (1997) and has been extended to diminish the effects of anthropogenic noise (which
so far, has caused challenges to process the data from some of the sites) by using an
Eigenwert-criterium for the selection of the time segments (Hering, 2019). It allows for theminimization of coherent and incoherent noise between the field components of the local
sites and improves results compared to standard processing approaches.
Keywords: Magnetotelluric, Zagros orogenic belt, Conductivity structure, Magnetotelluric transfer functions

REFRENCES
Agard, P. et al., 2011. Zagros orogeny: a subduction-dominated process, Geol. Mag., 148(5/6), 692–725.
Egbert, G., 1997. Robust multiple-station magnetotelluric data processing, Geophys. J. Int. 130, 475-496.
Hering, P., 2019. Advances in Magnetotelluric Data Processing, Interpretation and Inversion, illustrated by athree-dimensional Resistivity Model of the Ceboruco Volcano. PhD Thesis, Goethe-Universität Frankfurt am
Main, Frankfurt, Germany.
Motaghi, K., Shabanian, E. & Kalvandi, F., 2017. Underplating along the northern portion of the Zagros suturezone, Iran, Geophys. J. Int., 210, 375–389.
Rahmani, M., Motaghi, K., Ghods, A., Sobouti, F., Talebian. M., Ai Y. and Chen, L., 2019. Deep velocity imageof the north Zagros collision zone (Iran) from regional and teleseismic tomography, Geophys. J. Int. 219, 1729–
1740.

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308

Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

What are the compositional causes behind electrical conductivity variations incontinental lithospheric mantle? Methodology and practice for quantified
interpretations.
S. Özaydın1 , K. Selway2 , M. Moorkamp3 , W. L. Griffin1 and C. Manassero1
School of Natural Sciences, Macquarie University, Sydney, Australia, sinan.ozaydin@mq.edu.au
2
MinEx CRC, Future Industries Institute, University of South Australia, Adelaide, Australia,
kate.selway@unisa.edu.au
3
Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität, Munich, Germany,
moorkamp@geophysik.uni-muenchen.de
1

S UMMARY
Since the electrical conductivity can be used as a proxy for many properties of the earth media, the magnetotelluric method remains one of the most powerful tools for unveiling the lithospheric architecture. However,the compositional nature behind variations in electrical conductivities is often under-interpreted. This practice primarily stems from the non-uniqueness in solutions acquired via deterministic inversion techniques andhardship in tying them to the vast literature of experimental petrology and electrical conductivity studies.
We demonstrate a review of methodologies used in making such quantified interpretations of magnetotelluric
models centering the use of the MATE software (Özaydın and Selway, 2020, https://github.com/sinanozaydin/MATE)
and portray the findings of such analysis from case studies in Southern Africa (Özaydın et al, 2021, 2022),
Eastern Australia, Tanzania and Continental US.
Detailed comparison of xenoliths and xenocrysts from Southern Africa and recently-developed 3D magnetotelluric models demonstrate that such relationships between composition and electrical conductivity indeed
exist. However, they constitute a complex phenomenon that requires careful analysis, which should include
the emphasis on style, recurrence and intensity of metasomatism alongside the initial formation and age of thelithosphere in the first place.
Relationships between mantle-derived magma (e.g., kimberlites) and electrical conductivity; also exist according to analyses made from MT models around the Earth and spatial distribution of kimberlites. This finding
supports magma transport mechanisms for kimberlites are controlled by lithospheric composition.
Keywords: lithospheric mantle, electrical conductivity, cratons, kimberlites, metasomatism

R EFERENCES
Özaydın S, Selway K (2020) Mate: An analysis tool
for the interpretation of magnetotelluric models of
the mantle. Geochemistry, Geophysics, Geosystems 21(9):e2020GC009126
Özaydın S, Selway K, Griffin WL (2021) Are xenoliths from southwestern kaapvaal craton repre-

sentative of the broader mantle? constraints frommagnetotelluric modeling. Geophysical Research
Letters 48(11):e2021GL092570
Özaydın S, Selway K, Griffin WL, Moorkamp M(2022) Probing the southern african lithospherewith magnetotellurics: 2. linking electrical conductivity, composition, and tectonomagmatic evolution. Journal of Geophysical Research: Solid
Earth 127(3):e2021JB023105

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309
Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Imaging of an intraplate volcanic system from source to surface
Matthew Joseph Comeau1, Michael Becken2 and Alexey V. Kuvshinov3
1Institut für Geophysik, Universität Münster (WWU), Münster, Germany, matthew.comeau@uni-muenster.de2Institut für Geophysik, Universität Münster (WWU), Münster, Germany, michael.becken@uni-muenster.de3Institute of Geophysics, ETH, Zürich, Switzerland, kuvshinov@erdw.ethz.ch

SUMMARY
The structure of continental intraplate volcanic systems — which occur far from active tectonic margins, unlike
the majority of Earth’s volcanism — is enigmatic and not fully understood, as are the underlying mechanisms
responsible. Quaternary–Neogene aged, alkaline basalt flows and clusters of volcanic cones are found inCentral Mongolia, which is in the continental interior. Using a high-resolution, multi-scale, magnetotelluric
dataset, we generate both 3-D and 2-D electrical resistivity models of the lithosphere and uppermost mantle
beneath this region. We focus on two volcanic fields that are separated by approximately 100 km. By examining
the models and considering all available evidence we propose the following: 1) narrow, vertical, low-resistivity
anomalies located in the upper–middle crust beneath the surface expressions of volcanism represent theremnant signatures of ancient, ephemeral magma pathways (or collection of pathways) and record the locationof magma ascent; 2) widely distributed low-resistivity zones in the lower crust can be explained by very small
amounts of saline fluids in a thermally perturbed region, and are hard to reconcile with magma storage; and3i) a local low-resistivity zone in the lithospheric mantle and a broad, doming low-resistivity feature in theuppermost mantle are interpreted to represent a metasomatized lithospheric mantle and a mantle upwelling
and thermal anomaly explained by low-percent partial melt, which is inferred to be the source for intraplate
volcanism. Thus the geophysical images reveal magma generation and transport from a mantle source to thesurface beneath a continental intraplate basaltic volcanic system. Furthermore, they are consistent with
geochemical and petrological evidence from erupted lavas that point to a single common mantle source region
for both volcanic zones and a volatile-enriched, metasomatized sub-continental lithospheric mantle, as well as
limited crustal contamination and rapid magma ascent.
Keywords: magma plumbing; intraplate volcanism; mantle upwelling; partial melt; electrical resistivity

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Crustal structure across Indus Tsangpo Suture zone NW Himalaya, India as
revealed from Magnetotelluric study
C.K. Rao
ckraochinta@gmail.com
Adhiraj Gardens, Kharghar, Navi Mumbai

SUMMARY
Broad band magnetotelluric (MT) data were acquired at 18 stations along NE-SW profile crossing Tso Morari
and Indus Tsangpo (ITS) suture in NW Himalayan region. Regional strike analysis indicates the MT data is
consistent with an assumption of 2D geoelectric strike direction. The regional strike indicates N400W andcorrelates well with the observed predominant geological strike of the region. The galvanic distortion corrected
data were inverted with 2D inversion code for obtaining geoelectric model of the study region. The geoelectric
section broadly reflects the resistivity pattern typically observed over Indus Tsangpo suture in other parts of
Himalaya. A low resistivity zone of 20 km thick and 15 km wide with resistivity less than 2 Ohm-m is delineatedcorresponds to ITS is flanked on either side by the high resistivity bodies. The high resistivity bodies
correspond to Rupshi granite on SW of ITS and Ladakh batholith and Chusul granite on the NE. Studies indifferent parts of ITS indicate that the low resistivity is a regional feature extending over the entire 2500 km
length of Himalaya. The low resistivity observed in the present study is attributed to either saline fluids orpartial melts of the rocks due to high temperature regime in deeper crust, and also the frictional heatgenerated in the process of Indian Plate sliding across the Tibetan block. The high surface heat flow of 180
mW/sq.m in the region suggest that most of contribution to the observed low resistivity is of thermal origin.
Keywords: Magnetotellurics, Higher Himalayas, Indus Tsangpo suture

311
Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Long period magnetotelluric at the Antarctica: The role of asthenospheric mantle
anisotropy in Glacial Isostatic Adjustment.
González-Castillo L.1, Madarieta-Txurruca A.1, Hill G.2, Castro C. 3, Galindo-Zaldívar J. 1,4 and Junge A. 3
1 Dpto. de Geodinámica, Universidad de Granada, Granada, Spain. lgcastillo@ugr.es
1 Dpto. de Geodinámica, Universidad de Granada, Granada, Spain. amadatxu@ugr.es
2 Institute of Geophysics, Czech Academy of Science, Prague, Czech Republic. gjhill@ig.cas.cz
3 Institut für Geowissenschaften, Goethe Universität Frankfurt, Frankfurt am Main, Germany. castro@geophysik.uni-frankfurt.de
4 Instituto Andaluz de Ciencias de la Tierra CSIC-Universidad de Granada, Granada, Spain. jgalindo@ugr.es
3 Institut für Geowissenschaften, Goethe Universität Frankfurt, Frankfurt am Main, Germany. junge@geophysik.uni-frankfurt.de

SUMMARY
Understanding the behavior and geodynamic evolution of Antarctica is essential to determine theprocesses controlling future climatic warming and sea level rise. The Antarctic continent was isolated since 3040 Ma ago by the tectonic opening of the Drake-Scotia and Tasman gateways creating the Antarctic
Circumpolar Current. This change in oceanic circulation doubtlessly affected the global climate and Antarctic
ice sheet evolution with consequent sea level change. Mantle rheology influences the motion of lithosphericplates and the Glacial Isostatic Adjustments (GIA) processes which imprint the mantle structure including
anisotropic features. Mantle electrical anisotropy may be identified by means of long period magnetotelluric(LMT) measurements. In the first stage of our research, LMT data were collected at seven sites distributed on
the Antarctic Peninsula and South Shetland Islands, from January to March 2022. Here we present preliminary
data analysis that suggests possible presence of electrical anisotropy in the asthenospheric mantle of thisportion of the Antarctic Peninsula and South Shetland Islands. Accounting for the tectonic evolution of
Antarctica, is essential to identify the source of potential mantle anisotropy i.e. is it a result of geodynamic and/or GIA processes. These results will contribute to the improvement of GIA models currently developed in viewof an isotropic mantle. GIA models allow for reconstructions of ice mass changes which help the understanding
and prediction of sea-level changes.
Keywords: Antarctica, mantle anisotropy, Long Period Magnetotelluric, GIA.

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Investigation into lithospheric mantle of Northern Tanzania utilising 3D
magnetotellurics.
S. Özaydın1 , K. Selway2 , S. Foley1 , P. Tarits3 and S. Hautot4
School of Natural Sciences, Macquarie University, Sydney, Australia
2
MinEx CRC, Future Industries Institute, University of South Australia, Adelaide, Australia
3
Laboratoire Géosciences Océan, Institut Universitaire Européen de la Mer, 29280 Plouzané, France
4
IMAGIR sarl, 29290 Saint Renan, France
1

S UMMARY
The relationship between mantle melting and its residual effects on the composition of the lithosphere has
not been well documented. Neogene rifting system in Northern Tanzanian Divergence provides an excellent
laboratory to investigate such a relationship since a variety of volcanic rocks in the vicinity are associatedwith different categories of recent incipient mantle melting (Foley et al, 2012). Electrical conductivity is sensitive to the products of metasomatism exerted on the mantle (e.g., water, hydrous minerals); therefore, themagnetotelluric (MT) method can be utilised to explore such relationships.
The electrical structure of North Tanzanian Divergence was investigated using 3D magnetotelluric modelling(Kelbert et al, 2014) of a dataset combining long-period and broadband MT stations (Selway, 2015; Plasman
et al, 2019). Then the magnetotelluric model was interpreted using the MATE software (Özaydın and Selway,2020) to understand the relationship between mantle composition and electrical conductivity. The preliminary
results indicate that the Tanzanian craton and the rift region consist of a more heterogeneous mantle withpatches of hydrated/conductive regions. On the other hand, a more dry/resistive mantle is present beneath theMozambique Belt, possibly reflecting the destruction of metasomes via larger volumes of mantle melting.
Keywords: magnetotellurics, electrical conductivity, cratons, rifting, incipient melts

R EFERENCES

the mantle. Geochemistry, Geophysics, Geosystems 21(9):e2020GC009126

Foley S, Link K, Tiberindwa J, Barifaijo E(2012) Patterns and origin of igneous activity around the Tanzanian craton. Journalof African Earth Sciences 62(1):1–18, DOI
10.1016/j.jafrearsci.2011.10.001

Plasman M, Hautot S, Tarits P, Gautier S, Tiberi
C, Le Gall B, Mtelela K, Gama R (2019)
Lithospheric Structure of a Transitional Magmatic to Amagmatic Continental Rift SystemâInsights from Magnetotelluric and Local Tomography Studies in the North Tanzanian Divergence,
East African Rift. Geosciences 9(11):462, DOI
10.3390/geosciences9110462

Kelbert A, Meqbel N, Egbert GD, Tandon K (2014)
Modem: A modular system for inversion of electromagnetic geophysical data. Computers & Geosciences 66:40–53Özaydın S, Selway K (2020) Mate: An analysis tool
for the interpretation of magnetotelluric models of

Selway K (2015) Negligible effect of hydrogen content on plate strength in East Africa. Nature Geoscience 8(7):543–546, DOI 10.1038/ngeo2453

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The Electrical Signature of the Manzaz and Atakor Intraplate Cenozoic
Volcanism (Central Hoggar, South of Algeria)
Zakaria Boukhalfa1, Abderrezak Bouzid1 and Amel Benhallou1
1

Centre de Recherche en Astronomie, Astrophysique et Géophysique (CRAAG), BP63,
Route de l’Observatoire, Bouzereah, Algiers, 16340, Algeria, boukhalfa.zakaria@yahoo.com

SUMMARY

Volcanism occurs in several distinct geological settings. Most of these are associatedwith tectonic plate boundaries. In contrast, a relatively small number of volcanoes occur
within plates far from their margins. The crustal and lithospheric mantle structure ofsuch continental intraplate volcanic systems are enigmatic and the origins of volcanic
activity remain controversial, which is due in part to a lack of high-resolution
geophysical data. In Algeria, the intraplate volcanism is well presented in the Hoggar
massif which belongs to the Tuareg shield. This region is associated with a lithosphericswell of about 1000 km in diameter in relation with the Cenozoic volcanism occurringin several regions. The origin of this activity is nowadays debated. The Atakor/Manzaz
region is located in the center of the swell. In order to define its deep structure,broadband magnetotelluric (BBMT) data from 40 stations along three intersected
profiles were collected. The BBMT soundings are scattered across an area
approximately 170km from east to west, and 80 km from north to south. We performed
3-D inversion of full impedance tensor and vertical magnetic field transfer function. The
obtained model exhibits very complex electrical features. It highlights three electricallevels reflecting the crust and the uppermost lithospheric mantle. The upper crust is
mainly dominated by a sharp resistant structure interrupted by several conductors
related to the known shear zones of the area, while the middle-to-lower crust shows avery conductive character, the whole set is underlined by more or less conductive partwhich materializes the uppermost lithospheric mantle. The architecture formed by the
conductive bodies at different depths in this model, is reminiscent of the plumbing
system, and known as the Flower Structure in geology.

Keywords: Intraplate volcanism, Broadband magnetotelluric (BBMT), Flower
structure.

