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Journal articles on the topic 'Geophysics; Electrical resistivity tomography'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Pidlisecky, Adam, Rosemary Knight, and Eldad Haber. "Cone-based electrical resistivity tomography." GEOPHYSICS 71, no. 4 (July 2006): G157—G167. http://dx.doi.org/10.1190/1.2213205.

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Determining the 3D spatial distribution of subsurface properties is a challenging, but critical, part of managing the cleanup of contaminated sites. We have developed a minimally invasive technology that can provide information about the 3D distribution of electrical conductivity. The technique, cone-based electrical resistivity tomography (C-bert), integrates resistivity tomography with cone-penetration testing. Permanent current electrodes are emplaced in the subsurface and used to inject current into the subsurface region of interest. The resultant potential fields are measured using a surface reference electrode and an electrode mounted on a cone penetrometer. The standard suite of cone penetration measurements, including high-resolution resistivity logs, are also obtained and are an integral part of the C-bert method. C-bert data are inverted using an inexact Gauss-Newton algorithm to produce a 3D electrical conductivity map. A majorchallenge with the inversion is the large local perturbation around the measurement location, due to the highly conductive cone. As the cone is small with respect to the total model space, explicit modeling of the cone is cost prohibitive. We have developed a rapid method for solving the forward model which uses iteratively determined boundary conditions (IDBC). This allows us to generate a computationally feasible, preinversion correction for the cone perturbation. We assessed C-bert by performing a field test to image the conductivity structure of the Kidd 2 site near Vancouver, British Columbia. A total of nine permanent current electrodes were emplaced and five C-bert data sets were obtained, resulting in 6516 data points. These data were inverted to obtain a 3D conductivity image of the subsurface. Furthermore, we demonstrated, using a synthetic experiment, that C-bert can yield high quality electrical conductivity images in challenging field situations. We conclude that C-bert is a promising new imaging technique.

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2

Putiška, René, Maroš Nikolaj, Ivan Dostál, and David Kušnirák. "Determination of cavities using electrical resistivity tomography." Contributions to Geophysics and Geodesy 42, no. 2 (January 1, 2012): 201–11. http://dx.doi.org/10.2478/v10126-012-0018-3.

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Abstract Geophysical surveys for cavity detection are one of the most common nearsurface applications. The usage of resistivity methods is also very straightforward for the air-filled underground voids, which should have theoretically infinite resistivity in the ERT image. In the first part of the paper, we deal with the comparison of detectability of the cavity by several types of the electrode arrays, the second part discusses the effect of a thin layer around the cavity itself, by means of 2D modelling. The presence of this layer deforms the resistivity image significantly as the resistive anomaly could be turned into a conductive one, in the case when the thin layer is more conductive than the background environment. From the electrical array analysis for the model situation a dipole-dipole and combined pole-dipole shows the best results among the other involved electrical arrays.

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3

Nero, Callistus, Akwasi Acheampong Aning, Sylvester K. Danuor, and Reginald M. Noye. "Delineation of graves using electrical resistivity tomography." Journal of Applied Geophysics 126 (March 2016): 138–47. http://dx.doi.org/10.1016/j.jappgeo.2016.01.012.

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4

Hörning, S., L. Gross, and A. Bárdossy. "Geostatistical electrical resistivity tomography using random mixing." Journal of Applied Geophysics 176 (May 2020): 104015. http://dx.doi.org/10.1016/j.jappgeo.2020.104015.

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5

Kneisel, C., A. Bast, and D. Schwindt. "Quasi-3-D resistivity imaging – mapping of heterogeneous frozen ground conditions using electrical resistivity tomography." Cryosphere Discussions 3, no. 3 (October 30, 2009): 895–918. http://dx.doi.org/10.5194/tcd-3-895-2009.

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Abstract. Up to now an efficient 3-D geophysical mapping of the subsurface in mountainous environments with rough terrain has not been possible. A merging approach of several closely spaced 2-D electrical resistivity tomography (ERT) surveys to build up a quasi-3-D model of the electrical resistivity is presented herein as a practical compromise for inferring subsurface characteristics and lithology. The ERT measurements were realised in a small glacier forefield in the Swiss Alps with complex terrain exhibiting a small scale spatial variability of surface substrate. To build up the grid for the quasi-3-D measurements the ERT surveys were arranged as parallel profiles and perpendicular tie lines. The measured 2-D datasets were collated into one quasi-3-D file. A forward modelling approach – based on studies at a permafrost site below timberline – was used to optimize the geophysical survey design for the mapping of the mountain permafrost distribution in the investigated glacier forefield. Quasi-3-D geoelectrical imaging is a useful method for mapping of heterogeneous frozen ground conditions and can be considered as a further milestone in the application of near surface geophysics in mountain permafrost environments.

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6

Putiška, René, Ivan Dostál, and David Kušnirák. "Determination of dipping contacts using electrical resistivity tomography." Contributions to Geophysics and Geodesy 42, no. 2 (January 1, 2012): 161–80. http://dx.doi.org/10.2478/v10126-012-0007-6.

