2020-2026年电阻率法研究进展与前沿创新：正反演算法、测量装置、多方法联合反演与应用场景研究

… Electrical resistivity tomography (ERT) is widely used for permafrost investigations. However, conventional inversion … neural network (CNN) inversion framework designed to improve the …

Electrical resistivity tomography inversion often encounters uncertainty stemming from two primary sources: epistemic uncertainty, arising from imperfect underlying physics and improper initial approximation of model parameters, and aleatory variability in observations due to measurement errors. Despite the widespread application of electrical resistivity tomography in imaging the resistivity distribution of subsurface structures for various hydro-geophysical and engineering purposes, the assessment of uncertainty is seldom addressed within the inverted resistivity tomograms. To explore the combined impact of epistemic and aleatory uncertainty on resistivity models, we initially perturb the observed data using non-parametric block-wise bootstrap resampling with an optimal choice of the block size, generating different realizations of the field data. Subsequently, a geostatistical method is applied to stochastically generate a set of initial models for each bootstrapped dataset from the previous step. Finally, we employ a globally convergent homotopic continuation method on each bootstrapped dataset and initial model realization to explore the posterior resistivity models. Uncertainty information about the inversion results is provided through posterior statistical analysis. Our algorithm’s simplicity enables easy integration with existing gradient-based inversion methods, requiring only minor modifications. We demonstrate the versatility of our approach through its application to various synthetic and real electrical resistivity tomography experiments. The results reveal that this approach for quantifying uncertainty is straightforward to implement and computationally efficient.

The direct current (DC) resistivity method is extensively used to predict water‐inrush disasters in tunnel prospecting. However, during DC resistivity inversion, different initial models can significantly affect the inversion results, often resulting in convergence at a local optimum. To overcome these challenges, we propose a new method for DC data inversion that uses prior information as a reference model. First, the resistivity distribution of the surrounding rock mass was estimated based on detailed geological analysis. Next, an initial homogeneous resistivity model was constructed by averaging the observed tunnel resistivity values. Finally, the initial model was developed by incorporating borehole rock samples and water content data. The effectiveness of the method' was assessed using a series of synthetic models of typical water‐bearing structures. We then applied this approach to the Laomacao Tunnel in the Yunnan Central Water Diversion Project (southwestern China), where drilling data were used as a priori information to optimize the initial model together with the average tunnel resistivity values, successfully identifying the water‐bearing structure ahead of the tunnel face. Overall, the proposed method enhances the understanding of sudden surges, aiding in the prevention and control of water disasters in tunnels.

Geophysical methods, such as electrical resistivity tomography (ERT), can be used to imaging the near-surface electrical resistivity as field measurements depend on the subsurface porosity, water saturation and fluid salinity. ERT has been widely applied to investigate mineral and groundwater resources, and in archaeological, environmental, and engineering studies. The prediction of subsurface electrical conductivity from ERT data requires solving a geophysical inverse problem. For near-surface characterization studies, this is often accomplished with deterministic inverse methods. These methods linearize the problem around an initial solution, and their smoothness depends on an imposed a priori spatial regularization term. Depending on this parameterization, these methodologies might struggle to capture the natural variability of the subsurface. Moreover, deterministic solutions have limited capabilities for uncertainty assessment. On the contrary, stochastic inverse methods can assess uncertainties by predicting multiple model realizations that fit similarly the recorded ERT data. However, they are often more computationally expensive than deterministic solutions. Deep learning algorithms based on deep generative models have been used to re-parametrize model and data spaces into low-dimensional domains and efficiently solve geophysical inverse problems. However, within this context, uncertainty assessment is challenging. We propose a deep convolutional variational autoencoder (VAE) coupled with stochastic adaptive optimization to perform stochastic ERT inversion. Geostatistical simulations of electrical resistivity are used as training data set of the VAE. After training, the VAE generates electrical resistivity models that reproduce the statistics and spatial continuity patterns of the training data set. Then, the VAE latent space is iteratively perturbed and updated with adaptive stochastic sampling based on the misfit between observed and predicted ERT data. The proposed methodology is illustrated in two-dimensional synthetic and real data sets to illustrate the ability of the proposed method to predict reliable electrical resistivity models while generating multiple possible scenarios for uncertainty assessment.

… using the electrical resistivity tomography technique. To attain this goal, we employed two distinct methodologies for inversion. The first approach used the conventional isotropic …

… , such as resistivity estimated from inversion of electrical resistivity tomography (ERT) data. The predicted … Ghanati, and CL Berube, 2024, Uncertainty quantification in electrical resistivity …

… individual resistivity and velocity models for all deduced measurements, ERT and SRT datasets were cooperatively inverted using the Structurally Coupled Cooperative Inversion (SCCI)…