314
Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Electrical resistivity structure beneath the southern Tohoku, Northeast Japan,
inferred from a joint inversion of magnetotelluric and geomagnetic transfer
functions
D. Diba1, M. Uyeshima2, M. Ichiki3, S. Sakanaka4, M. Tamura5, and Y. Usui6
1Earthquake Research Institute, the University of Tokyo, dieno@eri.u-tokyo.ac.jp
2Earthquake Research Institute, the University of Tokyo, uyeshima@eri.u-tokyo.ac.jp
3Graduate School of Science, Tohoku University, masahiro.ichiki.b5@tohoku.ac.jp
4Graduate School of International Research Sciences, Akita University, sakanaka@gipc.akita-u.ac.jp
5Research Institute of Energy, Environment, and Geology, Hokkaido Research Organization, tamura-makoto@hro.or.jp
6Earthquake Research Institute, the University of Tokyo, yusui@eri.u-tokyo.ac.jp

SUMMARY
Significant in-land activities in southern Tohoku are implications of the ongoing subduction system in Northeast
Japan. Knowing that fluids play an essential role in arc magmatism and the associated seismicity, this study isintended to understand the deep subsurface fluids distribution beneath the southern Tohoku to clarify the originof the in-land activities. A magnetotelluric survey delineating the subsurface electrical resistivity structure issuitable for this purpose because bulk resistivity is sensitive to the composition and connectivity of fluids. Timevarying electric and magnetic field data were recorded at 15 stations along a profile line. The frequency-domain
response functions of the measured fields were interpreted to yield a two-dimensional electrical resistivity
structure using a newly developed joint inversion code. In addition to the commonly used response functions,the inter-station horizontal magnetic field transfer function was also considered to determine the regional trend
and estimate the electrical resistivity structure. The result shows that in the southern Tohoku, especiallybeneath the survey line, a fluid-rich area is located under the back-arc side instead of the volcanic front. This
understanding comes after inversion reveals an upper mantle – middle crustal conductor between a back-arc
volcano and the volcanic front, well-correlated with a swarm of deep low-frequency events. The bulk
conductivity of the conductor could be explained by some degree of partially molten rock and saline wateroriginating from the upwelling flow above the subducting slab. In addition, a surficial, inclined conductive body
is found on the back arc, the boundary of which is very consistent with the eastward dipping reverse fault of
the Tsukioka fault zone.
Keywords: electrical resistivity structure, magnetotelluric method, geomagnetic transfer function, inversion,
subduction zone

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315
th

Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Crustal Structure beneath East Himalayan Syntaxis and the Relation to its Rapid
Uplift and Exhumation
Hao Dong

1,2*

1

1,2

1,2

1,2

, Jialin Qi , Sheng Jin , Gaofeng Ye and Wenbo Wei
China University of Geosciences, Beijing,
2
State Key Laboratory of Geological Process and Mineral Resources, Beijing*donghao@cugb.edu.cn
1

SUMMARY
The unique East Himalayan Syntaxis (EHS) is featured with a sharp U-turn of all geological boundaries,
geomorphic features and river systems at the east end of the Himalayan orogenic belt. The EHS is believed to
be one of the regions with the strongest Cenozoic uplift and exhumation in Tibetan Plateau. Among the EHS,
the most significant geomorphology feature is the Namcha Barwa (NB) peak, which rises abruptly to over
7700m from the surrounding Yarlung-Tsangpo canyon. The core Namcha Barwa area remains a high uplift
rate since late Pliocene and forms a series of high-angle fault systems centered on EHS. To understand themechanisms behind the rapid uplift of the study region, magnetotelluric array dataset were used to image the3D electrical structure beneath the EHS. The inversion model reveals crustal high conductivity anomalies
around the NB region, at the depth of 20-40 kilometers. The rapid uplift of the region may lead todecompression melts of the mid-to-lower crust, which is correlated with local leucogranites. The melts may
provide the heat sources for the hydrothermal systems around Namcha Barwa. On the other hand, the crustal
structure directly beneath NB is imaged as highly resistive NNE directed anomaly. This feature is consistent
with previous regional magnetotelluric model and the high-velocity structure discovered by Rayleigh wave
velocity results, but do not support the popular “tectonic aneurysms” model which involves crustal advective
flows towards the topographic gap. The resistive “wedge” may reflect the northward intrusion of deep
materials into the crust of the Lhasa block, which further induces the uplift of regional structure and the
upwelling of hot materials at the front end. With the gradual intrusion of the wedge, the uplift may gradually
expands to the northeast, forming the long and narrow compound anticline responses.

Keywords: East Himalayan Syntaxis, Namcha Barwa, Magnetotellurics, uplift and exhumation

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316

Magnetotelluric investigations in south of Mexico to better understand the seismic hazard of
the area.
Ruiz-Aguilar, D., Husker, A., Arango-Galván, C., Romo-Jones, J.M., García-Suárez, E.,
Constable, S.
As part of a collaborative project, we applied Magnetotellurics in the south area of Mexico to better
understand its seismic hazard. In the framework of this NSF funded project, we propose an amphibous
magnetotelluric sounding survey coupled with seismotectonic analysis to: (1) Determine differencesin coupling/slip regimes in and around the Mexican Tehuantepec seismic gap; (2) Image theTehuantepec Ridge within the subducted plate to determine its role in the coupling and if it contributes
to the break between the Trans-Mexican Volcanic Belt (TMVB) arc system and the Chiapas arc; (3)Image any evidence of fluid infiltration in the Tehuantepec fault. The proposed study analyzes theunusually large Mw8.2 normal fault Tehuantepec earthquake which is downdip of a slab interface
that has no recorded earthquakes. The proposed reasons for why the earthquake occurred are that it
was due to slab pull below a strongly coupled interface or that it was due to slab bending along areactivated, possibly fluid-filled and poorly coupled subducted outer rise fault. This study would
represent the only physical measurement to directly determine which is correct and determine the link
to possible future earthquakes. In this work, we present results of the MT land measurements thatwere recorded from January to March 2022. We used the long-period LEMI-424 systems that areprovided by PASSCAL instrument center, and the MT Time Series were robust processed to estimate
the MT transfer functions. A 3D inversion scheme was applied using ModEM and we present theobtained results.

317

The Geometry of the Main Himalayan Thrust along the Satluj river valley,
Northwest Himalaya, India retrieved from Magnetotelluric studies.
Authors: Dhamodharan S and Gautam Rawat
Abstract
The magnetotelluric (MT) data collected along the Nahan- Kourick-Chango transact in the Satluj rivervalley in the northwest Himalaya have been analysed to obtain subsurface resistivity structure. The
objective is to study tectonics and the geometry of the major fault systems in the region.
Dimensionality analysis of robustly estimated transfer functions indicates the distribution of electricalconductivity in the study area is complex specifically at low frequencies, whereas at middle andhigher frequencies majority of MT responses are two-dimensional (2-D). Dimensionality anddecomposition analysis recovers geoelectrical strike NE-SW striking with an angle of ~45 for the
majority of responses. The geoelectrical strike coincides with the general trend of the major
geological ruptures in the study area. The Non-Linear Conjugate Gradient (NLCG) algorithm wasutilized to obtain the 2-D resistivity model of the study area. Near-surface resistivity distribution
favours known geological findings of the medium to highly metamorphosed crystalline rocks of theLesser Himalaya and Higher Himalaya. Shallow surface high conducting layer mapped throughout the
profile, is characterized by the presence of trapped water/fluids expelled from the underthrusting
sedimentary rocks. The Intra-Crustal Low Resistive Layer (IC-LRL) of the Himalayan wedge with athickness of ~9 km marked with very low resistivity throughout the profile. The resistivity transition
between the Indian basement and the base of the IC-LRL of the Himalayan wedge making thedetachment or Main Himalayan Thrust (MHT) with a dipping angle of 3.5 in the Sub Himalaya, 6 inthe Lesser Himalaya, and ramping with 21 to the south of the Munsiari Thrust and further dipping
north-easterly with 5 at the north of the South Tibetan Detachment. Despite of some undulation in
the scale, the observed geometry of the MHT with ramp structure in the present study is well reliablewith the geometry of the MHT discovered by previous balanced geologic cross-sections in the study
area and the adjacent Garhwal and Kumaun region.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Integrated geophysical study of the deep structure of Yenisei-Khatanga regional
trough: new results and MTS contribution
Е. Yu. Sokolova 1, 2, Bolshakov E.M.2, Biserkin I.A.1, Finkelshtein M.Ya.1, I. S. Kupriyanov 1, Pimanova
N.N.1, Shirokova T.P.1,3
1 All-Russian Research Institute of Oil Geology, Moscow, Russia, sokol_l@mail.ru
2 Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, Moscow, Russia,
3 Moscow State University, Moscow, Russia, tpshirokova@yandex.ru

SUMMARY
The paper presents modern results of integrated interpretation of the data obtained by different geophysicalmethods in the region of Yenisei-Khatanga regional trough (YKRT) - oil and gas-bearing province located at
Taimyr peninsular, the Northern frame of East Siberian platform. The regional stage of thegeological-geophysical investigation of this hard to reach, remote but perspective province is going to the end.
The goal of our study is to develop an integrated volume model of the deep structure of the trough as abasement for scientific forecasting in hydrocarbon prospecting. Solid ground for this model formation in its
turn became the interpretation of the CCP seismic and MT soundings’ data of the regional profile network(with attraction of several DSS geotraverses). This profile analysis was based on the results of gravity andmagnetic fields’ zoning, 2D and 3D geophysical (potential fields and MT) inversions and the classification of
the main sedimentary and igneous rock formations by physical properties. This stage was finished by designof the spatial structural framework of the Mz sedimentary cover of the trough as well as underlain
consolidated older crustal and upper-mantle layers. Then, this layered construction was endowed withmaterial properties in the course of 3D inversions of gravity and magnetic field, constrained by profile seismicand MT data as well as 3D MT inversion (Slinchuk et al, 2022) results and geological information. Finally,layered distributions of density and magnetization with lateral inhomogeneities, which satisfy the observed
potential fields, were restored for all the YKRT. The main instruments for potential fields’ quality and quantity
analyses were presented by geoinformation system INTEGRO developed in All-Russian Research Institute of
Oil Geology RRIOG (Cheremisina et al., 2022), while MT data were inverted with a help of modern efficient
open access codes.
The presentation of the course of geophysical YKRT model construction is focused on the contribution ofmagnetotelluric data to restoring the deep architecture of the through: identification of fault zones, fluidized
areas and sedimentary grabens in profile sections, construction of the structural surfaces of the Pz formations
and crystalline basement, as well as localization and determination of the morphology of magmatic formationsof the period of geodynamic activation of the Pz-Mz boundary - buried intrusives and trap strata.
The information obtained is important for updating the structural-tectonic and geodynamic models of theYKRT – the main products of the regional stage of any oil and gas province investigations. Finally, we discuss
the possibilities of using the results of the study as markers of the main stages of the YKRT formation
associated with the evolution of the Khatanga local center of the Siberian superplume, as well as in theapplied aspect – for searching potential hydrocarbon traps.
The authors are grateful to colleagues from Nord-West Ltd for providing unique magnetotelluric data for the
integrated geophysical studies as well as for fruitful bilateral discussions of the results obtained. The research
was carried out as part of state assignments of All-Russian Research Institute of Oil Geology and Schmidt
Institute of Physics of the Earth, Russian Academy of Sciences.
Keywords: integrated interpretation of geophysical data, 2D and 3D inversions, deep architecture of oil andgas-bearing provinces, Yenisei-Khatanga regional trough, products of Siberian superplume activity_____________________________________________________________________________________

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Mapping the geometry of volcanic systems with magnetotelluric soundings: resultsfrom a land and marine magnetotelluric survey performed during the 2018-2019
Mayotte seismovolcanic crisis
Mathieu Darnet1, Pierre Wawrzyniak1, Pascal Tarits2, Sophie Hautot3 and Jean-François D’eu4
BRGM – French Geological Survey, Orléans, France – m.darnet@brgm.fr or p.wawrzyniak@brgm.fr
IUEM, Institut Universitaire Européen de la Mer, LGO, UMR 6538 - IUEM/UBO - pascal.tarits@univ-brest.fr
3
IMAGIR Sarl, Saint Renan, France - sophie.hautot@imagir.eu
4
MAPPEM Geophysics SAS, Saint Renan, France - jf.deu@mappem-geophysics.com
1

2

SUMMARY

A major seismovolcanic crisis has afflicted the islands of Mayotte, Comoros Archipelago, since May 2018,
although the origin is debated. Magnetotellurics (MT), which is sensitive to hydrothermal and/or magmatic
fluids and can map the subsurface electrical resistivity structure, can provide insight by revealing the internal
structure of the volcanic system. In this paper, we report the results of a preliminary land and shallow marine
MT survey performed on and offshore the island nearest the crisis. The 3D inversion-derived electricalresistivity model suggests that the island is underlain by a shallow ~500-m-thick conductive layer atop a
deeper, more resistive layer, possibly associated with a high-temperature geothermal system. At depths of ~15km, the resistivity drops by almost two orders of magnitude, possibly due to partial melting. Further
petrophysical and geophysical studies are underway for confirmation and to map the geometry and evolutionof the volcanic system.
Keywords: magnetotelluric, electrical resistivity, seismovolcanic, geothermal

INTRODUCTION
Mayotte, located along a WNW-ESE oceanic ridgeat the boundary of the Lwandle and Somalian plates,
represents a region of islands within the volcanic
Comoros Archipelago north of the Mozambique
Channel between the northern tip of Madagascar
and the eastern coast of Mozambique. The regionof Mayotte is composed predominantly of two mainislands, namely, Grande Terre (363 km2) to the west
and Petite Terre (11 km2) to the east (figure 1).
In May 2018, an offshore seismovolcanic crisis
initiated approximately 50 km to the east of Mayotte;
the crisis included the largest seismic event everrecorded in the Comoros with a Mw=5.9 and an
estimated 5 km3 of lava was released from aneruptive site in the same area (REVOSIMA bulletin,
http://www.ipgp.fr/fr/actualites-reseau).
The seismicity subsequently migrated to the west
and is now located between 5 and 15 km from thePetite Terre. The possible causes of the Comoros
volcanism continue to constitute a topic ofcontroversy (Lemoine et al., 2019), as its origin
could be related to i) the presence of a hot spot, ii)
lithospheric fractures, or iii) a combination of the two,i.e., regional extension in conjunction withasthenospheric processes. Forecasts regarding theevolution of this crisis remain very uncertain andrequire the gathering of additional geoscientific data,

particularly geophysical data, to help understandthe internal structure of the corresponding volcanic
system.
In this paper, we present the results of an land andshallow marine Magneto-Telluric (MT) survey
carried out during the seismovolcanic crisis of
Mayotte.
MT DATA ACQUISITION AND PROCESSING
Due to its high degree of urbanization, Petite Terreisland presents a challenging environment in whichto perform passive electromagnetic (EM)measurements. To mitigate the impacts of ambient
EM noise on the MT soundings, we deployed two
MT stations on the most isolated parts of the island(sites L1 and L2) and one remote reference MT
station on Grande Terre island (site L0),
approximately 15 km away from Petite Terre (figure1). We used ADU07 systems (Metronix, Germany)with unpolarizable Pb-PbCl2 electrodes (Wolf Ltd,
Hungary) and MSF07 magnetic coils (Metronix,
Germany). The sensors were oriented toward thenorth and east (x=north, y=east). MT recordings
were performed synchronously for 4 days at bothsites.

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Figure 1. Location of the MT stations deployedonshore and offshore Petite Terre island. The blue
triangles represent the MT sites. The green circles
represent the epicenters of the seismic events
recorded from May 2018 to May 2019. The red
ellipses with an arrow represent the phase tensor
ellipses at 1000 s at sites L0 and L2. Profile AA' is
the location of the cross-section through the 3D
resistivity model in Figure 3.
We processed the time series following the robust
approach of Chave and Thomson. (2004) with aremote reference MT station. We computed the full
impedance tensor at each site at periods ranging
from 0.001 to 1000 s. An MT sounding example isshown in figure 2. Overall, the data were of goodquality over the period range; however, at 1-10 s,noise could not be excluded due to the weakness ofthe primary field. The phase tensor ellipses shownin figure 1 were extracted from the phase tensors
calculated using the formula of Booker (2014).
Because of the limited number of sites favorable for
MT measurements on Petite Terre island, we
deployed four new-generation low-power shallow
marine MT systems (STATEM) around the island atwater depths ranging from 15 to 25 m (figure 1).
These STATEM systems were recently developedby MAPPEM Geophysics and the Ocean
Geosciences Laboratory (LGO), European Institutefor Marine Studies (IUEM). Each STATEM system
records the two horizontal components of theelectric field with 5m-long electric dipoles andmarine Ag-AgCl electrodes. The three componentsof the magnetic field were obtained from a 3component fluxgate sensor. With an optimized
datalogger, the measurements were performed
synchronously with the land stations for 2 days at asampling rate of 512 Hz. The design of the system
is such that motion of the system induced byoceanic current is minimized. During the survey,
bidirectional tiltmeter measurements performed
every second showed that motion and drift of thesensor were minimum (less than +/- 0.2 deg).

Similarly to the land case, the time series wererobustly processed with the bounded influence,
remote reference processing (BIRRP). The
processing of shallow marine MT data is achallenging task due to the high level of oceaninduced EM noise that can mask the MT signal. For
the electric field, noise can be generated not only by
the movement of the water layer (e.g., waves,swells, tides) within the Earth's magnetic field but
also by the current-induced motion of the electrodes.
Similarly, magnetic field measurements canbecome contaminated by noise associated with not
only ocean-induced electric currents but also the
motion of the sensor. As a consequence, bothelectric and magnetic measurements may exhibit
high levels of ocean-induced EM noise that is wellcorrelated and difficult to distinguish from the MT
signal. Therefore, the use of a land remotereference is of paramount importance to reduce theimpacts of ocean-induced noise. Remotereferencing between marine sites was sufficient atlong periods (>100 s); hence, the challenge was toobtain the MT impedance at the shortest possible
period (<0.1 s). Depending on the type of noise,magnetic and/or electric field measurements fromthe remote reference were used to filter out oceanic
noise. An example of the MT transfer function isshown in figure 2. Despite the lack of highfrequency measurements (<0.01 s) due to theweakness of the MT signals recorded beneath ~20
m of seawater, the MT transfer function was reliably
recovered over the 0.02 - 1000 s period band andwas consistent with the nearby land site (site L2).
Nevertheless, similar to the land data, the MT
impedance results at 1-10 s were not reliable, as
this period range is further impacted by a high levelof swell-induced noise.

Figure 2. MT soundings for sites L2 (left panel) andM2 (right panel). The upper and lower panels
display the apparent resistivity in Ohm.m and phasein degrees, respectively. The full lines signify the
responses of the best-fitting model.

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MT INVERSION RESULTS
The data recorded at the two land and the four
marine MT sites were jointly inverted to image theelectrical resistivity structure beneath Petite Terreisland. We inverted the four components of the MT
impedance tensors at all available periods; theperiod range for the land MT data was 0.009-1000s, and that for the marine MT data was 0.02-1000 s.
We excluded data with large errors, especiallywithin the dead band. For the 3D inversion, we used
the MININ3D code from Hautot et al. (2007). Given
the small number of sites, we used a grid of
21x18x18 cells, which included the bathymetry of
the study area. The total volume of the 3D model
was 21x20x13 km3. The horizontal dimensions of
the cells in the central part of the model is 500 x 500
m. The thickness of the layers increased from 5 to5000 m. The 3D model topped a 1D model withthree layers with thicknesses of 13, 38 and 88 km,
whose resistivities were also included in the
inversion. Except for the marine part (0.3 Ohm.m),the starting model was homogeneous (18 Ohm.m).
The 3D inversion was applied to minimize a misfit
function between the observed data and the 3Dmodel response at all sites and frequencies
weighted by the data variance. Data were the four
complex components of the MT tensor. The starting
RMS was 9.3, and the RMS decreased down to 2.2.