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Determination of dipping contacts using electrical resistivity tomographyGenerally, all electrode arrays are able to delineate the contact of two lithostratigraphic units especially with very high resistivity contrast. However, the image resolution for the location of vertical and dipping structures is different. The responses of dipole-dipole (DD), Wenner alpha (WA), Schlumberger (SCH) and combined pole-dipole (PD) arrays have been computed using the finite difference method. Comparison of the responses indicates that: (1) The dipole-dipole array usually gives the best resolution and is the most detailed method especially for the detection of vertical structures. This array has shown the best resolution to recognize the geometrical characterisation of the fault. (2) The pole-dipole has shown the second best result in our test. The PD is an effective method for detection of vertical structures with a high depth range, but the deepest parts are deformed. (3) Wenner alpha shows a low resolution, inconvenient for detailed investigation of dip structures. (4) The Schlumberger array gives a good and sharp resolution to assess the contact between two lithological units but gives poor result for imaging geometry of dipping contact.

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7

Mollica, R., R. de Franco, G. Caielli, G. Boniolo, G. B. Crosta, A. Motti, A. Villa, and R. Castellanza. "Micro electrical resistivity tomography for seismic liquefaction study." Journal of Applied Geophysics 180 (September 2020): 104124. http://dx.doi.org/10.1016/j.jappgeo.2020.104124.

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8

Daily, William, and Earle Owen. "Cross‐borehole resistivity tomography." GEOPHYSICS 56, no. 8 (August 1991): 1228–35. http://dx.doi.org/10.1190/1.1443142.

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Electrical resistivity tomography (ERT) is a method for determining the electrical resistivity distribution in a volume from discrete measurements of current and voltage made within the volume or on its surface. We have developed an ERT algorithm that is an iterative, modified least squares inversion, based on a finite element forward solution of Laplace’s equation. We report the results of tests on this algorithm designed to determine how resistance measurements made from two boreholes may be used to image the resistivity distribution between them. A number of simple but geophysically significant structures are modeled. These include a single isolated block anomaly, two layers, a thin isolated continuous layer, and a vertical band. The main features of most resistivity models were identifiable in the reconstructions. Limited data accuracy and noise were simulated and found to cause a deterioration of the image. However, even with measurements of only one significant figure accuracy, the algorithm converged toward the desired solution for at least the first iteration and the targets were identifiable in the reconstructions. Imprecision in the data influences convergence as well as image quality; more iterations eventually lead to divergence. Spatial resolution depends on such factors as data errors and the specific target geometry.

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9

Sudha, Kumari, M. Israil, S. Mittal, and J. Rai. "Soil characterization using electrical resistivity tomography and geotechnical investigations." Journal of Applied Geophysics 67, no. 1 (January 2009): 74–79. http://dx.doi.org/10.1016/j.jappgeo.2008.09.012.

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10

Lochbühler, Tobias, Stephen J. Breen, Russell L. Detwiler, Jasper A. Vrugt, and Niklas Linde. "Probabilistic electrical resistivity tomography of a CO2 sequestration analog." Journal of Applied Geophysics 107 (August 2014): 80–92. http://dx.doi.org/10.1016/j.jappgeo.2014.05.013.

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11

Robbins, Austin R., and Alain Plattner. "Offset-electrode profile acquisition strategy for electrical resistivity tomography." Journal of Applied Geophysics 151 (April 2018): 66–72. http://dx.doi.org/10.1016/j.jappgeo.2018.01.027.

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12

Aleardi, Mattia, Alessandro Vinciguerra, and Azadeh Hojat. "A convolutional neural network approach to electrical resistivity tomography." Journal of Applied Geophysics 193 (October 2021): 104434. http://dx.doi.org/10.1016/j.jappgeo.2021.104434.

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13

Vu, M. T., A. Jardani, A. Revil, and M. Jessop. "Magnetometric resistivity tomography using chaos polynomial expansion." Geophysical Journal International 221, no. 3 (February 14, 2020): 1469–83. http://dx.doi.org/10.1093/gji/ggaa082.

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SUMMARY We present an inversion algorithm to reconstruct the spatial distribution of the electrical conductivity from the analysis of magnetometric resistivity (MMR) data acquired at the ground surface. We first review the theoretical background of MMR connecting the generation of a magnetic field in response to the injection of a low-frequency current source and sink in the ground given a known distribution of electrical conductivity in the subsurface of the Earth. The forward modelling is based on sequentially solving the Poisson equation for the electrical potential distribution and the magnetostatic (Biot and Savart) equation for the magnetic field. Then, we introduce a Gauss–Newton inversion algorithm in which the logarithm of the electrical conductivity field is parametrized by using the chaos polynomial expansion in order to reduce the number of model parameters. To illustrate how the method works, the algorithm is successfully applied on four synthetic models with 3-D heterogeneous distribution of the electrical conductivity. Finally, we apply our algorithm to a field case study in which seepage was known to be occurring along an embankment of a headrace channel to a power station.

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14

Arjwech, Rungroj, and Mark E. Everett. "Electrical Resistivity Tomography at Construction Sites in Northeast Thailand with Implications for Building Foundation Design." Journal of Environmental and Engineering Geophysics 24, no. 2 (June 2019): 333–40. http://dx.doi.org/10.2113/jeeg24.2.333.