In electrical resistivity tomography (ERT), the anomaly effects of different electrode arrays vary depending on the geological model. The appropriate combination of different electrode arrays can optimize detection performance and enhance the reliability of interpretation results. However, traditional inversion methods, constrained by single-array data, sparse observations, and ill-posed problem-solving, often yield low-resolution or inaccurate results. To address the resolution challenges in ERT inversion, inspired by the outstanding fusion and nonlinear mapping capabilities of multimodal deep learning (DL) image methods, we propose the super-resolution ERT fusion network (SRERTF-Net), which utilizes traditional inversion results of multiarray as the initial models, efficiently leveraging and integrating prior physical information to achieve multiarray data joint super-resolution inversion. In SRERTF-Net, different downsampling paths are employed to process the inversion results of various electrode arrays, while Inception modules are introduced to enhance feature extraction. In addition, dense connections are implemented both within and across paths to effectively integrate complementary information from different arrays, ensuring robust multimodal feature fusion. Finally, we designed training samples that include randomly generated typical structural models and comprehensive complex models, in order to enhance the practicality and adaptability of the network. Experiments on synthetic and field-measured data indicate that SRERTF-Net outperforms other methods in terms of resistivity accuracy, resolution, and background performance.

Electrical resistivity tomography (ERT) is a widely used and effective tool for hydrogeological investigations. Conventional ERT inversion approaches are based on gradient-based algorithms, which typically provide deterministic optimal solutions, which are subject to uncertainty. Such uncertainty could have significant impact on hydrogeological interpretation using ERT. Model appraisal is a critical step after inversion, however, conventional appraisal methods are qualitative and thus subjective. To address these limitations, this study introduces a probabilistic variational inference (VI) method, referred to as Stein variational gradient descent (SVGD), to quantify both resistivity distributions and associated uncertainties in ERT inversions. Synthetic examples are conducted to investigate the effects of configurations and noise, and to compare the performance of SVGD with conventional inversion and model appraisal techniques. A field case study and its model validation are also presented to demonstrate the practical advantages of uncertainty quantification in field. The results indicate that SVGD can effectively reduce artifacts introduced by regularization and provide more comprehensive quantitative insights into subsurface structures compared to conventional approaches. The study also reveals limitations in the interpretation of basic statistics of uncertainty estimates, highlighting the need to examine the entire posterior distributions of parameter values. Additionally, this study demonstrates that the final uncertainty arises from a trade-off among multiple factors, such as geometry of subsurface structures, measurement techniques and data noise levels. Finally, we also discuss some comparisons with other probabilistic frameworks in hydrogeophysics, highlighting its potential to improve uncertainty and probability quantification in ERT, and possible future developments in hydrogeophysical coupled inversion.

Electrical Resistivity Tomography (ERT) is widely used for subsurface imaging. Despite recent advancements in machine learning based ERT inversion using conventional deep learning models such as CNN and UNet, accurately delineating resistivity profiles of natural and artificial subsurface anomalies remains challenging. These challenges stem from multi‐scale complex geological heterogeneity, spatial resolution limitations, and the ill‐posed nature of ERT inversion. This study proposes a new hybrid image‐based ERT inversion method leveraging a Vision Transformer (ViT) model incorporating convolutional blocks to map observed pseudo sections of subsurface apparent resistivity to true resistivity distributions. This novel approach is compared to others including CNN AutoEncoder, UNet, and Latent Diffusion. A synthetic data set generated using forward modeling with varying conductive/resistive heterogeneity zone, facilitates model training using a curriculum strategy to improve generalization across complexity levels. To address the absence of true resistivity data for field observations, a residual CNN model is employed to calibrate resistivity colormap ranges of ERT imaging to ensure consistency in resistivity values represented in both input and output images of the ViT model, maximizing inversion accuracy and result interpretation in applications. The proposed method demonstrated superior performance over frequently used comparative models, as the adopted ViT model excels in inversion accuracy, computational efficiency, adaptability to heterogeneity, and robustness to measurement noise. Inversion results showed significant advantages over traditional Gauss‐Newton numerical inversion, which achieved high fidelity in rapidly resolving subsurface anomalies with sharp boundaries and accurately mapped resistivity distributions with high resolution, presenting a promising data‐driven approach for large‐scale ERT imaging applications.

… This study employed the Geostatistical Electrical Resistivity Tomography (GERT) to examine the spatial distribution of mass water content (ω s ) in loess strata to understand the …

The Ningdong coalfield has played a pivotal role in advancing local economic development and meeting national energy. Nevertheless, mining operations have engendered ecological challenges encompassing subterranean water depletion, land desertification, and ground subsidence, primarily stemming from the disruption of coal seam roof strata. Consequently, the local ecosystem has incurred substantial harm. Water-preserved coal mining presently constitutes the pivotal technology in mitigating this problem. The primary challenge of this technique lies in identifying critical aquifer layers and understanding the heights of water-conducting fracture zones. To obtain a precise comprehension of the seepage patterns within the upper coal seam aquifer during mining, delineate the extent of water-conducting fracture zones, non-invasive geophysical techniques such as time-lapse electrical resistivity tomography (TL-ERT), magnetic resonance sounding (MRS), and spontaneous potential (SP) have been employed to monitor alterations within the shallow coalfield’s aquifer throughout the mining process in the Ningdong coalfield. By conducting meticulous examinations of fluctuations in resistivity, moisture content, and self-potential within the superjacent strata during coal seam extraction, the predominant underground water infiltration strata were ascertained, concurrently enabling the estimation of the development elevation of water-conducting fracture zones. This outcome furnishes a geophysical underpinning for endeavors concerning local water-preserved coal mining and ecological rehabilitation.