Figure 3. N120° cross-section through the 3D
resistivity volume obtained from the inversion of theland and marine MT data. Green circles represent
the hypocenters of the seismic events recorded
from May 1, 2018, to May 28, 2019\citep{lemoine2019volcano}.
Yellow
triangles
represent the MT sites used in the MT inversion.
To assess the uncertainties in the deep resistivity
structures identified in the 3D resistivity model, we
performed a sensitivity analysis on both theresistivity of the conductive layer (below a depth of
13 km) and the depth to the top of this conductor(figure 4). The misfit rapidly increases withincreasing resistivity below a depth of 13 km,
confirming that a conductor of less than 4 Ohm.m is
required to fit the data (figure 4a). The optimum
depth of this conductor was found at approximately
16 km (figure 4b). However, the misfit increases
slowly between this interval indicating that thisdepth is not very well resolved. We also tested thesensitivity of the model to the presence of the C2
conductor between depths of 5 and 13 km to thesoutheast of Petite Terre and on the edge of the MT
network (figure 3). The absence of this conductor
significantly increased the misfit (figure 4c),suggesting that this feature was not an artifact of theinversion process and was constrained by the MTdata.

A cross-section through the shallow (until 5km depth)and deep (until 50km depth) section of the 3D
resistivity model is shown in figure S2 and figure 3,
respectively. The most prominent feature is thepresence of a deep conductive layer (with aresistivity of less than 2 Ohm.m) beneath a depth of
13 km (labelled C1 on figure 3). A shallow
conductive layer (resistivity of less than 5 Ohm.m)is also present within the first 500 m of the model(labelled C3 on figure S2). Between these two
conductive structures, the resistivity increases up to~100 Ohm.m. Toward the southeast, the resistivity
in the 5-13 km depth range decreases to less than
10 Ohm.m (labelled C2 on figure 3); this conductor
is located close to the hypocenters of the seismic
events recorded during the seismovolcanic crisis.

Figure 4. Variation of the misfit between the tested
and preferred model as a function of a) the resistivityof the deep conductive layer, b) the depth of the topof this conductor and c) the presence of a conductor
between 5 and 13km depth to the South-East of
Petite Terre. The value 0 corresponds to thepreferred model.
DISCUSSION
The presence of a shallow conductive layer
overlying a more resistive body beneath the surface
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of Petite Terre (labelled C3 in figure 3) is consistent
with the electrical resistivity structure typicallyobserved under volcanoes exhibiting welldeveloped hydrothermal systems (Flovenz et al.,2005). According to these models, this shallow
conductive layer (resistivity of 1-10 Ohm.m)corresponds to a smectite-rich, low-temperature(<220°C), hydrothermally altered layer, often calleda clay cap. For Petite Terre, this layer would beapproximately 500 m thick. At greater depth andwith increasing temperature (>220° C), the material
is less rich in smectite, whereas the illite content
increases. Furthermore, porosity tends to decrease
with depth, which reinforces the modeled resistivity
increase due to the change in alteration productswith resistivity values ranging from 20 to 100
Ohm.m. On Petite Terre, such values are observed
below depths of 500 m and deeper and could
correspond to a high-temperature geothermal
reservoir.

CONCLUSIONS
Since May 2018, a major seismovolcanic crisis has
affected the islands of Mayotte in the Comoros
Archipelago, providing a unique opportunity tomonitor the development of an active volcanic
system. Preliminary MT data acquired on and near
this island were implemented to image the electricalresistivity structure of the volcanic system. The
resulting model suggests the presence ofhydrothermal fluids in the shallow part of the system(< 2 km) and magmatic fluids at greater depth (> 15km). Further petrophysical and geophysical studies(e.g. additional land and offshore MT surveys,
seismic surveys) are ongoing to confirm the origin
and geometry of these deep conductors and to help
better understand the associated magmatic andvolcanic activity.
ACKNOWLEDGEMENTS

At depths of 13-16 km, the resistivity drops byalmost two orders of magnitude, reaching values ofa few Ohm-meters (labelled C1 on figure 3). Theselow resistivities could be caused either by thepresence of altered rocks saturated with fluid below
the supercritical point (<400°C) or by the presenceof a small fraction of connected melt. At these
depths, the temperature exceeds the supercriticalpoint; hence, the most likely explanation for theobserved conductive layer is the presence of melt.
Similar observations have been reported beneathoceanic ridges on the basis of MT soundings (Baba,
2006) and interpreted as being indicative of thepresence of melt (Laumonier et al., 2017).
Nevertheless, additional laboratory data (e.g.,
electrical resistivity measurements on Mayotte
volcanicrock
samples)andgeophysicalobservations (e.g., seismic tomography) arenecessary to confirm this interpretation.
Finally, we noticed the presence of a conductive
structure in the 5-15 km depth range to thesoutheast of Petite Terre (labelled C2 on figure 3)
close to the seismic events recorded between May
2018 and May 2019. In this area, recent volcanic
material and gas emissions have been observed on
the seafloor. Accordingly, this conductive anomaly
could be related to recent seismovolcanic activity.
Additional deep marine MT sites are currently being
deployed in this area to obtain more insight into thepresence and geometry of this conductive anomaly
and its relationship with the regional seismovolcanic
activity. Furthermore, the land stations at sites L0
and L2 are currently being monitored continuously
to perform robust-processing of the deep marine MTsites but also long-term monitoring of any resistivity
changes at depth related to the evolution of theseismovolcanic crisis.

We would like to thank the General Directorate for
Risk Prevention (DGPR) for financially supporting
the geophysical work.
REFERENCES
Baba, K., Chave, A.D., Evans, R.L., Hirth, G.,
Mackie, R.L., 2006. Mantle dynamics beneaththe east pacific rise at 17 s: Insights from the
mantle electromagnetic and tomography (melt)
experiment. Journal of Geophysical Research:
Solid Earth 111.
Booker, J.R., 2014. The magnetotelluric phasetensor: a critical review. Surv. Geophys. 35, 7–
40.
Chave, A.D., Thomson, D.J., 2004. Bounded
influence magnetotelluric response function
estimation. Geophys. J. Int. 157, 988–1006.
Flóvenz, Ó., Spangenberg, E., Kulenkampff, J.,Árnason, K., Karlsdóttir, R., Huenges E., 2005.
The role of electrical interface conduction in
geothermal exploration. In: Proceedings of the2005 World Geothermal Congress. pp. 24–29.
Hautot, S., Single, R., Watson, J., Harrop, N.,
Jerram, D., Tarits, P., Whaler, K., Dawes, D.,2007. 3-d magnetotelluric inversion and model
validation with gravity data for the investigationof flood basalts and associated volcanic rifted
margins. Geophys. J. Int. 170, 1418–1430.
Lemoine, A., Bertil, D., Roullé, A., Briole, P., 2019.
The Volcano-Tectonic Crisis of 2018 East of
Mayotte, Comoros Islands
Laumonier, M., Farla, R., Frost, D.J., Katsura, T.,
Marquardt, K., Bouvier, A.S., Baumgartner, L.P.,
2017. Experimental determination of meltinterconnectivity and electrical conductivity inthe upper mantle. Earth Planet. Sci. Lett. 463,
286–297.

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3D CSEM inversion data at Campos basin Brazil constrained by seismic and well log
A. Benevides1, N. Meqbel1, W. Lima1, S. Fontes1, G. Egbert2, P. Werdt1 and E. La Terra1
1 Dept. Geophysics, Observatório Nacional Brazil, benevidesartur@gmail.com
2 Oregon State University, Corvallis, egbert@coas.oregonstate.edu

SUMMARY
A set of 3D CSEM inversion runs of a real data in the Campos basin, SE Brazil is presented in this study. The
Campos basin was formed during Neocomian (Cretaceous period 145–130 my) and its clastic reservoirs havebeen the largest oil producer in Brazil for the past three decades. This basin presents challenges in the
exploration of clastic reservoirs due to the deeper waters and the complex geological configuration imposedby the tectonics associated with giant salt domes. Electromagnetic (EM) methods are sensitive to subsurface
resistive variations and have been frequently used in exploration programs for hydrocarbon (HC). Reservoirs
filled with HC are generally more resistive than the host rocks which is an advantage for EM method.
Combining resistive models with seismic minimizes ambiguity in the interpretations and considerably improves
the resolution of the subsurface structures and the geometry of the reservoir targets. In this study, a set of 40
EM receivers were deployed at the seafloor (water thickness around 1.7 km) in a grid-shape array varying
spacing between 5 km to 10 km. The electric field components recorded from an active electromagnetic source
towed 50 m above the seafloor was processed and generated signal in four frequencies: 0.125, 0.25, 0.5 and1.25 Hz. Maximum polarization ellipses parameters obtained by rotating the horizontal electric components forthe same transmitter azimuth is used as measured data during the inversion. For the 3D CSEM inversion we
have used a modified version of the Modular System for EM inversion (ModEM code) which is underdevelopment within a research project at Observatório Nacional – Brazil. Preliminary resistivity model results
yielded a good agreement with resistivity well logs. Strategies including seismic features as a-priori information
has been analyzed and is showing improvements in resolution of the resistivity models.
Keywords: 3D CSEM inversion, Electromagnetic, Salt Structures, Campos basin

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Abstract, 25th EM Induction Workshop, Kuşadası, Turkey, September 13-19, 2020

Comparing results from a new bottom-towed CSEM system against seismic andcore data
Roslynn B. King1,2, Amy Gusick3, Steven Constable1, Amy Gusick3, and Jillian M. Maloney2
1 Scripps Institution of Oceanography, University of California San Diego, California, USA,rbking@ucsd.edu, sconstable@ucsd.edu
2 Department of Geological Sciences, San Diego State University, California, USA,jmaloney@sdsu.edu
3 Department of Anthropology, Natural History Museum of Los Angeles County, California, USA,agusick@nhm.org

SUMMARY
Bottom-dragged marine EM systems minimize the attenuating effect of the seawater as well as achieve
maximum coupling to the seafloor. Such systems have been developed to characterize and map the top tensof meters of the seafloor to study a range of targets from groundwater discharge to gas hydrates. However,the use of systems dragged across the seafloor is limited to settings that are heavily sedimented and without
protective status. This limitation can significantly restrict potential survey areas as nearly 41 percent of U.S.
marine waters have protected status and many offshore infrastructure projects require baseline studies during
which the seafloor should not be significantly altered. To survey these regions, we are developing a controlledsource electromagnetic (CSEM) system that minimizes impact on the seafloor and is sensitive to the upper 50
meters of the subseafloor. The new CSEM system can be deployed in culturally and biologically sensitive
regions and is designed to resolve small (∼3 meter wide and 20 cm thick) subtle targets. The system is neutrally
buoyant, flying between 1 to 2 meters above the seafloor, with a small counterweight to minimize impact on
the seafloor. The system is made up of a transmitter that doubles as a depressor weight and three receivers
for a final array length of 40 meters. The transmitter emits a waveform between 1 to 5 amps on a 2-meter
horizontal electric dipole which is received by triaxial receivers. From preliminary results, the system canresolve the porosity of the shallow subseafloor with good agreement with seismic and core data. Initial
inversions generated from a survey offshore California, USA indicate significant improvement in resolution
from the new CSEM system when compared to surface-towed CSEM systems. Here, we will present resultsfrom 3 case studies testing the sensitivity and resolution of the new CSEM system.
Keywords: Marine CSEM; Near seabed resistivity; Resolution

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Multi- EM surveying and data analysis for deep-sea seafloor massive sulfide
exploration
K. Schwalenberg1, H. Mueller1, U. Barckhausen1, and the INDEX Exploration Team1 BGR - Federal Institute for Geosciences and Natural Resources, Hannover, Germany,

katrin.schwalenberg@bgr.de

SUMMARY
Deep-sea hydrothermal vent fields associated with the formation of seafloor massive sulfides (SMS) may
become a future source of high-tech minerals such as Cu, Ni, Pb, Co, and REE, which are all demanded forthe energy transition away from hydrocarbon resources. The identification and evaluation of the-deposits inthe deep ocean is a needle in a haystack problem. They are relatively small (size of a soccer field) and formin complex terrain at mid-ocean ridges, island arcs and back-arc spreading centres. The plume of activehydrothermal systems can be traced in the water column; however, active hydrothermal vents have anabundant, environmental sensitive fauna and low mineral potential. Inactive and extinct vent sites are generally
missing characteristic seafloor expressions such as black smokers and distinct vent fauna, and may be hidden
under a thin layer of sediments. An important aspect in mineral resource assessment is the inner structure andspatial extent of the SMS deposits, in particular their dimensions in depth, which can be addressed bygeophysical methods.
In addition to magnetic and bathymetric surveying, we have collected a variety of marine frequency domain
CSEM loop data and electric dipole-dipole data (DC conductivity, induced polarisation and self potential) overactive and inactive hydrothermal vent fields associated with seafloor massive sulfides in the German licenceareas located in the Indian Ocean. The data have been collected using BGR’s unique deep-sea GOLDEN EYECSEM profiler and newly acquired tandem Vulcan E-field receivers for self potential measurements. First
results from the most recent cruise in 2021 indicate several anomalous features in the data that agree wellwith previously known, visually identified sulfide occurrences. The interpretation of the data is challenged by
the complexity of the terrain and navigation issues. In our presentation we will give an overview of the current
status of the data analyses and discuss our next steps to derive the 3D structure of the subseafloor mineraldeposits.
Keywords: marine electromagnetics, seafloor massive sulfide exploration

Figure 1. Left: Golden Eye recovery on RV Pelagia.
Top: Vulcan E-field receiver being deployed at the aft
deck.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Characterizing Offshore Freshened Groundwater in a Carbonate Shelf Using
Integrated Geophysical and Geochemical Analysis: A Case Study from the Maltese
Islands
Zahra Faghih1, Amir Haroon1, Marion Jegen1, Christian Berndt1, Bradley A. Weymer2, Konstantin Reeck1,
Thomas Müller1, Mark Schmidt1& OMAX Scientific Party
1GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
2Shanghai Jiao Tong University, China

SUMMARY

Coastal carbonate lithologies host considerable quantities of potable freshwater worldwide. However, thecontinuation of the terrestrial aquifers across the coastline into the marine realm, or the existence of carbonate-hosted offshore freshened groundwater (OFG) in general, are poorly constrained. Distribution, geometry,volume as well as the controlling factors of freshened groundwater beneath the present-day seafloor remain

uncertain in many places across the world. To investigate whether freshened groundwater can exist offshore

a semi-arid carbonate coastline like Malta and assess if such resource has the potential to serve as analternative unconventional source of drinking water is addressed within a joint project between University ofMalta and GEOMAR Helmholtz Centre for Ocean Research Kiel. Presently, OFG has been documented incoastal embayments and continental shelves worldwide. Geophysical studies carried out in Israel, New

Zealand, Malta and the United States of America provide good examples of how integrating geophysicalapproaches and borehole data constrain the spatial extent of OFG systems, as well as provide estimates on

the salinity of the pore-water. Here, we present an integration of marine controlled source electromagnetic(CSEM) data with 2-D seismic reflection profiles, core samples, geochemical data and borehole measurements

targeting OFG along a carbonate shelf offshore the Maltese Islands. Electrical resistivity models derived from

2-D inversion of CSEM data identify a resistive anomaly (> 10 Wm) offshore the northeastern coast of Gozo. If

this resistivity anomaly is associated with a groundwater body at a depth of ~ 300 m below sea level or,alternatively, caused by lithological variability is discussed through an integrated geophysical model using 2-D

resistivity models, multi-channel seismic reflection data and in-situ geochemical measurements. Preliminary

results show that the resistivity anomalies indicate an OFG body located within the seafloor east of Gozo which

extends northeastward. Furthermore, hydrogeological modeling is planned to quantitatively characterize thepotential OFG system offshore Maltese Islands.