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A good understanding of the subsurface geological conditions at proposed construction sites is a fundamental requirement to design appropriate building foundations. In this study, the 2D electrical resistivity tomography (ERT) method was used to characterize the subsurface geology at three active construction sites located on or near exposed bedrock in northeast Thailand. The resistivity tomograms proved useful for determining the thickness of intact bedrock overlying a potentially weaker weathered rock of variable saturation. The wide-area information provided by the ERT method should be helpful to foundation design engineers assuming they have confidence in the geophysical results. Geophysics was also useful to guide suitable locations for ongoing geotechnical tests at a given construction site especially if difficult ground conditions exist.

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15

Christensen, N. B., D. Sherlock, and K. Dodds. "Monitoring CO2 Injection with Cross-Hole Electrical Resistivity Tomography." Exploration Geophysics 37, no. 1 (March 2006): 44–49. http://dx.doi.org/10.1071/eg06044.

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16

Drahor, Mahmut G., Meriç A. Berge, Özde Bakak, and Caner Öztürk. "Electrical resistivity tomography monitoring studies at Balçova (Turkey) geothermal site." Near Surface Geophysics 12, no. 3 (October 2013): 337–50. http://dx.doi.org/10.3997/1873-0604.2013054.

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17

Zhou, Bing, Youcef Bouzidi, Saif Ullah, and Muhammad Asim. "A full‐range gradient survey for 2D electrical resistivity tomography." Near Surface Geophysics 18, no. 6 (October 14, 2020): 609–26. http://dx.doi.org/10.1002/nsg.12125.

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18

Wang, Haoran, Chih-Ping Lin, and Hsin-Chang Liu. "Pitfalls and refinement of 2D cross-hole electrical resistivity tomography." Journal of Applied Geophysics 181 (October 2020): 104143. http://dx.doi.org/10.1016/j.jappgeo.2020.104143.

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19

Clement, R., and S. Moreau. "How should an electrical resistivity tomography laboratory test cell be designed? Numerical investigation of error on electrical resistivity measurement." Journal of Applied Geophysics 127 (April 2016): 45–55. http://dx.doi.org/10.1016/j.jappgeo.2016.02.008.

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20

Bazin, S., and A. A. Pfaffhuber. "Mapping of quick clay by electrical resistivity tomography under structural constraint." Journal of Applied Geophysics 98 (November 2013): 280–87. http://dx.doi.org/10.1016/j.jappgeo.2013.09.002.

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21

Bouchedda, Abderrezak, Giroux Bernard, and Erwan Gloaguen. "Constrained electrical resistivity tomography Bayesian inversion using inverse Matérn covariance matrix." GEOPHYSICS 82, no. 3 (May 1, 2017): E129—E141. http://dx.doi.org/10.1190/geo2015-0673.1.

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Bayesian inversion using maximum a posteriori estimator is a quantitative approach that has been successfully applied to the electrical resistivity tomography inverse problem. In most approaches, model covariance parameters are generally chosen as stationary and isotropic, which assumes a statistical homogeneity of the studied field. However, the statistical properties of resistivity within the earth are, in reality, location dependent due to spatially varying processes that control the bulk resistivity of rocks, such as water content, porosity, clay content, etc. Taking into account the spatial variability of the resistivity field, we use the nonstationary Matérn covariance family, which is defined through linear stochastic partial differential equations. Two types of prior information are considered: structure orientation and spatially increasing the range with increasing depth. The latter is applied successfully on the first synthetic model, which aims at retrieving the depth of bedrock and the shape of the conductive lens. In the second synthetic example, a conductive dike model embedded into four layers is used to study the performance of structure orientation. Finally, the proposed approach is used to invert real data measured over an extensively characterized sandy-to-silty aquifer. First, the structure orientation of this aquifer was determined by applying a structure tensor calculated using gradients of the ground penetrating radar image. The introduction of this information gives a resistivity model that is more compatible with the aquifer structure.

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22

Paglis, Carlos Mauricio. "Application of Electrical Resistivity Tomography for Detecting Root Biomass in Coffee Trees." International Journal of Geophysics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/383261.

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Roots play an important role in plants and are responsible for several functions; among them are anchorage and nutrient and water absorption. Several methodologies are being tested and used to study plant root systems in order to avoid destructive root sampling. Electrical resistivity tomography is among these methodologies. The aim of this preliminary study was to use electrical resistivity for detecting root biomass in coffee trees. Measurements were performed in a soil transect with an ABM AL 48-b resistivimeter with a pole-dipole configuration. The tomograms indicated variability in soil resistivity values ranging from 120 to 1400 Ω·m−1. At the first 0.30 cm soil layer, these values were between 267 and 952 Ω·m−1. Oriented by this result, root samples were taken at 0.10, 0.20, and 0.30 m depths within 0.50 m intervals along the soil transect to compare soil resistivity with root mass density (RMD). RMD data, up to this depth, varied from 0.000019 to 0.009469 Mg·m−3, showing high spatial variability and significant relationship to the observed values of soil resistivity. These preliminary results showed that the electrical resistivity tomography can contribute to root biomass studies in coffee plants; however, more experiments are necessary to confirm the found results in Brazil coffee plantations.

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23

Skibbe, Nico, Raphael Rochlitz, Thomas Günther, and Mike Müller-Petke. "Coupled magnetic resonance and electrical resistivity tomography: An open-source toolbox for surface nuclear-magnetic resonance." GEOPHYSICS 85, no. 3 (April 8, 2020): F53—F64. http://dx.doi.org/10.1190/geo2019-0484.1.

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Nuclear-magnetic resonance (NMR) is a powerful tool for groundwater system imaging. Ongoing developments in surface NMR, for example, multichannel devices, allow for investigations of increasingly complex subsurface structures. However, with the growing complexity of field cases, the availability of appropriate software to accomplish the in-depth data analysis becomes limited. The open-source Python toolbox coupled magnetic resonance and electrical resistivity tomography (COMET) provides the community with a software for modeling and inversion of complex surface NMR data. COMET allows the NMR parameters’ water content and relaxation time to vary in one dimension or two dimensions and accounts for arbitrary electrical resistivity distributions. It offers a wide range of classes and functions to use the software via scripts without in-depth programming knowledge. We validated COMET to existing software for a simple 1D example. We discovered the potential of COMET by a complex 2D case, showing 2D inversions using different approximations for the resistivity, including a smooth distribution from electrical resistivity tomography (ERT). The use of ERT-based resistivity results in similar water content and relaxation time images compared with using the original synthetic block resistivity. We find that complex inversion may indicate incorrect resistivity by non-Gaussian data misfits, whereas amplitude inversion shows well-fitted data, but leading to erroneous NMR models.

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24

Chambers, Jonathan E., Oliver Kuras, Philip I. Meldrum, Richard D. Ogilvy, and Jonathan Hollands. "Electrical resistivity tomography applied to geologic, hydrogeologic, and engineering investigations at a former waste-disposal site." GEOPHYSICS 71, no. 6 (November 2006): B231—B239. http://dx.doi.org/10.1190/1.2360184.

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A former dolerite quarry and landfill site was investigated using 2D and 3D electrical resistivity tomography (ERT), with the aims of determining buried quarry geometry, mapping bedrock contamination arising from the landfill, and characterizing site geology. Resistivity data were collected from a network of intersecting survey lines using a Wenner-based array configuration. Inversion of the data was carried out using 2D and 3D regularized least-squares optimization methods with robust (L1-norm) model constraints. For this site, where high resistivity contrasts were present, robust model constraints produced a more accurate recovery of subsurface structures when compared to the use of smooth (L2-norm) constraints. Integrated 3D spatial analysis of the ERT and conventional site investigation data proved in this case a highly effective means of characterizing the landfill and its environs. The 3D resistivity model was successfully used to confirm the position of the landfill boundaries, which appeared as electrically well-defined features that corresponded extremely closely to both historic maps and intrusive site investigation data. A potential zone of leachate migration from the landfill was identified from the electrical models; the location of this zone was consistent with the predicted direction of groundwater flow across the site. Unquarried areas of a dolerite sill were imaged as a resistive sheet-like feature, while the fault zone appeared in the 2D resistivity model as a dipping structure defined by contrasting bedrock resistivities.

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25

Papadopoulos, Nikos G., Myeong-Jong Yi, Jung-Ho Kim, Panagiotis Tsourlos, and Gregory N. Tsokas. "Geophysical investigation of tumuli by means of surface 3D Electrical Resistivity Tomography." Journal of Applied Geophysics 70, no. 3 (March 2010): 192–205. http://dx.doi.org/10.1016/j.jappgeo.2009.12.001.

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26

Bergmann, Peter, Cornelia Schmidt-Hattenberger, Dana Kiessling, Carsten Rücker, Tim Labitzke, Jan Henninges, Gunther Baumann, and Hartmut Schütt. "Surface-downhole electrical resistivity tomography applied to monitoring of CO2 storage at Ketzin, Germany." GEOPHYSICS 77, no. 6 (November 1, 2012): B253—B267. http://dx.doi.org/10.1190/geo2011-0515.1.

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Surface-downhole electrical resistivity tomography (SD-ERT) surveys were repeatedly carried out to image [Formula: see text] injected at the pilot storage Ketzin, Germany. The experimental setup combines surface with downhole measurements by using a permanent electrode array that has been deployed in three wells. Two baseline experiments were performed during the site startup and three repeat experiments were performed during the first year of CO2 injection. By the time of the third repeat, approximately 13,500 tons of [Formula: see text] had been injected into the reservoir sandstones at about 650 m depth. Field data and inverted resistivity models showed a resistivity increase over time at the [Formula: see text] injector. The lateral extent of the related resistivity signature indicated a preferential [Formula: see text] migration toward the northwest. Using an experimental resistivity-saturation relationship, we mapped [Formula: see text] saturations by means of the resistivity index method. For the latest repeat, [Formula: see text] saturations show values of up to 70% near the injection well, which matches well with [Formula: see text] saturations determined from pulsed neutron-gamma logging. The presence of environmental noise, reservoir heterogeneities, and irregularities in the well completions are the main sources of uncertainty for the interpretations. The degradation of the permanently installed downhole components is monitored by means of frequently performed resistance checks. In consistency with the SD-ERT data, these resistance checks indicate a long-term resistivity increase near the [Formula: see text] injector. In conclusion, the investigations demonstrate the capability of surface-downhole electrical resistivity tomography to image geologically stored [Formula: see text] at the Ketzin site.