Keywords: Offshore Groundwater, Controlled Source Electromagnetics, Carbonate Margin, Integrated
Geophysics

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

The dependence of the tsunami electromagnetic signals observed at islands on thesubsurface resistivity
R. Shibahara1, T. Minami2
1Kobe University, Japan, 225s414s@stu.kobe-u.ac.jp

2Kobe University, Japan, tminami@port.kobe-u.ac.jp

SUMMARY

The electromagnetic (EM) field variations due to large tsunamis have been observed at seafloor
stations and land ones close to coastlines. If the subsurface resistivity affects the tsunami EM
signals, the phenomena could be applied to investigation of the resistivity structure as well as
prediction of tsunami arrivals and heights. As for tsunami EM signals at the flat seafloor, Shimizu
and Utada (2015) revealed that the EM field variations due to long waves are less affected by thesubsurface resistivity. However, the dependence of the EM field observed at land and island stations
has not been clarified and needs further investigation.
In this study, we investigated the subsurface resistivity dependence of the tsunami EM singles on
island by numerical calculations for the following two cases. In Case 1, plane waves propagate in aflat seafloor with a constant depth and passes through a simple conical island; in Case 2, the 2011
Tohoku tsunami reached Chichijima Island. In both cases, we assumed a simple semi-infinite
homogeneous resistivity structure below the island and the adjacent seafloor, and calculated thetsunami EM signals using the TMTGEM tsunami EM simulation code (Minami et al. 2017). In the EM
field calculations for Case 1, we tested several radii of the island and three homogeneous subsurface
resistivities of 1000,100, and 10 Ωm. We found in the result of Case 1 that the amplitude of themagnetic vertical component (Bz) varies by a maximum of 10% and the electric horizontal
component (Eh) does by a maximum of 40%, in terms of those for 1000 Ωm. The variations in theamplitude were not so affected by the radius of the island. In Case 2, the case of Chichijima, weused the fault model of the 2011 Tohoku earthquake estimated by Tatehata et al. (2015) as thetsunami source for tsunami simulation and tested three subsurface resistivities in the same manneras Case 1. The results of Case 2 show that the amplitude of Eh varies by a maximum of 60%, andthat of Bz varies by a maximum of 30%, in terms of those for 1000 Ωm. The results both in Case 1
and 2 indicate that the tsunami-generated Eh on the island is affected by the subsurface resistivity
and may be possible to utilize in exploring the resistivity structure of islands. We succeeded in
explaining the dependence of the amplitude of Eh on the subsurface resistivity by a simple stationary
parallel circuit model. The stationarity of the circuit model accounting for the simulation results
implies that the tsunami-generated electric field variation observed at islands are explained by thestationary process, and that the resistivity structure below the island could be explored by thetsunami electric sounding.
Keywords: tsunami, island, resistivity, simulation, FEM

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Revisit of the mantle electrical structure beneath the Tristan da Cuna hotspot byusing a 3-D inversion based on non-conforming deformed hexahedral mesh
Singh, R.K.1, Baba, K.2, Usui, Y.3, Grayver, A.4, Jegen, M. 5, Morschhauser, A.6, Geissler, W.7,
Matzka, J.8 , Haroon, A.9 and Kuvshinov, A10
1ERI, The University of Tokyo, roshan@eri.u-tokyo.ac.jp
2ERI, The University of Tokyo, kbaba@eri.u-tokyo.ac.jp
3ERI, The University of Tokyo, yusui@eri.u-tokyo.ac.jp
4ETH, Zürich, agrayver@ethz.ch
5GEOMAR, Helmholtz Centre of Ocean Research Kiel, mjegen@geomar.de
6GFZ, Potsdam, mors@gfz-potsdam.de
7AWI, Helmholtz Centre for Polar and Marine Research, Wolfram.Geissler@awi.de
8GFZ, Potsdam, jmat@gfz-potsdam.de
9GEOMAR, Helmholtz Centre of Ocean Research Kiel, aharoon@geomar.de
10ETH, Zürich, kuvshinov@erdw.ethz.ch

SUMMARY
The Tristan da Cunha islands are located 450 km east of the Mid-Atlantic Ridge and 60 km north of the Tristan
da Cunha transform fault, and fracture zone system in the Atlantic Ocean. The islands are products of intraplate
hotspot volcanism. However, several controversies are associated with theories for the origin of the Tristan da
Cunha hotspot. Previous magnetotelluric data 3-D inversion results based on finite-difference algorithm
couldn’t image plume-like vertical structure, unlike many geological and geochemical studies (Baba et al.,2017). In this study, we revisit the electrical structure of the region by inverting combined vertical magnetictransfer functions and the MT responses from seafloor stations around the islands and those newly obtainedon the Tristan da Cunha and Nightingale islands. The topographic effect is one of the keys to obtaining a morereliable subsurface structure, and therefore, we use two finite-element-based inversion methods of Usui (2021)
and Grayver (2015). For inversion analysis, we compared the topographic responses from these methods anda finite-difference-based forward modeling method (Baba & Seama, 2002; Baba & Chave, 2005) used in theprevious study (Baba et al., 2017). The responses for sites with minimum topography effects are almost
identical from all the three methods except for some sites where difference is observed in shorter periods. This
could be due to the difference in the mesh resolution around the sites. The responses for sites with significant
topography effects show a reasonable match for the finite-element methods in longer periods. In contrast asignificant difference is observed in responses from all three methods for short periods. The comparison ofmodeled responses proves the applicability of Usui’s inversion scheme to MT data from this region. In theworkshop, the inversion results from Usui’s inversion method will be presented.
Keywords: Non-conforming deformed hexahedral mesh, Finite-element, Marine magnetotelluric

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Imaging deep resistivity in 3D in coastal areas and volcanic islands:
Toward a multi-method and multi-scale approach combining land and shallow water
passive and active EM
F. Bretaudeau1, S. Védrine1,2, P. Tarits2, J-F d’Eu3, Q. Daverdisse3, N. Coppo1, P. Wawrzyniak1, S.
Hautot4, F. Dubois1, E. Civallero1, F. Beaubois1, Y. Legendre1, M. Darnet1
1BRGM (French Geological Survey), f.bretaudeau@brgm.fr
2GEO-OCEAN UMR6538, University of Western Britany, France, pascal.tarits@univ-brest.fr
3Mappem Geophysics, France, jf.deu@mappem-geophysics.com
4IMAGIR Sarl, France, sophie.hautot@imagir.eu

SUMMARY
3D resistivity imaging with Electromagnetic (EM) methods is commonly used in volcanic context. It isparticularly useful to characterize geothermal reservoirs, thanks to the sensitivity of resistivity of the variationof types and levels of alteration (mapping clay cap to delineate reservoir). However, application of EM
exploration techniques on coastal areas of volcanic islands can be challenging due to the high level ofanthropogenic noise, proximity to the sea/land interface, strong variations in topography and near-shore
bathymetry, and near-surface heterogeneities.
Magnetotellurics (MT) is the historical method and the only one capable of sounding at very large depths(>3km). But anthropogenic noise related to urbanization often limits its performances and therefore often
restricts its use to quiet areas and coarse mesh surveys. Controlled Source EM in land, with the use of powerfulactive current source and lighter recording systems is an interesting alternative to overcome anthropogenic
noise and increase data coverage and resolution close to populated or industrialized zones, but is more limited
in depth (0-2km). Finally, Airborne EM shows limited investigated depth (0-300m) but can provide very dense
coverage in the near surface poorly sampled by CSEM and MT.
Furthermore, all those methods are now quite common in land, but their use along the coast and in particular
in shallow water is not obvious and almost never considered, whereas there is no reason for coastal
geothermal reservoirs to be limited by the coastline. The two main reasons are: (1) costs and availability of
deployment of adapted coastal /shallow water equipment (2) difficulty of modeling and inversion processing of
the EM data considering accurately the topography, bathymetry and land/sea high resistivity contrast.
We try to summarize here the progress made in recent years to try to overcome those limitations and provide
deep resistivity models of geothermal reservoirs bellow coastal areas on volcanic islands, based on the
feedback from various coastal MT and CSEM surveys. We discuss the improvement provided by:
1) adapted instrumentation for shallow water MT2) adapted instrumentation and protocols for land/shallow water CSEM
3) optimized processing of marine MT4) 3D accurate modeling including topography/bathymetry and coastline
5) Marine shallow electrical resistivity mapping
6) 3D inversion of multi-method multi-scale workflow of MT/CSEM and including shallow resistivity
We illustrate the potential of this multi-scale multi-method approach with a large campaign conducted over theoperated Bouillante geothermal field in Guadeloupe in the French Lesser Antilles, where a joint land/marine
CSEM/MT + AEM + marine streamer was conducted in 2021-2022.
Keywords: Coastal EM, Volcanic islands, geothermal exploration, MT, CSEM, AEM, multi-method, multi-scale,joint 3D inversion

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Links between slab mantle dehydration and forearc seismogenic zone structure in
the Shumagin Gap, Alaska using magnetotelluric imaging
D. Cordell1, S. Naif2, R. Evans3, K. Key 4, S. Constable5, D. Shillington6, and A. Bécel7
1
Georgia Institute of Technology, dcordell6@gatech.edu
2Georgia Institute of Technology, snaif3@gatech.edu
3Woods Hole Oceanographic Institution, revans@whoi.edu
4Lamont-Doherty Earth Observatory, Columbia University, kkey@ldeo.columbia.edu
5
Scripps Institution of Oceanography, sconstable@ucsd.edu
6
Northern Arizona University, donna.shillington@nau.edu
7Lamont- Doherty Earth Observatory, Columbia University, annebcl@ldeo.columbia.edu

SUMMARY
Fluids in subduction zones influence many important geological processes as the hydrated incoming plate
lithosphere (i.e. slab) releases pore-bound and chemically-bound water into the over-riding crust and mantle
wedge. In particular, fluids released from the slab beneath the forearc seismogenic zone are thought to create
conditionally-stable regions which have strain-dependent slip behavior and may control megathrust rupture.
The Shumagin Gap is unique along the Aleutian arc for its paucity of historical megathrust (>8 Mw) rupture
nucleating in the gap. Geodetic data suggest that gap is weakly-coupled relative to adjacent segments. The
reasons for the weak coupling and lack of megathrust rupture are unclear. Furthermore, the adjacent Semidi
segment ruptured in July 2020 with Mw 7.6 and propagated into the Shumagin Gap. To examine the role offluids, marine magnetotelluric (MT) data were collected along a 250 km trench-perpendicular profile in 2019as part of the Electromagnetic Alaskan GeoPRISMS Experiment. The resulting 2-D resistivity model contains
a conductor near the plate interface and is correlated with a wide-band of reflectors from seismic studies,seismicity, and the July 2020 rupture patch. This suggests that fluids play an important role in controlling theslip behavior in the Shumagin Gap. The source of the fluids at the plate interface is commonly thought to comefrom the downgoing slab crust. However, the MT results indicate a significant slab mantle conductor >15 km
below the slab Moho, directly beneath (and connected to) the conductor at the plate interface. This suggeststhat deeper slab mantle serpentinization may reach pressure-temperature conditions conducive to dehydration
beneath the forearc seismogenic zone. This provides an additional source of forearc fluids not previously
considered in subduction zone water budgets which can modulate slip behaviour.
Keywords: magnetotellurics, subduction zone, seismogenic zone, dehydration, Alaska

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Modelling 3D coast effects in marine magnetotelluric data using edge-based finite
element method
Jianbo Long, Shunguo Wang
jianbo.long@ntnu.no; shunguo.wang@ntnu.no
Norwegian University of Science and Technology (NTNU), Trondheim, Norway

S UMMARY
Magnetotelluric method has been an important method in investigating offshore geologic structures in a variety
of applications. Previous studies have shown that coast distortion can affect measurements over a great
distance. Often, 2D models are used in marine magnetotelluric studies, partly because the 2D assumption is
acceptable in test regions and partly because 3D tools are unavailable. Here, we have developed a vector finite
element based modelling code to study the 3D coast effect. Initial test results have confirmed the modelling
accuracy and we are now able to analyze 3D coast effects over more complex scenarios.
Keywords: marine magnetotellurics, coast effect, finite element, modelling

I NTRODUCTION
Coast and bathymetry effects are the primaryterrain-related distortions when interpreting marine
magnetotelluric (MT) data. In some experiment environments such as mid-ocean ridges, the underlying earth model may be approximated as a twodimensional (2D) conductivity model where the conductivity does not change along the strike direction(i.e., ocean ridges). In this case, MT data analysis iscommonly separated into that of transverse electric(TE) mode and transverse magnetic (TM) mode.
Coast effect does not just affect the close region ofcontinental margin, but can propagate to a long distance (Worzewski et al, 2012). In the TE mode of
the 2D scenario, the effect is due to the induction
coupling between the coast and the rest of the earthmodel (i.e., sea water and sea floor) since the electrical current flows in parallel to the strike direction
and no electric charges are built on conductivity interfaces. In the TM mode, in addition to the induction, galvanic diffusion of the electromagnetic (EM)field will also play a part in distorting the MT measurements. In both modes, these distortions canbe indicated by the existence of abnormal apparent
resistivity values and phase changes, which havebeen well observed in field (e.g., Key and Consta-

ble, 2011) and in numerical modellings (e.g., Santos et al, 2001; Worzewski et al, 2012; Wang et al,2019).
In the three-dimensional (3D) scenario, it is reasonable to expect that both induction and galvanic diffusions will be present. In addition, unlike the relatively flat seafloor surface around mid-ocean ridges
that can often be approximated as 2D models, 3D
coastal lines are much more ubiquitous. These factors present a very challenging but important question: how can the coast distortions be recognized(and then removed if possible), or be taken into consideration in interpreting 3D MT data ?
Here, we have developed a 3D finite element modelling code in the hope that it will allow us to accurately model the coast effects on marine MT data,thus allowing us to better recognize 3D coast effects
in real data. The code utilizes unstructured tetrahedral meshes which allow for efficient and accurate
representation of coastal lines and bathymetry.

M ODELLING CODE
Our finite-element based forward modelling code isdeveloped using modern FORTRAN features and
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x (km)

can compute both 2D and 3D MT responses using
unstructured meshes. 2D triangular and 3D tetrahedral meshes are used in this study and are generated using software Triangle (Shewchuk, 1996) andTetgen (Si, 2015), respectively.

0

500

20

1000

1500

2000

2500

3000

3500

Air
Sea water, 0.3 Ωm (3.3 S/m)

0z (km)

Coast−20

Seafloor
10000 Ωm

−40

Continent
10000 Ωm

−60
Basement, 10 Ωm

−80
−100

E QUATIONS AND BOUNDARY CONDITIONS

Common equations solved in the modelling include electromagnetic potential equations and theHelmholtz equation for electric field, E or magnetic
field, H. To best model the coast distortion in the
MT data, we have chosen the E-field equation. Thisis because the coastal lines can extend to a great
distance making the vertical surfaces of a 3D model
as essentially 2D models, as shown in Figs 1 and 2.
On 2D boundary surfaces, electric charges will accumulate when the external electric field is parallelto the surfaces (similar to the TM mode of a pure 2Dmodel), giving rise to possible discontinuities in EM
potential functions. In this case, specifying boundary EM field values will be much easier than specifying potential function values. Boundary E field values are from solving 2D modelling problems overthe 2D boundary surfaces.

Figure 1: Example 3D tetrahedral mesh of part of
the test 3D marine conductivity model with avertical coast at x = 2500 km. Vertical boundary surfaces in the x−z plane are 2D surfaces.

Figure 2: Corresponding 2D conductivity model ofthe 3D model shown in Fig1. Sea water layer
has a thickness of 5 km and conductivity of 3.3
S/m.

E XAMPLE MODELLING RESULT

Our initial test model, which is shown in Figs 1 and
2, is a relatively simple 3D marine model with only 3
conductivity regions: sea water (3.3 S/m), seafloor(1.e-4 S/m) and an asthenosphere basement (0.1
S/m). The coast boundary is located at x = 2500
km. The sea water has a depth of 5 km. The topof the conductive basement is at z = -50 km. These
parameters are taken from the example model from
Wang et al (2019). A series of frequency value ranging from 1.e-5 Hz to 0.5 Hz was used to test themodelling. The actual computational domain of thistest extends from x = -500 km to x = 4000 km in
the horizontal direction and from z = -200 km to z
= 80 km in the vertical direction. Due to the use of
unstructured meshes, we were able to locally refine
the mesh around regions of interest.
Since the conductivity contrast between sea waterand the coastal region is high, large coast effect canbe expected at measurements along sea floor. The
calculated 3D impedance tensor, expressed in apparent resistivity and phase values, is shown in Fig 3
at site x = 2050 km, y = 0 along the seafloor (1 meter above seafloor), which is 450 km away from thecoastal line.

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a)

102
100

10−4
10−4

10−3

10−2

10−1

c)

90
0
−90
−180
10−6
104

e)
3

10

10−5

10−4

10−3

10−2

10−1

100

Zyx

102
101
100
10−1
10−2
10−6
180

10−5

10−4

10−3

10−2

10−1

g)

0
−90
−180
10−6

10−5

10−4

10−3

10−2

10−1

Frequency (Hz)3D result (Site x=2050 km)

100

b)

C ONCLUSIONS

Zxy

101
100
10−1

180

10−5

10−4

10−3

10−2

10−1

100

10−5

10−4

10−3

10−2

10−1

100

d)

90
0
−90
−180
10−6
104

f)

102

3D coast effects are expected to be more complex
than the 2D case. Our finite-element based modelling code for 3D coast effects has been initiallytested and shown to be capable of accurately modelling the effect. More complex 3D marine conductivity models, possibly with bathymetry information,
will be tested in the next stage.

Zyy

100

ACKNOWLEDGMENTS

10−2
10−4
10−6
10−6

100

90

102

10−2
10−6

100

Phase φ (deg)

180

10−5

App Res ρ ( Ω m)Phase φ (deg)App Res ρ ( Ω m)

Zxx

10−2

10−6
10−6

Phase φ (deg)App Res ρ ( Ω m)

104

Phase φ (deg)App Res ρ ( Ω m)

Long and Wang, 2022, Modelling 3D MT coast effects

180

10−5

10−4

10−3

10−2

10−1

100

10−4

10−3

10−2

10−1

100

h)

90
0
−90
−180
10−6

10−5

The study was funded by the Norwegian ResearchCouncil (294404, 309960, and 324442) and the National Infrastructure for High-Performance Computing and Data Storage in Norway (nn9872k).

Frequency (Hz)Previous 2D (Site x=2050 km)

R EFERENCES
Figure 3: Apparent resistivity and phase values of
4 components of impedance tensor calculated
at the site x = 2050 km (see Fig 2). Previous
2D result is from Wang et al (2019)

The 3D result is seen to be in good agreementwith the 2D results previously presented in Wanget al (2019), with the latter only having TE (corresponding to Zyx here) and TM mode (corresponding to Zxy here) impedance components. Since thismodel is essentially the “3D version” of that 2D marine model from Wang et al (2019), the diagonals of
the impedance tensor are expected to be trivial, as
shown by the very small values of Zxx and Zyy here.
The larger errors at high frequencies of the results,
particularly in Zyx, are due to limited size of meshused in the test.
Further vector E field flows are plotted in Fig 4 forthe polarization mode of having the MT source E
field parallel to the x − z plane (i.e., TM mode in
a 2D case). The vertical section of the plot is at
y = 0. Results similar to previous 2D modellings
are observed here. For example, there are only
dominantly downward flows of current density in theresistive seafloor at low frequency, but at high frequency, downward flows of current density near thecoast are seen upward flowing back to the sea water, causing coast effect at great distances awayfrom the coast line.