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27

Wang, Yuling, Ya Xu, Changxin Nai, and Lu Dong. "Assessment of Chromium Waste Contamination by Electrical Resistivity Tomography: A Case Study." Journal of Environmental and Engineering Geophysics 24, no. 1 (March 2019): 163–67. http://dx.doi.org/10.2113/jeeg24.1.163.

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This study aims to determine the chromium contamination at an abandoned chemical factory by electrical resistivity tomography (ERT). Five ERT survey lines were conducted in the main production plant and two boreholes were drilled to collect soil samples for soil analysis. The 2D and 3D resistivity model were constructed to evaluate the pollution plumes. The ERT results showed that seven low-resistivity zones are observed in the 2D resistivity profiles, which may indicate the main pollution areas at the site. The 3D electrical resistivity model further showed that the soil pollution is more severe in the southwest than in the other areas of the site. The ERT results were partly verified by chemical analysis of soil samples. These ERT results can be further used for additional designs of soil and groundwater sampling.

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28

Ranieri, G., L. Sharpe, A. Trogu, and C. Piga. "Time-lapse electrical resistivity tomography to delineate mud structures in archaeological prospections." Near Surface Geophysics 5, no. 6 (August 1, 2007): 375–82. http://dx.doi.org/10.3997/1873-0604.2007019.

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29

Rings, Joerg, Alexander Scheuermann, Kwasi Preko, and Christian Hauck. "Soil water content monitoring on a dike model using electrical resistivity tomography." Near Surface Geophysics 6, no. 2 (December 1, 2007): 123–32. http://dx.doi.org/10.3997/1873-0604.2007038.

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30

Naudet, V., J. C. Gourry, F. Girard, F. Mathieu, and A. Saada. "3D electrical resistivity tomography to locate DNAPL contamination around a housing estate." Near Surface Geophysics 12, no. 3 (November 2012): 351–60. http://dx.doi.org/10.3997/1873-0604.2012059.

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31

Caterina, David, Jean Beaujean, Tanguy Robert, and Frédéric Nguyen. "A comparison study of different image appraisal tools for electrical resistivity tomography." Near Surface Geophysics 11, no. 6 (April 1, 2013): 639–58. http://dx.doi.org/10.3997/1873-0604.2013022.

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32

Piegari, E., V. Cataudella, R. Di Maio, L. Milano, M. Nicodemi, and M. G. Soldovieri. "Electrical resistivity tomography and statistical analysis in landslide modelling: A conceptual approach." Journal of Applied Geophysics 68, no. 2 (June 2009): 151–58. http://dx.doi.org/10.1016/j.jappgeo.2008.10.014.

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Ochs, Johanna, and Norbert Klitzsch. "Considerations regarding small-scale surface and borehole-to-surface electrical resistivity tomography." Journal of Applied Geophysics 172 (January 2020): 103862. http://dx.doi.org/10.1016/j.jappgeo.2019.103862.

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34

Oldenborger, Greg A., Partha S. Routh, and Michael D. Knoll. "Sensitivity of electrical resistivity tomography data to electrode position errors." Geophysical Journal International 163, no. 1 (October 2005): 1–9. http://dx.doi.org/10.1111/j.1365-246x.2005.02714.x.

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35

Carrière, Simon D., Konstantinos Chalikakis, Charles Danquigny, and Laura Torres-Rondon. "Using resistivity or logarithm of resistivity to calculate depth of investigation index to assess reliability of electrical resistivity tomography." GEOPHYSICS 82, no. 5 (September 1, 2017): EN93—EN98. http://dx.doi.org/10.1190/geo2016-0244.1.

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We have conducted a comparative study to determine the most efficient and reliable way to calculate the depth of investigation (DOI) index to assess the quality of electrical resistivity tomography models. We compared the results of using resistivity and logarithm of resistivity after testing them on four synthetic models by direct modeling and a field case, in which the resistivity model was validated by auger drillings. We tested the two most commonly used acquisition arrays, dipole-dipole, and Wenner-Schlumberger. The index calculated with the logarithm of resistivity clearly appears to be more satisfactory than the resistivity-based index. The method based on resistivity systematically overestimates risk (high DOI) in areas of high resistivity, and it underestimates risk in conductive zones. As a result, we strongly recommend the use of the logarithm of inverted resistivity to calculate the DOI index.

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36

Daily, William, and Abelardo L. Ramirez. "Electrical imaging of engineered hydraulic barriers." GEOPHYSICS 65, no. 1 (January 2000): 83–94. http://dx.doi.org/10.1190/1.1444728.

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Electrical resistance tomography (ERT) was used to image the full‐scale test emplacement of a thin‐wall grout barrier installed by high‐pressure jetting and a thick‐wall polymer barrier installed by low‐pressure permeation injection. Both case studies compared images of electrical resistivity before and after barrier installation. Barrier materials were imaged as anomalies which were more electrically conducting than the native sandy soils at the test sites. Although the spatial resolution of the ERT was insufficient to resolve flaws smaller than a reconstruction voxel (50 cm on a side), the images did show the spatial extent of the barrier materials and therefore the general shape of the structures. To verify barrier performance, ERT was also used to monitor a flood test of a thin‐wall grout barrier. Electrical resistivity changes were imaged as a saltwater tracer moved through the barrier at locations which were later found to be defects in a wall or the joining of two walls.

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Butchibabu, B., Prosanta K. Khan, and P. C. Jha. "Foundation Evaluation of a Repeater Installation Building using Electrical Resistivity Tomography and Seismic Refraction Tomography." Journal of Environmental and Engineering Geophysics 24, no. 1 (March 2019): 27–38. http://dx.doi.org/10.2113/jeeg24.1.27.

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Geophysical investigations were carried out for evaluation of damage and to assess the possible causes for repeated occurrence of damage at one of the buildings constructed for oil pumping in the northern part of India. Electrical Resistivity Tomography (ERT) and Seismic Refraction Tomography (SRT) techniques were adopted for studying the subsurface of the area around the building with an objective of ascertaining the cause of damage. High resolution imaging was done using both the techniques in this investigation. ERT delineated the presence of low resistivity (2 ohm-m) water filled voids below the structures and mapped different subsurface layers such as sandy soil, clay and sandstone in the study area. SRT revealed P-wave velocity ( V P ) of the subsurface medium in the range of 400–3,400 m/s. Corresponding densities and S-wave velocities ( V S ) were determined based on Gardner's and Castagna's relationships. Subsequently, the V P , V S and the modulus values were used in estimating compressibility of soil and rock strata. Results showed near surface layers were characterized by high compressibility (26.673 × 10 −5 Pa −1 ), decreases with depth. This paper presents the details of the site, techniques used in the investigation and correlation of geophysical results with lithological information, and the subsequent analysis for understanding the distress in the subsurface of the study area.

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Carollo, Alessandra, Patrizia Capizzi, and Raffaele Martorana. "Joint interpretation of seismic refraction tomography and electrical resistivity tomography by cluster analysis to detect buried cavities." Journal of Applied Geophysics 178 (July 2020): 104069. http://dx.doi.org/10.1016/j.jappgeo.2020.104069.

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Lesur, Vincent, Michel Cuer, and André Straub. "2-D and 3-D interpretation of electrical tomography measurements, Part 2: The inverse problem." GEOPHYSICS 64, no. 2 (March 1999): 396–402. http://dx.doi.org/10.1190/1.1444544.

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The interpretation of borehole‐to‐borehole electrical measurements requires solving an inverse problem for a given class of model geometries. The usual approach to an inverse problem includes a model dependent task (i.e., forward modeling) and a generic task (i.e., an optimization scheme). We have developed an optimization algorithm using a nonlinear inversion technique. This algorithm allows recovery of a possible resistivity distribution in an investigated zone between two boreholes or in the vicinity of them. This resistivity distribution is defined as a set of 2-D or 3-D volumes of constant resistivity. The inversion procedure minimizes a least‐squares term plus a damping term. This latter term seeks to minimize the roughness of the solution. An improved form of this smoothness term may enhance the spatial resolution of the resistivity image, assuming that the resistivity contrast is known a priori. This reconstruction algorithm has been tested for both 2-D and 3-D geometries. These inversion tests were conclusive enough such that a successful interpretation on a 16 × 16 grid has been carried out for a field data set obtained from a mineral exploration test site.

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Simyrdanis, Kleanthis, Ian Moffat, Nikos Papadopoulos, Jarrad Kowlessar, and Marian Bailey. "3D Mapping of the Submerged Crowie Barge Using Electrical Resistivity Tomography." International Journal of Geophysics 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6480565.

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This study explores the applicability and effectiveness of electrical resistivity tomography (ERT) as a tool for the high-resolution mapping of submerged and buried shipwrecks in 3D. This approach was trialled through modelling and field studies of Crowie, a paddle steamer barge which sunk at anchor in the Murray River at Morgan, South Australia, in the late 1950s. The mainly metallic structure of the ship is easily recognisable in the ERT data and was mapped in 3D both subaqueously and beneath the sediment-water interface. The innovative and successful use of ERT in this case study demonstrates that 3D ERT can be used for the detailed mapping of submerged cultural material. It will be particularly useful where other geophysical and diver based mapping techniques may be inappropriate due to shallow water depths, poor visibility, or other constraints.

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Mulyono, Asep, Ilham Arisbaya, and Yayat Sudrajat. "IMAGING TREE ROOT ZONE GEOMETRY USING ELECTRICAL RESISTIVITY TOMOGRAPHY." RISET Geologi dan Pertambangan 30, no. 1 (July 20, 2020): 55. http://dx.doi.org/10.14203/risetgeotam2020.v30.1074.