Key K, Constable S (2011) Coast effect distortion ofmarine magnetotelluric data: Insights from a pilot
study offshore northeastern Japan. Physics of the
Earth and Planetary Interiors 184(3-4):194–207
Santos FAM, Nolasco M, Almeida EP, Pous J,
Mendes-Victor LA (2001) Coast effects on magnetic and magnetotelluric transfer functions andtheir correction: application to MT soundings carried out in SW Iberia. Earth and Planetary Science Letters 186(2):283–295
Shewchuk JR (1996) Triangle: Engineering a 2D
quality mesh generator and Delaunay triangulator. In: Workshop on Applied Computational Geometry, Springer, pp 203–222
Si H (2015) TetGen, a Delaunay-based quality tetrahedral mesh generator. ACM Transactions onMathematical Software (TOMS) 41(2):11
Wang S, Constable S, Reyes-Ortega V, Rychert
CA (2019) A newly distinguished marine magnetotelluric coast effect sensitive to the lithosphere–
asthenosphere boundary. Geophysical JournalInternational 218(2):978–987
Worzewski T, Jegen M, Swidinsky A (2012) Approximations for the 2-d coast effect on marine magnetotelluric data. Geophysical journal international
189(1):357–368

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x (km)
0

500

1000

1500

2000

2500

3000

2

z (km)

−20

−40

0

−60

−2
−80

Freq = 0.10000E-04

−100

−4

x (km)
0

500

1000

1500

Log10 E (V/m) -- real

0

2000

2500

3000

2

z (km)

−20

−40

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−60

−2
−80

Freq = 0.22361E-02

−100

−4

x (km)
0

500

1000

1500

Log10 E (V/m) -- real

0

2000

2500

3000

2

z (km)

−20

−40

0

−60

−2
−80

Freq = 0.49212E-01−100

Log10 E (V/m) -- real

0

−4

Figure 4: Calculated vector E field distribution in the x − z plane with y = 0 from the 3D model at three different
frequencies. Only real part of E field is shown. At frequencies f = 2.23e-3 Hz and f = 0.049 Hz, some
vector arrows of the field become more random than others; this is due to insufficient modelling accuracy
caused by limited element sizes of the mesh in those regions. Current density flows follow the same of
E field.
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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Multi-Data Inversion Approach for Retrieving Rock Properties
from Measurements on Drill Cuttings
J. H. Börner1 , V. Herdegen2 , J.-U. Repke3 and K. Spitzer1
Institute of Geophysics and Geoinformatics, Technical University Bergakademie Freiberg, Germany;jana.boerner@geophysik.tu-freiberg.de2
Institute of Thermal, Environmental and Natural Products’ Process Engineering, Technical University
Bergakademie Freiberg, Germany
3
Process Dynamics and Operations Group, DBTA, Technical University Berlin, Germany
1

S UMMARYElectromagnetic methods can be of great value in monitoring applications (e.g. for geothermal energy, carbon
dioxide sequestration, enhanced oil recovery, natural gas discharge zones) due to their sensitivity to reservoir
fluids and their properties. Quantitative evaluation of monitoring data requires knowledge of reservoir-specificpetrophysical relationships. As sample material for establishing these petrophysical relationships, drill cuttings
are often the only rock material available from deep formations due to related high costs and time-consumptionof coring. Therefore, estimating various formation properties from cuttings is a common task. However, forphysical rock properties like complex electrical conductivity this formation property retrieval is challenging.
We therefore systematically investigated the electrical and structural properties of four different carbonate rocks
and one sandstone in both crushed (8 different particle sizes, between 0.03 mm and 10 mm particle size)and plug form with a vast set of methods at normal conditions: complex-valued electrical conductivity, NMR,
mercury intrusion porosimetry, nitrogen adsorption, XRD, µCT, scanning electron microscopy. By means of thehuge data set we are able to quantify the pore-space, surface and electrical properties as functions of particle
size, understand their relations, differentiate between intra- and inter-particle responses. The samples show
significant variation in their electrical conductivity depending on salinity, porosity, pore-space heterogeneity and– due to the peculiarities of the crushed samples – the organization of particles in the packing.
We present a three-level model for the crushed rock material, which accounts for particle size and inner structure. Based on this model, it is possible to computationally recover the corresponding properties of the original,
undisturbed formation. We realize this by means of a multi-data inversion of complex electrical conductivity,
specific surface and porosity data for each rock. The presented inversion approach allows for utilizing theavailable drill cuttings, while maintaining the direct connection to the original rock.
Keywords: Electrical rock conductivity; Petrophysical joint inversion; Microstructures

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Comparative 3D inversion of magnetotelluric phase tensors and impedances
reveals electrically anisotropic base of Gawler Craton, South Australia
K. Tietze1,2,3, S. Thiel1,2, K. Brand1,2,4 and G. Heinson2
1 Geological Survey of South Australia, Department for Energy and Mining, Adelaide
2 School of Physical Sciences, University of Adelaide
3 Now at: Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Telegrafenberg,

Potsdam,
4 Now at: Australian Space Weather Forecasting Centre, Bureau of Meteorology, Adelaide

kristina.tietze@gfz-potsdam.de

SUMMARY
Isotropic three-dimensional (3-D) inversion has become a standard tool in the interpretation of magnetotelluric(MT) data. 3-D anisotropic inversion codes are under development, yet the number of unknowns increases by
a factor of 6 rendering the problem extremely ill-posed. Presence of anisotropy is usually inferred from (i)spurious sequences of conductive and resistive bodies or (ii) comparison with two-dimensional anisotropic
modelling approaches.
Here, we investigate the 3-D structure of the Gawler Craton down to ~250 km depth using 282 sites of the
AusLAMP array located in the southern half of South Australia. Inversions of the MT impedance as phasetensors and real and imaginary parts result in diverging structures at depths > 70 km. We demonstrate that aunifying model which explains all data types similarly well is suggestive of an anisotropic resistivity structure
at the base of the Gawler Craton lithosphere at depths of 120-210 km. Depth location and orientation of theanisotropy agrees well with results from analysis of seismic receiver functions. We suggest that electricanisotropy in the Gawler Craton is a result of lattice-preferred orientation of olivine crystals and metasomatic
processes with macroscopic preferential orientation.
Our results illustrate that inversion of phase tensor data is superior for the direct imaging of anisotropic
resistivity contrasts in otherwise isotropic resistivity models; inversion models obtained with impedances may
miss such structures. ‘Comparable’ overall RMS misfits are often meaningless when comparing inversion
results for various data types since sensitivities differ between data types. Reliable inversion results consistent
with the entire data set can only be recovered if data fits are assessed systematically for all data
representations. We also discuss the influence of error settings for phase tensors on the inversion.
Our study also revealed that, if persistent across large areas, (i) parallel orientation of phase tensor major axes,(ii) constantly high phase tensor maximum phases or (iii) diverging directions of phase tensor major axes andinduction arrows are suggestive of anisotropic structures and corresponding hypotheses should be evaluated.
Keywords: Give 1 to 5 keywords

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Electrical monitoring of dynamic drainage and imbibition processes in rockfluid-gas systems
Martin Sonntag 1, Jana Börner2, Volker Herdegen 3, Franz Grahl 3, Klaus Spitzer 2
1 TU BAF, Institute of Geophysics and Geoinformatics, msonntag@geophysik.tu-freiberg.de
2 TU BAF, Institute of Geophysics and Geoinformatics
3 TU BAF, Institute of Thermal-, Environmental- and Resources Process Engineering

SUMMARY
Understanding physico-chemical interactions and quantification of reaction kinetics during dynamic processes
in multi-phase systems and their impact on the electric conductivity can help to interpretation EM-monitoringdata. Therefor a multistage experimental setup was developed and performed on different reactive and nonreactive rock-fluid-gas systems.
The results shows that the real part of the el. conductivity decreases by about -90% in the dewatered states
and after imbibition the el. conductivity increases differently depending on material and measuring height. A
combination of reactive materials show the strongest conductivity changes after cycles of dynamic processes.

Keywords: Spectral Induced Polarization, carbonate rocks, non-equilibrium condition

ABSTRACT
Carbonate rocks host large fossil oil and natural gas
deposits and are targets for Enhanced Oil Recovery
and Carbon Capture and Storage applications.
Although electromagnetic methods can be used formonitoring. The understanding of the electricalproperties of carbonates is still incomplete due totheir heterogeneous pore space and reactivity
towards aqueous phases, especially in thepresence of CO2 (dissolution and precipitation
processes).
Therefore, a systematic multistage experimental
procedure was developed as well as the successful
realization of test series on different multiphasesystemsforelectricalmonitoring
andcharacterization of dynamic processes.
In each case, a combination of sand or carbonate
as rock matrix, and nitrogen or CO2 as gas
component was used for the individual experiments.
Thus, the combinations of rock matrix and gas canbe divided into subsystems with different degrees ofreactivity.
It can be seen that the carbonate systems show astrong response to the pressure increase,depending on their chemical reactivity potential. The
sand systems, on the other hand, show no reaction
to the pressure increase (inert matrix). The
subsequent dynamic processes can causesignificant changes in the characteristics of thecomplex conductivity spectra, where the chemical
reactivity of the individual components plays animportant role.

The results help to understand the physicochemical
understanding and quantification of the reaction
kinetics during long-term surface aging of carbonate
rocks exposed to CO2. A model-based relationshipbetween complex electrical properties and longterm reaction kinetics for named conditions
supports geoelectrical monitoring and enables
prediction of parameter contrasts.

REFERENCES
Journal article
Börner J.H. et. al. (2017) Spectral induced
polarization of the three-phase system CO2 brine - sand under reservoir conditions.
Geophysics 208, Issue 1: p289–p305
Börner J.H. et. al. (2015) The electrical conductivityof CO2-bearing pore waters at elevatedpressure and temperature: a laboratory study
and its implications in CO2 storage monitoring
and leakage detection, Geophysics 203,
Issue 2: p1072–p1084
WaxmanM, Smits L, (1968) Electricalconductivities in oil bearing shaly sands,
Society of Petroleum Engineers Journal, Vol.8

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Imaging and inversion of potential field data, a case study for
exploring Iron-bearing zones in Golgohar, Iran

Behrooz Oskooi1, Pardis Mansourshoar1, Maysam Abedi2
1

Institute of Geophysics, University of Tehran, Iran
2

School of Mining Engineering, College of Engineering, University of Tehran, Iran

Abstract
Imaging and inversion of potential field data give us an estimation of the source property distribution and
it is a powerful tool which yields a fast 3D representation of the source distribution. The execution time ofthese approaches is substantially different. Imaging methods are characterized by fast computation,
simplicity and the results are quantities proportional, but not the real physical properties, on the other hand,
the inverse modeling can always determine the real physical parameters distribution but it iscomputationally expensive.
In this work we want utilize both approaches of the inversion and the Depth from Extreme Points (DEXP)
of magnetic data for the Golgohar Iron mine located in the Sanandaj-Sirjan zone in the province of Kermanof Iran. First both approaches are compared for three synthetic scenarios corrupted by 3% of Gaussian
noise; which are a single source, a single sloping source and a multi source of magnetic anomaly. This isneeded for the investigation on its reliability for the practical application on the real data. Reconstructed
models from synthetic data are in close association with the sought geometry. Ultimately, the approaches
are applied for the interpretation of the real data and the results shows a high correlation between imaging
and inversion in terms of the magnetic anomaly positions, horizontally and vertically.
Keywords: DEXP, Potential field data, Imaging, Inversion, Golgohar Iron mine.

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Imaging the weathering zone in Chile with active Radio-Magnetotellurics
Weckmann, U. 1, Cruces Zabala, J. 1, Patzer, C. 1, 2, Ritter, O. 1, Araya Vargas, J. 3
1

German Research Centre for Geosciences Potsdam - GFZ, Germany (uweck@gfz-potsdam.de)2
now at:Geological Survey of Finland (GTK), Finland;
3
Universidad de Atacama (UdA) Copiapo, Chileffiliation, email contact

SUMMARY
The interdisciplinary DeepEarthShape project focusses on the weathering zone with drillings and a suite ofgeophysical, geochemical and geobiological approaches. The weathering zone is the uppermost part of the
Earth's crust where rocks and soils experience breakdown either mechanically or chemically through theimpact of air/gases, water and/or biological organisms. Weathered bedrock and soil belong to the lifesustaining, layer of the Earth's surface. Its thickness depends on the balance between erosion, removing
weathered material from the surface, and weathering processes that deepen the interface between weathered
and fresh bedrock. Although at shallow depths of 1 – 2 m appreciable amounts of microbial biomass and DNA
counts were observed that might be related to weathering processes, our insight into the entire critical zone
and its processes is still limited. We do not know for instance a) the depth of weathering; b) the processadvancing it; c) whether this advance is driven by water, gases, and/or biological activity and concentrated
along faults. Since some of the properties and characteristics of the weathering zone seem to be linked withclimate, a set of four study sites is studies within the framework of the DFG Special Priority Program 1803
which belong to different climate zones and thus experience different vegetation, precipitation and erosion.
However, the long-stretched coast of Chile represents a prime location to be exposed to these climaticdifferences but allows at the same time to stay in a similar geological/tectonic complex - the Coastal Cordillera.
Therefore, we expect to compare the obtained results from the different study sites and finally test hypotheses
concerning the weathering zone.
We utilised a combined geophysical approach using P- and S-wave seismics as well as Radio-Magnetotelluric(RMT) measurements along approximately 200 – 300 m long profiles at the study sites along the Chilean coast.
Within the framework of the RMT experiment, we used our horizontal magnetic dipole transmitter in
combination with a classical MT station nearby. Here, we will show and discuss 2D and 3D inversion results
together with geophysical logging data from the drill holes and lab measurements of core samples. First results
indicate that precipitation and shallow fluid enhanced zones e.g. in minor faults and folds can be traced andinto the active weathering zone.
Keywords Weathering Zone, Radio-Magnetotellurics, Chile, magnetic dipole transmitter

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Anisotropy estimation using 1D joint inversion of DC resistivity and CSRMT
methods in the granite-gneiss terrains of Eastern Ghats, India
Akarsh Singh 1, Sudha Agrahari 2, Arseny Shlykov 3, Alexander Saraev 4 and Abhishek Yadav 5
1 Indian Institute of Technology, Kharagpur, India, singhakarsh100@gmail.com
2 Indian Institute of Technology, Kharagpur, India, sudha@gg.iitkgp.ac.in
3 St. Petersburg State University, Russia, shlykovarseny@gmail.com
4 St. Petersburg State University, Russia, asaraev51@mail.ru
5 Indian Institute of Technology, Kharagpur, India, abhishek47268@gmail.com

SUMMARY
The joint inversion of a galvanic and an inductive method is a standard procedure for the estimation of resistivity
anisotropy of the subsurface. Other advantages of the joint inversion include the enhancement in the resolution
and the importances of the model parameters obtained after the joint inversion. The joint inversion of the DC
resistivity method and far-field CSRMT method is utilized to evaluate the resistivity anisotropy in the fractured
granite-gneissic terrains of Eastern Ghats, Odisha, India. A table depicting the importances justifies theaccuracy of the joint inversion. The interpreted results validate the inferences made from a borehole lithology
in the area. The anisotropy in the subsurface is found to increase with depth and is more pronounced near thefractured horizons. Also, the magnitude of the anisotropy varies up to 15.
Keywords: Anisotropy, joint-inversion, DC resistivity, CSRMTIntroduction
Geophysical interpretation is always susceptible toambiguity, which can be resolved to a degree bytaking various measures like the inclusion of apriori
information or resorting to a combination of several
geophysical techniques. A good possibility for thejoint usage of more than one technique is byconcatenating the parameter, data, and theparameter sensitivity (Jacobian) matrices to utilize
the advantages of all the individual methods
involved.
In the standard DC resistivity surveys, theindividual consecutive layers are assumed isotropic
and homogeneous. However, in a scenario wheremultiple thin layers coexist inside a thicker stratum,
the assumption of an isotropic layer no longer holds.
The terminology macro- and micro-anisotropy areworth considering at this point. Maillet (1947)
referred to the single electrical equivalent of a set ofthin distinct resistive layers as a macro-anisotropic
layer and termed the phenomenon macroanisotropy. Nevertheless, the individual thin layers
are quite discernible in the well-logging
measurements (Shlykov et al. 2018). The other typeof anisotropy, which originates from the preferential
orientation of mineral grains (crystals) in the layer
matrix, is called micro-anisotropy. This may also be
due to the presence of certain flaky and platy
minerals like biotite and amphiboles respectively.
Jupp and Vozoff (1977) first elucidated anisotropy
estimation using the joint inversion of a resistive andan inductive method. They used a set of four

coupled equations as shown below (Equations 1-4):(1)𝜌 = 𝜌 ∗𝜌(2)ℎ = ℎ ∗ 𝜌 ⁄𝜌
(3)
𝜌 =𝜌
ℎ =ℎ
(4)
where, 𝜌 , 𝜌and ℎ , ℎ
are the resistivity
and thickness detected by the DC and MT methods.
𝜌 and 𝜌 are the actual vertical and horizontal
resistivity while ℎ is the actual thickness.
These equations clearly show that the resistivity
perceived by the DC resistivity method is the
product of the vertical and the horizontal resistivities,
while resistivity measured by the MT method(present case: CSRMT in a far-field zone) is purely
horizontal. Also, the thickness is over-estimated in
the resistivity method by a factor of the anisotropy
coefficient = 𝜌 ⁄𝜌of the layer while thethickness estimated by the CSRMT method
corresponds to the true (geological) thickness.
The accuracy of the Marquardt inverted model
parameters can be evaluated in terms of theirimportances (Jupp and Vozoff, 1975) which ispresented at the end, for all the soundings. Sudha
et al. (2014) defined importances for model
parameter (𝑚 ) as shown in equation (5)(5)
𝐼𝑚𝑝(𝑚 ) = [(𝑽𝑻)(𝑽𝑻) ]where 𝑉 is the parameter eigen vector matrix (or Vmatrix) obtained from the singular value
decomposition of the Jacobian matrix. T is the
diagonal matrix having damping factors as its
elements. Importances are a measure, of how well
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the parameters are resolved. Their high values
imply a major influence on the modeled data.
(Vozoff and Jupp, 1975).

provided good electrode connectivity. CSRMT data
was acquired in the broadside configuration. The
borehole was about 50 m WNW from the start of the
ERT profile.