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Root zone geometry research is usually done in a conventional way which is destructive, time-consuming, and requires a considerable cost. Several non-destructive measurements used geophysical methods have been developed, one of which is the Electrical Resistivity Tomography (ERT) method. Tree root zone determination using ERT has been carried out in Kiara Payung area, Sumedang, West Java, with Maesopsis eminii tree as the object study. A total of 29 ERT lines were measured using dipoledipole configuration with electrodes spacing of 50 cm. The results of two-dimensional (2D) and three-dimensional (3D) inversion modeling show that the ERT method has been successfully imaging the tree root zone. The root zone is characterized as 100-700 Ωm with an elliptical shape geometry of the root plate. The root radius is estimated to be 4-5 m from the stem, the root zone diameter reaches 8-9 m at the shallow soil surface and the root zone depth is approximately 2-2.5 m. ABSTRAK Pencitraan geometri zona perakaran pohon menggunakan electrical resistivity tomography. Penelitian geometri zona perakaran biasa dilakukan dengan cara konvensional yang destruktif, memakan waktu, dan membutuhkan biaya yang tidak sedikit. Beberapa pengukuran non-destruktif menggunakan metode geofisika telah dikembangkan, salah satunya adalah metode Electrical Resistivity Tomography (ERT). Penentuan zona perakaran pohon menggunakan metode ERT telah dilakukan di daerah Kiara Payung, Sumedang, Jawa Barat, dengan pohon Maesopsis eminii sebagai objek studi. Sebanyak 29 lintasan ERT diukur menggunakan konfigurasi dipole-dipole pada dengan jarak antar elektroda 50 cm. Hasil pemodelan inversi dua dimensi (2D) dan tiga dimensi (3D) menunjukkan bahwa metode ERT telah berhasil mencitrakan zona perakaran pohon. Zona perakaran teridentifikasi berada pada nilai resistivitas 100-700 Ωm dengan root plate dan root cross-sections berbentuk elips. Radius akar diperkirakan sejauh 4-5 m dari pangkal batang, sedangkan diameter zona perakaran mencapai sekitar 8-9 m di permukaan tanah dangkal dan kedalaman zona perakaran diperkirakan antara ~2-2.5 m.

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Niculescu, Bogdan Mihai, and Gina Andrei. "Application of electrical resistivity tomography for imaging seawater intrusion in a coastal aquifer." Acta Geophysica 69, no. 2 (January 21, 2021): 613–30. http://dx.doi.org/10.1007/s11600-020-00529-7.

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Ibraheem, Ismael M., Rainer Bergers, and Bülent Tezkan. "Archaeogeophysical exploration in Neuss‐Norf, Germany using electrical resistivity tomography and magnetic data." Near Surface Geophysics 19, no. 5 (August 13, 2021): 603–23. http://dx.doi.org/10.1002/nsg.12172.

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Sağır, Çağdaş, Bedri Kurtuluş, Pantelis Soupios, Korhan Ayrancı, Erkan Düztaş, Murat Ersen Aksoy, Özgür Avşar, et al. "Investigating the Structure of a Coastal Karstic Aquifer through the Hydrogeological Characterization of Springs Using Geophysical Methods and Field Investigation, Gökova Bay, SW Turkey." Water 12, no. 12 (November 28, 2020): 3343. http://dx.doi.org/10.3390/w12123343.

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The electrical resistivity tomography method has been widely used in geophysics for many purposes such as determining geological structures, water movement, saltwater intrusion, and tectonic regime modeling. Karstic springs are important for water basin management since the karst systems are highly complex and vulnerable to exploitation and contamination. An accurate geophysical model of the subsurface is needed to reveal the spring structure. In this study, several karst springs in the Gökova Bay (SW, Turkey) were investigated to create a 3D subsurface model of the nearby karstic cavities utilizing electrical resistivity measurements. For this approach, 2D resistivity profiles were acquired and interpreted. Stratigraphically, colluvium, conglomerate, and dolomitic-limestone units were located in the field. The resistivity values of these formations were determined considering both the literature and field survey. Then, 2D profiles were interpolated to create a 3D resistivity model of the study area. Medium-large sized cavities were identified as well as their locations relative to the springs. The measured resistivities were also correlated with the corresponding geological units. The results were then used to construct a 3D model that aids to reveal the cavity geometry in the subsurface. Additionally, several faults are detected and their effect on the cavities is interpreted.

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Barde-Cabusson, Stéphanie, Xavier Bolós, Dario Pedrazzi, Raul Lovera, Guillem Serra, Joan Martí, and Albert Casas. "Electrical resistivity tomography revealing the internal structure of monogenetic volcanoes." Geophysical Research Letters 40, no. 11 (June 3, 2013): 2544–49. http://dx.doi.org/10.1002/grl.50538.

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Skibbe, Nico, Thomas Günther, and Mike Müller-Petke. "Improved hydrogeophysical imaging by structural coupling of 2D magnetic resonance and electrical resistivity tomography." GEOPHYSICS 86, no. 5 (June 17, 2021): WB135—WB146. http://dx.doi.org/10.1190/geo2020-0593.1.

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Describing hydraulic properties in the subsurface in at least two dimensions is one of the main objectives in hydrogeophysics. However, due to the limited resolution and ambiguity of the individual methods, those images are often blurry. We have developed a methodology to combine two measuring methods, magnetic resonance tomography (MRT) and electrical resistivity tomography (ERT). To this end, we extend a structurally coupled cooperative inversion scheme to three parameters. It results in clearer images of the three main parameters: water content, relaxation time, and electrical resistivity; thus, there is a less ambiguous hydrogeophysical interpretation. Synthetic models demonstrate its effectiveness and show how the parameters of the coupling equation affect the images and how they can be chosen. Furthermore, we examine the influence of resistivity structures on the MRT kernel function. We apply the method to a roll-along MRT data set and a detailed ERT profile. As a final result, a hydraulic conductivity image is produced. Known ground-penetrating radar reflectors act as the ground truth and demonstrate that the obtained images are improved by the structural coupling.

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Gorbach, Dmitriy, Valeriya Yakimenko, and Olga Konovalova. "Application of electric resistivity tomography for investigation of geological situation closed to railways." MATEC Web of Conferences 265 (2019): 03005. http://dx.doi.org/10.1051/matecconf/201926503005.

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The paper reviews methods of engineering geophysics which can be applied to sections of railway tracks. The method of electrical resistivity tomography is used to study the properties of the geological situation under an engineering structure. In the course of practical work, two-dimensional geoelectric sections were obtained. Interpretation of the sections allowed to understand the structure of the near-surface zone.

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Fernández-Martínez, Juan Luis, Zulima Fernández-Muñiz, Shan Xu, Ana Cernea, Colette Sirieix, and Joëlle Riss. "Efficient uncertainty analysis of the 3D electrical tomography inverse problem." GEOPHYSICS 84, no. 3 (May 1, 2019): E209—E223. http://dx.doi.org/10.1190/geo2017-0729.1.

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We have evaluated the uncertainty analysis of the 3D electrical tomography inverse problem using model reduction via singular-value decomposition and performed sampling of the nonlinear equivalence region via an explorative member of the particle swarm optimization (PSO) family. The procedure begins with the local inversion of the observed data to find a good resistivity model located in the nonlinear equivalence region. Then, the dimensionality is reduced via the spectral decomposition of the 3D geophysical model. Finally, the exploration of the uncertainty space is performed via an exploratory version of PSO (RR-PSO). This sampling methodology does not prejudge where the initial model comes from as long as this model has a geologic meaning. The 3D subsurface conductivity distribution is arranged as a 2D matrix by ordering the conductivity values contained in a given earth section as a column array and stacking parallel sections as columns of the matrix. There are three basic modes of ordering: mode 1 and mode 2, by using vertical sections in two perpendicular directions, and mode 3, by using horizontal sections. The spectral decomposition is then performed using these three 2D modes. Using this approach, it is possible to sample the uncertainty space of the 3D electrical resistivity inverse problem very efficiently. This methodology is intrinsically parallelizable and could be run for different initial models simultaneously. We found the application to a synthetic data set that is well-known in the literature related to this subject, obtaining a set of surviving geophysical models located in the nonlinear equivalence region that can be used to approximate numerically the posterior distribution of the geophysical model parameters (frequentist approach). Based on these models, it is possible to perform the probabilistic segmentation of the inverse solution found, meanwhile answering geophysical questions with its corresponding uncertainty assessment. This methodology has a general character could be applied to any other 3D nonlinear inverse problems by implementing their corresponding forward model.

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Santoso, Budy. "IDENTIFICATION OF AQUIFER USING RESISTIVITY GEOELECTRIC METHOD IN REGIONAL OF BEBANDEM, KARANG ASEM, BALI." EKSAKTA: Berkala Ilmiah Bidang MIPA 19, no. 1 (April 21, 2018): 24–34. http://dx.doi.org/10.24036/eksakta/vol19-iss1/101.

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Bungaya Kangin Village, Bebandem District, Karangasem Regency, Bali Province consists of paddy fields and settlements, required therefore a water source / aquifer that can meet all these needs. One of the Geophysical Methods that can identify the aquifer is the Geoelectric Method. Geoelectric method used in this research is Resistivity Method. Data acquisition using Vertical Electrical Sounding (VES) and Electrical Resistivity Tomography (ERT) Methods. VES method is a method of measurement to determine the variation of resistivity vertically at one point. Electrical Resistivity Tomography (ERT) method is a method of measuring resistivity on soil surface / rock by using many electrode (51 electrode), to obtain sub-surface resistivity variation lateraly and verticaly, to obtain sub-surface image. The equipment used for geoelectric measurements is Resistivity Meter of Naniura NRD 300 Hf which has been equipped with a switchbox to adjust the displacement of 51 electrodes. Based on the resistivity modeling results, the aquifers in the study area were found in rough sandstones with resistivity values : (49 - 100) Ohm.m.

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Hermans, Thomas, Andreas Kemna, and Frédéric Nguyen. "Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data." GEOPHYSICS 81, no. 5 (September 2016): E311—E322. http://dx.doi.org/10.1190/geo2015-0491.1.

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Hydrogeophysics has become a major field of research in the past two decades, and time-lapse electrical resistivity tomography (ERT) is one of the most popular techniques to monitor passive and active processes in shallow subsurface reservoirs. Time-lapse inversion schemes have been developed to refine inversion results, but they mostly still rely on a spatial regularization procedure based on the standard smoothness constraint. We have applied a covariance-based regularization operator to the time-lapse ERT inverse problem. We first evaluated the method for surface and crosshole ERT with two synthetic cases and compared the results with the smoothness-constrained inversion (SCI). These tests showed that the covariance-constrained inversion (CCI) better images the target in terms of shape and amplitude. Although more important in low-sensitivity zones, we have observed improvements everywhere in the tomograms. Those synthetic examples also show that an error made in the range or in the type of the variogram model had a limited impact on the resulting image, which still remained better than SCI. We then applied the method to cross-borehole ERT field data from a heat-tracing experiment, in which the comparison with direct measurements showed a strong improvement of the breakthrough curves retrieved from ERT. This method could be extended to the time dimension, which would allow the use of CCI in 4D inversion schemes.

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