Geology of the study area
High-grade metamorphosed rocks of the Eastern
Ghats Mobile belt (EGMB) are found in the study
area. The area lies in the Archean-aged
Charnockite-Migmatites-Khondalite province (figure1). Due to the intense shearing and metamorphism,
the rocks were highly fractured. The borehole(figure 2) displays fractured granite gneiss in thedepth interval of 12-13 m. The successive layers
were mostly coarse-grained granite-gneiss with thepresence of feldspar veins for an interval of 1 m at
30.5 m depth and then the same rock without veins.

Frequency in the range of 1-1000 kHz was
propagated from the transmitter. The data wasrecorded in the broadside direction of the HED. The
far-field zone, which is least sensitive to verticalresistivity (Constable et al., 2010), lies in the
broadside direction.

Figure 2. Borehole lithology from the study area.
Results

Figure 1. Study area map depicting the geology ofOdisha (Mahalik, 1998) and the location of the DC
resistivity and CSRMT soundings.
Instruments and Field experimentSingle and joint inversion results of the sounding at
a profile distance of 50 m have been displayed intable 1 and figure 4. The joint inversion results ofseven CSRMT soundings coincident with 1-D DC
resistivity soundings are shown in table 2 and figure
3. For the sounding at 50 m, the single DC resistivity
inversion fitted most well for a three-layer model.
For the DC method mean resistivity and theequivalent thickness are shown in table 1.

The multi-electrode instrument ABEM Terrameter
LS was used for 2D DC resistivity data acquisitionin the Wenner-Schlumberger configuration using a
2x21-electrode protocol and 5 m inter-electrodespacing. The 1D data was extracted from the 2D
acquired data at the profile distances (mid-points)from 40-100 m. Figure 1 depicts the location of the
2D resistivity profile and the CSRMT soundings.

The parameter importances from the DC resistivity
inversion are larger than 0.6. CSRMT method fitted
better for the four-layer model and the horizontal
resistivity and layer thickness parameters are foundto be closer in magnitude to that obtained by thejoint inversion. The importances obtained in the joint
inversion are improved.

SM-25 RMT-F receiver and a portable ACtransmitter GTS-1 were used for CSRMT soundings(Saraev et al, 2017). The source was a groundedhorizontal electrical dipole (HED) with 800 m lengthand a cluster electrode arrangement at each end.
Two electric and three magnetic i.e. five channels
were used in the receiver. A portable Gasoline
generator powered the transmitter. The field
experiment was carried out in the granite-gneissic
terrains of the Eastern Ghats. Shallow clay layers

The variation of vertical and horizontal resistivity
and anisotropy coefficient with depth for all
soundings are shown in figure 3. The last layers inall the soundings are exhibiting a high anisotropy
coefficient with the magnitude varying up to 10.
However, the sounding at a profile distance of 90 mshows an anisotropy coefficient up to ~14 in the last
two layers. The appearance of a high anisotropy
coefficient (>4.0) below 10 m depth (figure 3c)
conforms to the lithology (granite-gneiss) reported
in the borehole.

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Table 1. Inversion results along with theimportances for the sounding at the profile distanceof 50 m

v1
v2
v3
v4
h1
h2
h3
h4
h1
h2
h3
RMS
(%)

Joint
importances

importances(mean, heq.)

CSRMT
alone

0.76

6.8

0.73

10.0

0.63

4.8

0.17

12.0

0.70

293.5

0.87

1.8
11.4
3.1

0.63
0.72

model
parameters

3.5

model
parameters

importances

param

DC
Alone

model
parameters

method

1.4e4

0.11

6.8

1.00

6.8

1.00

1.7

0.89

1.7

0.92

10.1

0.91

9.5

0.95

142.7

0.96

140.2

0.96

1.2
1.3
8.2
2.6 ()4.3 ()

0.97
0.66
0.95

1.2
1.2
7.8

0.98
0.80
0.96

2.5

Figure 3. 1D joint inversion of DC and far-field
CSRMT data: (a) vertical resistivity, (b) horizontal
resistivity, and (c) Anisotropy coefficient.

Conclusions
Joint inversion of DC resistivity and far-field CSRMT
data has successfully resolved the anisotropy
present in the area. High anisotropy values (up to15) have been observed in the deeper layers (>10m). This might be due to the presence of fractured
and foliated granite-gneiss rocks.
Acknowledgments
This work is done under the joint “DST-RFBR”
project scheme with financial assistance from the“Department of Science and Technology, India,
Project No: INT/RUS/RFBR/P-277” and the“Russian Science Foundation, project No 21-4704401”.
REFERENCES
Constable S (2010) Ten years of marine CSEM forhydrocarbon exploration. Geophysics, 75(5):75A67-75A81.
Jupp DLB, Vozoff K (1975) Stable iterative methods
for the inversion of geophysical data.
Geophysical Journal International 42(3):957976.
Jupp DLB, Vozoff, K (1977) Resolving anisotropy inlayered media by joint inversion. Geophysical
Prospecting 25(3):460-470.
Mahalik NK (1998) Precambrians. Geology andmineral resources of Orissa:43–81. SGAT Publ.
Raiche AP, Jupp DLB, Rutter H, Vozoff K (1985) The
joint use of coincident loop transient
electromagnetic and Schlumberger sounding
to resolve layered structures. Geophysics
50(10):1618-1627.
Saraev A, Simakov A, Shlykov A, Tezkan B (2017)
Controlled source radiomagnetotellurics: A toolfor near surface investigations in remoteregions. Journal of Applied Geophysics 146:
228-237.
Shlykov A, Agrahari S, Antaschuk K, Saraev A,
Simakov, A (2018) Estimation of the anisotropy
using CSRMT data in the transition zone ofelectric dipole. In 24th EM Induction Workshop,
Helsingør, Denmark, August 12-19, 2018, At
Helsingør, Denmark.
Sudha, Tezkan B, Siemon B (2014) Appraisal of a
new 1D weighted joint inversion of groundbased and helicopter-borne electromagnetic
data. Journal of Applied Geophysics 62(3):597614.
Vozoff K, Jupp DLB (1975) Joint inversion ofgeophysicaldata. GeophysicalJournalInternational 42(3):977-991.

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Table 2. Summary of the joint Inversion results along with the importances of all the soundings

importancesmodelparameters

importancesmodelparameters

importancesmodelparameters

importances

100m

model
parameters

90m

importances

80m

model
parameters

70m

importances

60m

model
parameters

50m

importances

40m

model
parameterssounding
param

v1

3.9

0.37

6.8

0.73

8.2

0.81

19.0

0.71

8.5

0.93

26.3

0.87

17.0

0.41

v2

50.

0.14

4.8

0.17

3.4

0.09

3.1

0.24

6.3

0.13

3.6

0.43

3.9

0.25

v3

63.9

0.41

293.5

0.87

57.3

0.94

42.1

0.81

45.1

0.91

4356.9

0.36

786.7

0.83

v4

7326

0.13

14448

0.11

25147

0.09

3652

0.45

7310

0.27

37427

0.03

8771

0.11

h1

3.9

1.00

6.8

1.00

8.2

1.00

19.0

0.75

8.5

1.00

26.3

0.48

17.0

0.93

h2

5.0

0.75

1.7

0.92

1.8

0.83

3.0

0.98

1.6

0.80

3.6

1.00

3.9

1.00

h3

2.4

0.77

9.5

0.95

11.2

0.99

10.2

0.96

11.7

0.98

23.8

0.95

19.1

0.95

h4

106.3

0.99

140.2

0.96

241.6

0.96

137.3

0.94

149.4

0.97

173.8

0.71

212.4

0.71

h1

1.9

0.76

1.2

0.98

1.4

0.98

0.6

0.93

1.8

0.99

0.5

0.97

0.7

0.98

h2

1.4

0.60

1.2

0.80

0.8

0.65

2.1

0.91

0.8

0.64

4.9

1.00

4.8

0.99

h3

2.0

0.78

7.8

0.96

10.2

0.99

8.7

0.97

10.7

0.98

18.7

0.90

17.6

0.94

RMS (%)

2.2

2.5

2.8

3.0

3.3

4.2

5.4

Figure 4. Single Isotropic inversion of (a) DC, (b) CSRMT. (c) Joint Anisotropic Inversion for sounding at 50 m.
Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Global induction response to 11 year period and the conductivity of the lower mantle

1

S. Constable1 , C. Constable1 , M. Korte2 and M. Morzfeld1
Scripps Institution of Oceanography, USA, sconstable@ucsd.edu, cconstable@ucsd.edu, matti@ucsd.edu
2
Deutsches GeoForschungsZentrum, Potsdam, Germany, monika@gfz-potsdam.de

S UMMARY
To date the electromagnetic response of Earth has been restricted to periods shorter than one year (with the
exception of an estimate for the 11-year sunspot cycle by Harwood and Malin (1977)) because at longer periods
the separation of the internal and external fields remains challenging. We adopt a simple approach to extracting
the external P10 variations and their inductive response from 117 years of hourly geomagnetic data from 192
observatories drawn from a recent compilation distributed by the British Geological Survey. Observatories
were selected to be within 65◦ of the geomagnetic equator and data gaps of up to 48 hours were repaired
using linear interpolation. Baseline corrections were made and the time varying International Geomagnetic
Reference Field (IGRF) was removed from the data, which were rotated into geomagnetic coordinates year
by year to allow for migration of the geomagnetic pole. Data were processed year by year by fitting hourly
estimates of the internal and external coefficients of a P10 field to the set of observatories that had no missing
data for that year (a number that varies between 3 and 93), using one month overlaps between years to remove
the small bias from year to year fits. Observatory years with excessive misfit were iteratively removed from theprocessing. Multi-taper spectral analysis and band averaging was used to estimate the power spectra andtransfer functions between the two time series. A clear peak at the 11-year solar cycle appears in the external
field spectrum, with a smaller but discernible peak at the first harmonic of 5.5 years, with associated peaks inthe coherency spectrum. Error bars on complex admittance were estimated from the statistics of the crossspectrum and a parametric bootstrap. The data can be fit to RMS 1.0 with a smooth Occam inversion model.
A Bayesian inversion approach was also used, generating 10,000 models that fit acceptably well. A conductive
core is seen in the models and is required by the data. The well-documented jump in conductivity between theupper and lower mantle is clearly seen, and a second jump in conductivity occurs at depth of 1,800 km.
Keywords: global induction, mantle conductivity, sunspot cycle
uppermantlelower mantle

core

Figure 1: Inverted models, complex impedance data, and model responses. Orange lines are 10,000 4-layer
models fitting between RMS 0.92 and 1.3. Black lines are Occam models and responses fitting to RMS
1.0 and 1.1. Red contours are the 95% bounds on acceptable model space.
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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Signatures of the global ocean circulation in geomagnetic secular variation andacceleration
C.C. Finlay1 , J. Velímský2 and C. Kloss1
DTU Space, National Space Institute, Copenhagen, Denmark, cfinlay@space.dtu.dk
2
Dept. of Geophysics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic,
jakub.velimsky@mff.cuni.cz
1

S UMMARY
Changes in the global ocean circulation driven by winds and density gradients produce, via motional induction,
time-varying geomagnetic signals. On long length and time scales these signals are hidden beneath larger
core-generated signals but, due to their location at Earth’s surface, on sufficiently short length scales andconsidering month to interannual timescales they could be detectable. Such signals would provide valuable
information related to ocean circulation and conductivity variations. Here, we investigate this question using
advanced forward simulations of the magnetic signals generated by (i) the oceans, using the ECCO v4r4
ocean circulation model and a realistic electrical conductivity model for the Earth’s oceans, sediments, andupper mantle, (ii) the core dynamo, using an advanced numerical model that correctly simulates the relevanttimescales of core processes (waves, advection etc.), and (iii) the ionospheric field, based on an empiricalmodel derived from satellite measurements and scaled by the solar radio flux index. We use these to generate
synthetic satellite observations on real Swarm orbits, and for simulated orbits of the proposed NanoMagSat
mission. We find it is possible to recover ocean-generated secular variation (SV) and secular acceleration (SA)signals from 6 months of low-earth-orbit satellite data. It is expected that there will be significant benefits fromusing NanoMagSat data when considering shorter periods; further work is needed in this direction.
Keywords: global ocean circulation, motional induction, secular variations and acceleration

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Limits of a-posteriori interpretation of electrical conductivity in terms of watercontent

1O. Knopp1
Dept. of Geophysics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic,
knopp@karel.troja.mff.cuni.cz

S UMMARYElectrical conductivity profile of the mantle, derived from EM inversion, can be itself viewed as data for further
interpretation. This interpretation in terms of the thermochemical state of the mantle rock can be obtainedusing Gibbs energy minimalization and experimental mantle rock electrical conductivity data. If we constrain
the temperature and some dimensions of the chemical composition it is possible to obtain the water content
from electrical conductivity. This inversion can have significant a-posteriori error margins. This is not only due tothe a-priori error estimate of the electrical conductivity and the thermochemical data or the error of the forward
problem derived from Hashin-Shtrikman bounds, but also due to the significant error of the experimentallyderived mantle rock conductivity data. I would like to present a few examples of this local inversion and show
how different errors are projected into the final a-posteriori distribution of the local water content.
Keywords: electrical conductivity, Gibbs energy minimization, mantle composition

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Regionality of mantle conductivity inferred from geomagnetic daily variation
analysis
Takao Koyama1, Shigeru Fujita2, Ikuko Fujii3, Kiyoshi Baba4 and Hisayoshi Shimizu5
1Earthquake Research Institute, The University of Tokyo, tkoyama@eri.u-tokyo.ac.jp
2The Institute of Statistical Mathematics, sfujita@ism.ac.jp
3Meteorological College, Japan Meteorological Agency, ifujii@mc-jma.go.jp
4Earthquake Research Institute, The University of Tokyo, kbaba@eri.u-tokyo.ac.jp
5Earthquake Research Institute, The University of Tokyo, shimizu@eri.u-tokyo.ac.jp

SUMMARY
This paper report on regionality of the electrical conductivity of the global mantle, inferred from geomagnetic
data analysis of daily variation. The mantle has a potential to reserve huge amount of water, and the electricalconductivity distribution can reveal water contents in the mantle. In this study, geomagnetic data of daily
variation at 71 stations were analyzed, and a global distribution of the electrical conductivity in the mantle was
detected. It is necessary to take into account the Sq field distribution, because the geomagnetic distribution ofdaily variation is complex, and it prevents a plane wave assumption. 71 stations, however, are still not enoughto draw a Sq field with high resolution. Instead of it, the GAIA (Ground-to-topside model of Atmosphere andIonosphere for Aeronomy) is used as the inducing field. The GAIA assimilates the meteorological reanalysis
data (JRA-55) to the whole atmosphere-ionosphere coupled model and thus it is the well-modelled Sq field.
First, we executed spherical harmonic expansion of the magnetic field of the GAIA for three-days time series,
which correspond to sequent solar quiet days. Next, three-dimensional forward modeling in the spherical Earthwas performed to compare with the observed geomagnetic data. Now, we suppose the one-dimensional
structure in the Earth under the ocean-land lateral contrast. We tried several one-dimensional models andsearched for the best-fitted model for each station. As the results, beneath Europe, the conductive model, in
which the electrical conductivity exceeds 0.1 S/m in the upper mantle, is preferred. Conversely, the resistivemodel, in which the electrical conductivity is 0.01 S/m or less in the upper mantle, is likely beneath thenorthwestern Pacific. This difference may be derived from the water contents, which were carried by a
subducting slab, because a maximum amount of water depends on the temperature of the slab.
Keywords: mantle conductivity, geomagnetic daily variation, Sq field, GAIA

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Hunga-Tonga Hunga-Ha'apai Eruption lightning as seen by remote MT
measurements in New Zealand and Japan
T. G. Caldwell1, P. A. Jarvis1, C. Noble2, Y. Ogawa3
1 GNS Science, New Zealand

2 NZ Metrological Service, New Zealand

3 Tokyo Institute of Technology, JapanOne of the astonishing aspects of the Jan-15 2022 Hunga-Tonga Hunga-Ha'apai (HTHH) eruption was theintensity of the volcanic lightning produced. Worldwide lightning detection networks recorded approximately
4x105 lightning strokes centred on HHTH in 2 hours starting around 04:10 Jan-15th UTC. During the eruption,
two continuously recording MT stations were operating on the east coast of New Zealand's North Island,
approximately 2000 km from HTHH. These stations continuously record the magnetic field variations at asample rate of 150 Hz using induction coils manufactured by Phoenix Geophysics. A similar recording site inJapan, about 8000 km from HHTH, was also operating during the eruption. The magnetic field signals in the
10-2 s to 1 s period range in both New Zealand (NZ) and Japan are tangentially polarized with respect to thedirection of propagation from HHTH; consistent with the expected surface-guided electromagnetic-wave
propagation in the resonant cavity formed by the earth and the ionosphere. The agreement between
spectral-time plots of the MT data and lightning count-rate data recorded by the Vaisala Global Lightning
Detection Network (GLD360) at HTHH is remarkably good.

The NZ and Japanese MT data indicate volcanic lightning associated with the main eruption commenced
between 04:08 and 04:14 Jan 15th (UTC). This agrees with time estimates of the eruption initiation fromvisible satellite imagery and global seismic datasets. The intense electrical activity associated with theeruption lasted 2 hours, peaking about 50 minutes after onset. The NZ MT data also show that a less-intenseburst of electrical activity first developed the day before the main eruption (Jan 13th), at about 17:00 UTC
lasting about 0.5 hours. Later that morning, the Tongan Geological Service observed a towering eruption
cloud with volcanic lightning at its margins. Additionally, a 0.7-hour-long episode of intense lightning occurred
about 4.5 hours after the onset of the main eruption, indicative of a second significant eruption, also seen insatellite imagery and seismic data.

A remarkable feature of the eruption was the atmospheric pressure wave that propagated worldwide.
Barometric pressure data recorded throughout NZ and on islands in the Pacific to the north and south of
New Zealand shows that the origin time for the ‘detonation’ event that caused the pressure pulse occurred
about the same time as the onset of lightning seen in the MT data. Such an energetic explosion suggeststhat large quantities of magma and seawater came into contact producing an eruption column that was waterrich; conditions needed for ice formation and thus lightning production in the eruption column and in the highaltitude umbrella-cloud that spread outwards from the eruption centre.

Keywords: Hunga-Tonga Hunga-Ha'apai (HTHH), eruption, lightning, magnetotellurics

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

MagVector/MFX-2 – a Planetary Laboratory on the International Space Station (ISS):
Electromagnetic Simulation and Inversion of Magnetic Field Data from Planetary andAsteroid Analogs
Jana Börner1 , Stefanie Garbade2 , Sarah Sophia Keßler1 , Detlef Konigorski2 , Volker Schmid3 , Lorenzo
Schmitt1 , Carolin Schneider1 , Frank Sohl4 and Klaus Spitzer1
1
Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Germany,jana.boerner@geophysik.tu-freiberg.de, carolin.schneider@geophysik.tu-freiberg.de,sarah-sophia.kessler@student.tu-freiberg.de, lorenzo.schmitt@student.tu-freiberg.de,klaus.spitzer@geophysik.tu-freiberg.de
2
Airbus Defense & Space GmbH, Bremen, Germany, stefanie.garbade@airbus.com,
detlev.konigorski@airbus.com
3
German Space Agency at DLR AR-AF BO - Forschung und Exploration, Bonn-Oberkassel, Germany,
Volker.Schmid@dlr.de4
German Aerospace Centre (DLR), Institute of Planetary Research, Rutherfordstraße 2, 12489 Berlin,
Germany, Frank.Sohl@dlr.de

S UMMARYThe MagVector/MFX-2 experiment was conducted in 2018 as part of Alexander Gerst’s horizons mission on
the International Space Station (ISS). It was planned and led by the German Space Agency at DLR and developed and built by Airbus Defense and Space GmbH with funds from the Federal Ministry for Economic Affairs
and Energy. Its main objective was to enlarge the number of sensors for measuring effects of the Magvector/MFX core. Furthermore, the new sensor array gave an excellent opportunity to host 13 different material
and planetary rock samples, for the first time measuring possible interactions with magnetic fields when travelling with orbital velocity in a laboratory experimental setup onboard the ISS. The magnetic environment aroundthe samples was continuously monitored by 32 sensors measuring the magnetic flux density in three components. The meteorite samples and sample preparation were provided by the Museum für Naturkunde Berlin.
The collected data material has been investigated using simulation and inversion software developed at the TU
Freiberg. Here, we are going to report about the results of these virtual experiments that aim at reconstructing and understanding the observed magnetostatic and inductive responses generated by the samples. The
magnetic susceptibility of the samples was successfully recovered by a newly developed magnetostatic inversion algorithm after preprocessing the data to remove unphysical signal components. Furthermore, we havecarried out a series of induction experiments using our Nédélec finite element unstructured tetrahedral meshtime-domain forward modeling code. Both codes allow for a highly accurate incorporation of the experimental
geometry and the shape of the samples. The induction experiments showed that a significant inductive effectof the samples at the nearest sensor location cannot be expected below 1 to 10 kHz. Based on these findings,
the method can be scaled up for the investigation of larger objects.
Keywords: International Space Station (ISS), horizons mission, MFX-2 experiment, terrestrial planets, asteroids, magnetostatics, electromagnetic induction, numerical simulation

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Deep geomagnetic sounding by Sq variations in Europe:
A 3-D inversion based on the regional-to-local transfer functions

1

J. Velímský1 , L. Šachl1 and O. Knopp1
Dept. of Geophysics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic,
jakub.velimsky@mff.cuni.cz

S UMMARY
The spatial configuration of the ionospheric currents precludes the use of simple local transfer functions ingeomagnetic deep sounding by the solar quiet (Sq) variations. The global-to-local transfer functions relate
the local vertical magnetic field at a particular observatory to the spherical harmonic coefficients describing
the ionospheric source. The latter must be obtained by initial processing of the horizontal magnetic fieldmeasurements, and using an a-priori conductivity model.
The approach presented here introduces two novel points in this concept. First, the global spherical harmonicbase is replaced on the regional scale with vector Slepian functions, concentrating the EM fields in the areaof interest while still allowing to use the well developed spherical-harmonic formalism. Second, the Dirichlet boundary condition is applied in the forward problem, prescribing the total horizontal magnetic field in the
Slepian base, and thus avoiding the use of an a-priori conductivity model. Such formulation allows a straightforward prediction of the regional-to-local transfer functions, which relate the vertical magnetic field at a particularpoint with the coefficients of the Slepian expansion of the horizontal component.
The methodology is tested on a synthetic quasi-realistic scenario for European observatories. A 3-D conductivity model WINTERC-e Wd-emax, based on a thermochemical state of the mantle and laboratory conductivity
measurements, is used to generate the responses, and subsequently is successfully recovered from them by
3-D inversion.
In the next step, we obtain robust estimates of the regional-to-local transfer functions for the daily time harmonics from geomagnetic field measurements at European observatories, and invert them in terms of 3-D
electrical conductivity distribution in the upper mantle below Europe. Finally, we look at the interpretation of
the conductivity model in terms of the mantle thermochemical structure.
Keywords: Sq variations, geomagnetic deep sounding, transfer functions, upper mantle conductivity below
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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

3-D inversion of tippers estimated at a continental grid of Chinese geomagnetic
observatories: Preliminary results
Shan Xu1,2 , Alexey Kuvshinov2 , Chaojian Chen2 , Mikhail Kruglyakov3 , Rafael Rigaud2 , Zhengyong Ren4 andXiangyun Hu1
1
China University of Geosciences (Wuhan), Wuhan, China, shanxu0919@gmail.com
2
ETH Zürich, Zürich, Switzerland, kuvshinov@erdw.ethz.ch
3
University of Otago, Dunedin, New Zealand, m.kruglyakov@gmail.com
4
Central South University, Changsha, China, renzhengyong@csu.edu.cn

S UMMARY
Deep 3-D electrical conductivity structures on a continental scale can be retrieved by the joint inversion ofmulti-source magnetic transfer functions (TFs) - tippers, and longer period global-to-local (G2L) TFs originatedfrom the signals due to ionospheric and magnetospheric sources - which in their turn can be estimated froma regional network of continuous measurements of the geomagnetic field. In this study, we discuss a data set
comprising measurements from a grid of continental Chinese geomagnetic observatories, present results of
estimating tippers from these data and perform their forward and inverse modelling. Specifically, we sortedout and carefully calibrated twelve years (2008-2019) of data from 67 Chinese observatories. We estimatedtippers at these observatories in a period range between 5 minutes and 3 hours; notably, tippers appeared to be
extremely large at four stations in southwest China. We also present the first results of inverting tippers in termsof 3-D conductivity distribution in the crust and part of the upper mantle beneath China. Inversion is performed
using a novel, accurate, and computationally efficient 3-D forward and inverse solver, GEMMIE, based on the
integral equation approach. This work is considered as the first step toward joint inversion of tippers and longer
period G2L TFs to obtain a 3-D conductivity model beneath China down to the lower mantle.
Keywords: Geomagnetic induction; Time-series analysis; Tippers; Three-dimensional conductivity structure.

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Investigation of the Impact of convectively coupled equatorial waves (CCEW) andtotal electron content (TEC) on the diurnal cycle in Indonesia as early warning
system of equatorial climate change
M. K. Rifai, C. A. Hapsoro and E. Latifah
Physics Department, Univesitas Negeri Malang, Indonesia, mochamad.khoirul.2003228@students.um.ac.id
Physics Department, Univesitas Negeri Malang, Indonesia, cahyo.ajihapsoro.fmipa@um.ac.id
Physics Department, Univesitas Negeri Malang, Indonesia, eny.latifah.fmipa@um.ac.id

SUMMARY
Climate change has become a global issue that impacts the world community's perspective on preventing
potential natural disasters in the future. One of the essential factors in the investigation of climate change
dynamics is the Convectively Coupled Equatorial Wave (CCEW). Recent studies on the CCEW found very
active climate dynamics in the Indo-Pacific warm sea region. This condition causes the Indo-Pacific region tohave the potential for extreme conditions in the future. On the other hand, several previous studies have shown
an unusual correlation between TEC (Total Electron Content) activity in the Ionosphere layer to climate
dynamics in the lower atmosphere. TEC induces the propagation of gravity and Kelvin waves, which affects
the formation speed of the weather phases. Although recent research has revealed a correlation between thetwo components with such a wide area coverage, the impact on Indonesia's territory is still not fully understood.
This is important considering that the Indonesian archipelago has various large islands with different
meteorological conditions. So, a more specific review regarding the impact of CCEW and TEC in theIndonesian archipelago needs to be carried out. The impact of these two components is focused on the diurnal
cycle, considering that the Indonesian archipelago has high precipitation levels. CCEW data processing uses
TMPA (TRMM Multisatellite Precipitation Analyses) and IR-WS (Infrared Weather State). Furthermore, the two
types of data models are processed to identify the CCEW phase and finally perform a statistical analysis of
the resulting diurnal cycle. TEC's behaviour was observed using Geostationary Satellite Radio Beacon
Techniques for further statistical analysis of the intensity of its influence on the diurnal cycle. This research isintended as an early mitigation effort to the changing conditions of the equatorial tropical climate in Indonesia,
which is expected to continue to increase in the next few decades.
Keywords: Climate change, Convectively Coupled Equatorial Waves (CCEW), Total Electron Content (TEC),
diurnal cycle, Indonesia.

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Abstract, 25 EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Variations of the induction vector, worldwide study
I.I.Rokityansky
Subbotin Institute of Geophysics of the National Academy of Sciences of Ukraine, Kiev, Ukraine
E-mail: rokityansky@gmail.com
SUMMARY
Real (in phase) and imaginary (out of phase) induction vectors (vertical response functions) were obtained forevery day in the time interval from 1991 to 2014 years on 137 observatories of the global network«Intermagnet» for 5 intervals of periods: 150-300 c, 300-600 c, 600-1200 c, 1200-2400 c, 2400-3600 c. To
reduce scatter and make it easier to work with great amount of data, the daily values were averaged to00monthly mean values. Such global material from +87 to -88 geomagnetic latitudes was obtained for the firsttime and its analysis yields new scientific results. The annual variation with a period 1 year is visible at about
2/3 of the observatories (At the rest observatories it is below background of shorter period variations and/or
noise). Its amplitude depends of geomagnetic latitude and sometimes attains such a high value as 0.4-0.5
0(peak-to-peak) in high (>65 ) latitudes and varies within 0.01-0.15 in middle and low latitudes. Previous
studies in middle latitudes led to conclusion that induction vector’s northern component Au is everywhere
positive (maximum in June, minimum in December) and proposed a global source model for its description.
This study states that in high latitudes Au is negative. 11-years variation found in ≈30% of observatories
distributed in all latitudes but more frequently in aurora zones. Non-periodic transient variations of theinduction vector also occur, in particular before strong earthquakes. In common, the behavior of the induction
vector variations is complex with many local exceptions and irregularities that leads to supposition that they
are not a simple «source effect», but contain information from the Earth interior and its environment includinginteractions in the system Earth-Sun-Moon-planets.
Keywords: geomagnetic field, geomagnetic variations, annual variation, magnetic variation profiling.

Introduction
The Induction vectors were introduced byParkinson (1959), Wiese (1965) and Schmucker(1970) for the study of strong conductivity contrasts
like sea-land and looking for electrical conductivity
anomalies in the Earth crust. To obtain an induction
vector, three components of natural geomagnetic
field B(t) are necessary.
MethodIn the geoelectromagnetic studies of the Earth’s
interior natural source field B(t)=Bxex+Byey+Bzez(where ex, ey, ez are unit vectors directed to North,
East and downward) is used. B(t) contains richinformation both from external source inionosphere and magnetosphere and from the Earthinterior. To clean out external influence and focus
on the Earth interior study special processing
transforms B(t) time series into Response
Functions (RF) time series. Induction vector C is
one of RF (response of the Earth to appliedexternal field):
C=Aex + Bey(1)

where A and B determined from the linear equation
Bz = ABx + BBy(2)

The quantities A and B are complex numbers, anda pair of induction vectors is determined: real Cu
and imaginary Cv (Rokityansky 1982).
Real induction vector Cu = Auex + Buey possess animportant property: in the Wiese notation used
here, it is directed away from a good conductor.
Processing technique
The processing of the data recorded in the timeinterval Δ₮=t2-t1 (usually 1 day in this work) is atransformation from 3 synchronous time series Вх,
By, Bz with a discreteness Δt (1 min – data
Intermagnet) into the time series of the induction
vector components for the set of obtained periods
Tn (n=1-5) with discreteness Δ₮. Obviously,
Δt«Tn«Δ₮.
Processing was carried out by programs of
Varentsov (2007) and Klimkovich (2009) based on
FFT, calculation of cross- and auto-correlationenergy spectra between magnetic components,
partial estimator procedure in a set of overlapping
windows, and a multi-stage selection of acceptableestimators according to coherence criteria.
Data and results
DigitalIntermagnet
worldwidedata
B(t)(http://www.intermagnet.org/) with the discretenessof 1 min have been processed since 1991 to 2014

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year and for every day the components Au, Bu, Av,
and Bv of real and imaginary induction vectors wereobtained for the intervals of periods 2,5-5 min, 5-10
min, 10-20 min, 20-40 min and 40-60 min. The daily
components of the induction vector arecharacterized by a considerable scatter. For theirsmoothing and compact presentation of long-term
data, the monthly mean values were calculated andused. This limited the resolution of the obtained
time series to 2-3 months.
Observatories with induction vectors at period 1800s are presented in Figure 1, the time series for 25
observatories at the same period are given in
Figure 2, the time series at 4 periods are publishedin (Babak et al 2017) for 8 observatories. Whenreading the following text, it is recommended tolook at the mentioned figures.
The annual variation (AV) with a period 1 year isvisible at about 2/3 observatories of world (at therest observatories it is below background of shorter
period variations and/or noise). Its amplitudedepends of geomagnetic latitude and sometimes
attains such a high value as 0.4-0.5 (peak-to-peak)0
in high (>65 ) latitudes and varies within 0.01-0.15
in middle and low latitudes. AV amplitude as a ruleincreases with the period.
Previous works (Araya and Ritter 2016 and others
referred in it) studied AV only in middle latitudes
and found that northern component Au is always
positive, i.e. Au has the maximum in June. Studies
in high latitudes yield: polarity of Au is usuallynegative (maximum in December) with few
exceptions. All this clearly seen in Figure 2 at
northern observatories THL and RES and in thesouthern one DRV, but in the most southern SBA
Au is positive (an «exception»). It means thatannual variations are not only global source field
effect but also effect of local factors.
Consider the results of 3 observatories:O
THL (geomagnetic latitude 87.31 , northwest ofGreenland). AV is most intense at the longest
period of 3000 s, sometimes reaching an amplitudeof 0.5 on Au and Bv components. With the period
decrease, AV amplitude decreases to values of theorder 0.2-0.05. AV is unstable, its amplitude atsome components/periods changes by 1.5-2 times
during 23 years. Also, there is an «overflow» of AV
from one component to another. So, at periods
5-10 min at Av in THL and at Bv in NAQ annual
variation in 1991-2004 was rather intensive, then it
was halved, and at Au and Bu of both observatories
AV amplitude was doubled. The same AV
enhancement from 2005-2007 to 2011-2014 wasobserved in HLP (Poland), KOU (Guyana) … andJapan where it was supposed to be middle-term
precursor of the Tohoku earthquake (Rokityansky
et al. 2019).
RES (north of Canada), the induction vector is on
average 0.2 directed to the north. The AV is slightlythsmaller in amplitude than in THL, but with a similar
distribution over the components. Intense
variations with a period little less than half of year
are imposed on the AV. They deviate the shape of
AV from a sinusoid and make the upper extremumof AV blunt and the lower extremum pointed (andsimilar form is observed also at observatories THL,
DRV, SBA on Au and Bv components); in othercases, vice versa, the upper one is pointed, thelower one is blunt (THL, SBA - on the Bu
component).
0
NAQ (geom. latitude 69.46 , south of Greenland).
Marine currents of deep ocean create a significant
real induction vector Cu≈0.9 at periods 20-60 min.
The imaginary vector Cv at periods 450, 900, 1800
and 3000 s is equal to +0.3, +0.15, 0 and -0.15
respectively which is in full agreement with thetheory and empirical formula (7.1) for the coasteffect developed in [Rokityansky, 1982, p. 313]. In
NAQ large annual (0.1-0.3) and 11-year variations(0.1-0.2 in all periods). And quite unusual thing - alarge trend ≈0.2 over 23 years. Glaciers are melting
in Greenland. May be the anomalous behavior ofvectors in NAQ related with global warming?
11-years variation found in ≈30% of observatories
distributed in all latitudes, more often in aurora
zones.
REFERENCES
Araya JV, Ritter O (2016) Source effects in
mid-latitude geomagnetic transfer functions.
GeophysJInt
204,
606-630.
doi:
10.1093/gji/ggv474.
Babak VI, Rokityansky II, Sokolova EYu, Tereshin
AV (2017) Annual, 11-year and aperiodic variationsof the induction vector at 8 observatories of theIntermagnet network. Geofiz j, 39, n. 1, 97-110, doi:org/10.24028/gzh.0203-3100.v39i1.2017.94013
Rokityansky
II(1982)
Geoelectromagnetic
investigation of the Earth's crust and mantle.
Berlin-Heidelberg-New York: Springer Verlag 381p.
Rokityansky II, Babak VI, Tereshyn AV,
Hayakawa M (2019) Variations of Geomagnetic
Response Functions before the 2011 Tohoku
Earthquake. Open Journal of Earthquake
Research,
8,70-84.
https://doi.org/10.4236/ojer.2019.82005
ACKNOWLEDGEMENTS
The author is grateful to I.M. Varentsov and T.A.
Klimkovich for the processing programs, V.I. Babak
and A.V. Tereshyn for processing a significant
amount of data, and to the staff of geomagnetic
observatories for the high quality records.

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Figure 1. Map of 137 Intermagnet + 14 Japanese observatories with real Cu and imaginary Cv induction
vectors for the period 1800 s (20-40 min), averaged for the entire observation period. Geomagnetic poles forepoch 2010 are marked by 5-ray stars, geomagnetic equator is also drawn.
Figure 2. Monthly average components Au and Av (upper graph), Bu and Bv (lower graph) in 1991-2014 for aperiod 1800 s (20-40 min) at 25 observatories selected with clearly visible annual variations. The
geomagnetic latitudes of each observatory are inscribed on the right. The scale for all observatories is thesame, the level is not saved. The color of the curves depends on the location: black - North America, red Europe, faded red - Asia, light green - Japan, green - South America, green-blue - Australia, blue - South
Africa, dark blue - Indian Ocean, orange - Antarctica.
CONCLUSIONSReal and imaginary induction vectors were calculated for every day and month in the time interval from 1991
to 2014 on 137 observatories of the global network «Intermagnet» for 5 intervals of periods: 150-300 c,
300-600 c, 600-1200 c, 1200-2400 c, 2400-3600 c. Such global material was obtained for the first time and its
analysis yields new results so far using only monthly mean values. The annual variation is visible at about 2/3
0of the observatories. In high (>65 ) latitudes its amplitude attains such a high value as 0.4-0.5, in middle andlow latitudes it varies within 0.01-0.15. Northern component of annual variations in low and middle latitudes ispositive (maximum in June, minimum in December), in high latitudes - negative. 11-years variation found in 30%
of observatories distributed in all latitudes but more frequently in aurora zones. Non-periodic transientvariations of the induction vector also occur, in particular before strong earthquakes. Dependence of theinduction vector variations on the magnitude and direction of the vector itself is not found in this study.
Copyright Statement: The author's copyright for this abstract is transferred to institution.
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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Constraining the crustal and mantle conductivity structures beneath islands by ajoint inversion of multi-source magnetic transfer functions
C. Chen1, A. Kuvshinov1, M. Kruglyakov2, F. Munch3 and R. Rigaud1
1Institute of Geophysics, ETH Zurich, Switzerland, chaojian.chen@erdw.ethz.ch
2Department of Physics, University of Otago, New Zealand
3Berkeley Seismological Laboratory, University of California, Berkeley, United States

SUMMARY
In this study, we present a tool to simultaneously invert multi-source magnetic transfer functions (TFs),
including tippers, solar global-to-local transfer functions (TFs) originating from the signals due to ionosphericsource, and global Q-responses originating from the signals due to magnetospheric source. We jointly invert
the aforementioned TFs to constrain the local conductivity structures beneath three islands in the Atlantic(Tristan da Cunha), Indian (Cocos), and Pacific (Oahu) Oceans. The recovered conductivity profiles appeared
to be consistent with the presence of upper mantle plumes beneath the Tristan da Cunha and Oahu Islands.
Our results indicate resistive lithosphere of different thicknesses beneath considered three islands. Besides,
new conductivity profiles suggest warmer-than-average mantle temperatures and the presence of a small
fraction of melt beneath Tristan da Cunha Island. At the same time, the conductivities beneath Cocos Island
are in good agreement with estimates expected for ambient mantle conditions.
Keywords: Joint inversion; electromagnetic; transfer functions; mantle conductivity

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Abstract, 25th EM Induction Workshop,Çeşmeı, Turkey, September 11-17, 2022

Making geo-electromagnetic (magnetotelluric) data accessible via EPOS portal
M.Yu. Smirnov1 , J. Hübert2 , O. Ritter3 , A. Neska 4 , T.M. Rasmussen1 , P. Hejda 5 , S. Flower 2 ,A. Chambodut 6 , J.J. Curto 7 , J. Matzka 3 , A. Thomson 2 , A. Viljanen 81Luleå University of Technology, Sweden, maxim.smirnov@ltu.se
2
British Geological Survey, Edinburgh, UK, juliane.huebert@bgs.ac.uk
3
GFZ, Potsdam, Germany, Oliver.Ritter@gfz-potsdam.de4
Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland, anne@igf.edu.pl
5
Institute of Geophysics, Prague, Czech Republic, ph@ig.cas.cz
6
Ecole et Observatoire des Sciences de la Terre, Université de Strasbourg, France,
aude.chambodut@unistra.fr
7
Observatori de l’Ebre, Roquetes, Spain, jjcurto@obsebre.es8
Finnish Meteorological Institute, Helsinki, Finland, ari.viljanen@fmi.fi

Over the past decades a great wealth of magnetotelluric (MT) data have been collected by many researchers
across the globe. Making the data accessible and available to other scientists within the same discipline andbeyond poses a big challenge but also offers great opportunities for new and cross-disciplinary science. Since
the last EM Induction workshop in Helsingor in 2018 we have continued to further develop the archiving of andaccess to European MT data within the European Plate Observing System (EPOS). EPOS is a multidisciplinary,
distributed research infrastructure that facilitates the integrated use of data, data products, and facilities fromthe solid Earth science community in Europe and is funded by the European union. MT data is integrated in
the Geo-Electromagnetic Thematic Core Service (TCS) in close collaboration with geomagnetic observations
and data services. EPOS’ main achievement is an openly accessible data portal that contains the metadataand download links to all data available through this service, thus making MT data searchable and accessible
to a wide range of science, policy makers and other users.
Geo-electromagnetic data are currently held in a variety of formats, software and locations, which createsa barrier to access. Data provided through EPOS will have to conform to common standards. We havedeveloped and implemented today’s standard-de-facto for web metadata - the JSON container, a standard
text-based format for representing structured data - as the data storage format for MT data and models. Thisdata format is intentionally implemented to contain all the necessary information to fully describe EM metadata.
Existing standards like .edi can be converted easily and software tools are openly available.
For EPOS, the data from all TCSs are searchable via the centralised Integrated Core Services (ICS) portal.
The portal itself only provides a search function through metadata provided by various TCSs, but the data
themselves are stored on individual TCS servers. We have established data storage of MT data at BGS andLTU facilities and discuss with other national research institutes to test and use their facilities for sustained MTdata storage and access.

Keywords: EPOS, magnetotelluric data, database, data format

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Multidimensional Interpretation of Controlled-Source Radio-Magnetotelluric(CSRMT) of a waste-site in Cologne, Germany
S. Fadavi Asghari1, A. Shlykov2, M. Smirnova3, A. Saraev4, P. Yogeshwar5 and B. Tezkan6
1Institute of Geophysics and Meteorology, University of Cologne, sfadavia@smail.uni-koeln.de
2Institute of Earth Sciences, St. Petersburg State University, shlykovarseny@gmail.com
3Institute of Geophysics and Meteorology, University of Cologne, maria.smirnova@uni-koeln.de
4Institute of Earth Sciences, St. Petersburg State University, asaraev51@mail.ru
5Institute of Geophysics and Meteorology, University of Cologne, yogeshwar@geo.uni-koeln.de
6Institute of Geophysics and Meteorology, University of Cologne, tezkan@geo.uni-koeln.de

SUMMARY
Radio-Magnetotelluric (RMT) method is based on measurements of the electromagnetic (EM) field using
military and civilian radio transmitters broadcasting in a frequency range between 10 to 1000 kHz as the source.
In order to reach to higher signal to noise ratio and a deeper penetration depth, CSRMT measurements are
performed using a controlled-source in a wider frequency range of 1 to 1000 kHz.
We accomplished a dense CSRMT survey over a waste-site in Cologne, Germany. The site was used as sandand gravel pit from 1940s to the 1950s. Afterwards, it was filled with different kinds of wastes including
household refuse, construction and industrial waste, cinder, tires, wood, plastic, military fences, etc. The aimof the CSRMT survey is to detect the boundary and the basement of the waste body and possibly any signatureof contamination leakage to the deeper subsurface.
Two perpendicular transmitters, each 265 and 580 meters long, were set-up to obtain the full impedance tensor
and the tipper elements. In order to validate the far-field condition, RMT measurements were also carried out
with the transmitters switched off. The far-field data have been acquired in a total of 177 stations from thirteen
profiles. The separation between the stations and the profiles was 10 and 30 m respectively.
The RMT and CSRMT data acquired in this field experiment, were processed and the corresponding apparent
resistivity, phase and tipper were calculated. Here, we present and discuss the 2D and 3D inversion of thecomputed transfer functions.
In general, we image a high conductive waste body extending to a maximum depth of 15 m. The exploration
depth is around 50 m in average. The waste body indicates an internal structuring and is well confined to theformer pit area. Below the waste, sandy gravel is deposited.
Outside the waste the subsurface is highly resistive.
The CSRMT results, are in a good agreement with former DCR results that are obtained from the same region
indicating the reliability of the data acquisition, processing and inversion.
Keywords: electromagnetic, CSRMT, inversion, conductivity, waste-site

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Delineating subsurface structures for deep aquifer study using MT, and airborne
geophysics. Case study of the Voltaian sedimentary basin, Ghana West Africa.
R.A. Mejida1,2, P. Tarits1, T.E. Armah2 , S. Hautot3 and S.M. Yidana 2
1 IUEM, Geo-Ocean, rmejida@ug.edu.gh, tarits@univ-brest.fr
2 University of Ghana, tekarmah@ug.edu.gh, smyidana@ug.edu.gh
3 IMAGIR, sophie.hautot@imagir.eu

SUMMARY
Locating groundwater productive zones in the near surface in the Voltaian sedimentary basin in Ghana has
been challenging over the past years causing water scarcity among residents in the basin. Resident relies soleon groundwater for their domestic supply as a result of extreme temperatures and high evapotranspiration
following a prolong dry season, causes surface water bodies to dry up. To address this challenge, airborne
geophysical data and magnetotellurics data has used to attempt to delineate aquifers deeper beyond theweathered zone in the basin. The results of the integrated geophysical approach suggest the presence of alow resistivity layer lies between 200m to 550 m within the sandstones and could have potential for holding
groundwater.
Keywords: Magnetotellurics, Inversion, Airborne geophysics, water resources, Voltaian Sedimentary Basin

Groundwater is the main source of potable water forindigenes in the northern part of the several kilometers
deep Voltaian Sedimentary Basin (VSB) in Ghana (Figure
1).

Figure 1 : Geology of Voltaian Sedimentary Basin

Groundwater exploration in the VSB over the years has
yielded very little success with a failure rate of about 60
percent (Agyekum and Asare, 2016). Electrical resistivity
tomography has been the main geophysical techniqueused for groundwater exploration in the VSB targeting
the weathered zone of the near surface. The low yields
of boreholes have pushed contractors to target deeper
aquifers as against the shallow aquifers in the basin to
supply sufficient water to cover the rapid economic
development coupled with population growth in thebasin. The complexity and paucity of the outcrops has
hampered sufficient mapping of the basin structure
down to the Proterozoic basement. To address thisproblem, and to offer a detailed description of the basin
structure, a preliminary integrated geophysicalinvestigation approach using existing airborne
geophysical data (obtained from the Ghana geologicalsurvey authority) and magnetotelluric (MT) dataobtained from field survey undertaken within the scopeof this project in the north part of the VSB. The target
areas for MT soundings were identified from airborne
geophysical data (EM, magnetic and gravity, Fugro(2009)). Vertical gradient maps of the airborne magnetic
data and Euler solutions of the airborne gravity data
revealed the presence of several geological structures(faults and folds) within the thick platform sediment(Figure 3) revealing their potential as deep-seatedaquifers. The depth to basement is observed toaveraging about 4.5 km with the MT while with the
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airborne gravity and magnetic data, the depth is 5.5 km
and 4 km respectively. A total of 13 broad-band MT
soundings along two profiles lines orienting north-south
were conducted. A preliminary 3-D inversion of thewhole data set showed the presence of conductive
layers within the basin (Figure 2).

siltstones in the area. Also, we will be carrying out
composition analysis as well as porosity andpermeability test of some outcrops in the region tofurther improve this work.

ACKNOWLEDGEMENTS
The authors thank the French embassy in Ghana
and the Ghana government for their financial
support toward this work.
REFERENCES
Agyekum W.A. and Asare E.B. (2016) Challenges
associatedwithgroundwater
resource
development in Northern Ghana. Ghana journalof science 56: 39-51

Figure 2. Southern and northern resistivity sections
from the 3-D inversion results.
One dimensional MT inversion of the Berdichevsky
invariant (e.g. Simpson and Bahr, 2005) was used to
improve the vertical resolution at each site. The average
thickness of the low resistive layer is about 300 m andwith values which ranges between a few tens of ohm mcorresponding to resistivity values of fresh groundwater.
Further work will target proper delineation of thepossible deep-seated aquifers possibly within thesandstones known to intercalate with the shales andFugro Airborne Survey (2009) Logistics andprocessing report, airborne magnetics and
GEOTEM survey. Area 1 to 8, Ghana 92.
Simpson F. and Bahr K. (2005) Practicalmagnetotellurics. Cambridge University Press,
Cambridge

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Figure 3. The vertical derivative of the magnetic data shows geological structures in the northern part of theVoltaian sedimentary basin in Ghana.

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

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Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

Investigation of the topographic effect in the San Pedro-Ceboruco graben: real data andsynthetic studies
M. K. Díaz1,2, C. Castro1, A. Junge1, F. Corbo2
1
Goethe-Universität Frankfurt am Main, mdiazna@geociencias.unam.mx
1
Goethe-Universität Frankfurt am Main, castro@geophysik.uni-frankfurt.de1
Goethe-Universität Frankfurt am Main, junge@geophysik.uni-frankfurt.de2
Universidad Nacional Autónoma de México, fercorbo@geociencias.unam.mx______________________________________________________________________________
Summary
The Magnetotelluric (MT) Transfer Functions (TF) establish the relationship between time variationsof the electric and magnetic fields at the Earth’s surface. Thus, they provide frequency-dependent
information about the electrical resistivity structure of the subsurface. The TFs can be strongly
influenced by topography and, cause misinterpretation in subsequent modelling. Therefore,
modelling topographic effects could lead to a deeper understanding of the TF during theinterpretation process.
A MT survey was carried out in the framework of the projects SPCGa and DEMITZb along the NW
Tepic-Zacoalco Rift (TZR), where important volcanic structures are present. The purpose of theseprojects is to investigate the magmatic systems and the conductivity distribution in the subsurface atthe northern part of the TZR, known as the San Pedro-Ceboruco graben (SPCG). The processing
results of measured time series, reveal induction arrows pointing away from the volcanic edifices(Wiese convention) for periods around 1s.
To determine the contribution of the topographic effect on the observed data, 3-D forward responses
are calculated accounting for a simplified topography, including the actual dimensions and positionsof the San Pedro Dome and Ceboruco Volcano, both located in the SPCG. The responses obtainedin the synthetic study reveal a strong influence of topography at frequencies above 1Hz represented
by large induction arrows and phase tensor split. Attempting to reproduce the observed data, thespatial and frequency distribution of TFs simulated for conductivity anomalies with different
geometries and varying background resistivities.
The simulation study reveals that the observed TFs do not result from topography but give evidence
for a conductive body centered in-between the existing volcanoes.
Keywords: Magnetotelluric Transfer Functions, Topography, Modelling, Magmatic Systems.
PAPIIT funded project “Geothermal assessing and determination of the magmatic source in the San
Pedro-Ceboruco graben (SPCG)”.
a

DFG funded project “Mid-to-Deep-crustal Electromagnetic Investigation of Tepic-Zacoalco Rift:
Exploring Magmatic Systems and Electrical Anisotropy in Western Mexico (DEMITZ)”.
b_____________________________________________________________________________

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