The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

… to tomography methods. Our study introduces a cost-effective framework to enhance … electrical resistivity tomography (GERT) under sparse observations by integrating electrical …

… -based variational inference approach for Electrical Resistivity Tomography (ERT) using the … dimensionality of the inverse problem and computational cost, we perform the inversion in a …

Groundwater serves as a vital water resource for human society, yet it also plays a significant role in geological- and engineering-related hazards, such as landslides, tunnel collapses, and mining-related issues. Detecting water-rich zones and groundwater seepage pathways is essential for mitigating these risks. The Xiaogangou Coal Mine, located in a low- to mid-mountainous region at the northern foot of the Tianshan Mountains in Xinjiang, China, contains multiple coal seams distributed at a depth of approximately 600 m. Surface infiltration from two rivers in the area has resulted in water-rich zones within the medium to coarse sandstone layers between these coal seams, posing a potential threat to mining operations and construction activities. In this study, geophysical methods, including transient electromagnetic surveys, electrical resistivity tomography, and self-potential measurements, were employed to investigate the extent of these water-rich zones and identify primary infiltration pathways. The transient electromagnetic data facilitated the construction of a three-dimensional geoelectric model of the mine, from which the planar distribution of resistivity in the medium to coarse sandstone layers—likely reservoirs of groundwater—was derived. Combining low-resistivity anomaly zones with geological and drilling data allowed for the delineation of water-rich areas. Additionally, two self-potential profiles along the rivers were used to map surface electric potential distributions, which, in conjunction with two-dimensional resistivity data from overlapping electrical resistivity tomography profiles, revealed the main infiltration points and seepage channels. The results from the three geophysical techniques corroborated one another, delineating the extent of the aquifer and demonstrating that the rivers recharge the groundwater through rock weathering and structural fractures. The subsequent post-processing of these detection results facilitated the construction of a comprehensive three-dimensional model of the groundwater system. This study highlights the efficacy of geoelectric methods in detecting water-rich zones and infiltration pathways in complex hydrogeological settings.

… to DC resistivity data acquired in mountain permafrost areas. A key feature of mountain permafrost is its potentially high resistivity, … Imaging of industrial waste deposits and buried quarry …

… The objective of the study was to evaluate the resolution of resistivity techniques in … other locations throughout our geophysical survey program. Resistivity measurements were carried …

… This paper includes a short introduction to DC resistivity imaging and attempts to provide … geophysical or other parameters, for example DC resistivity and inductive EM data or resistivity …

Abstract With permafrost thawing and changes in active layer dynamics induced by climate change, interactions between biogeochemical and thermal processes in the ground are of great importance. Here, active layer dynamics have been monitored using direct current (DC) resistivity and induced polarization (IP) measurements at high temporal resolution and at a relatively large scale at a heath tundra site on Disko Island on the west coast of Greenland (69°N). At the field site, the active layer is disconnected from the deeper permafrost, due to isothermal springs in the region. Borehole sediment characteristics and subsurface temperatures supplemented the DC-IP measurements. A time-lapse DC-IP monitoring system has been acquiring at least six datasets per day on a 42-electrode profile with 0.5 m electrode spacing since July 2013. Remote control of the data acquisition system enables interactive adaptation of the measurement schedule, which is critically important to acquire data in the winter months, where extremely high contact resistances increase the demands on the resistivity meter. Data acquired during the freezing period of October 2013 to February 2014 clearly image the soil freezing as a strong increase in resistivity. While the freezing horizon generally moves deeper with time, some variations in the freezing depth are observed along the profile. Comparison with depth-specific soil temperature indicates an exponential relationship between resistivity and below-freezing temperature. Time-lapse inversions of the full-decay IP data indicate a decrease of normalized chargeability with freezing of the ground, which is the result of a decrease in the total unfrozen water and of the higher ion concentration in the pore-water. We conclude that DC-IP time-lapse measurements can non-intrusively and reliably image freezing patterns and their lateral variation on a 10–100 m scale that is difficult to sample by point measurements. In combination with laboratory experiments, the different patterns in resistivity and chargeability changes will enable the disentanglement of processes (e.g., fluid migration and freezing, advective and diffusive heat transport) occurring during freezing of the ground. The technology can be expanded to three dimensions and also to larger scale.

… DC resistivity and induced polarization (DC-IP) imaging can … is to demonstrate the value of DC-IP imaging, in both 3D and … Underwater 3D DC-IP surveying was then conducted across …

Abstract Tunnel Boring Machines (TBMs) are used for tunneling and underground construction, by excavating material and subsequently installing a segmental concrete tunnel liner for support. However, unknown ground conditions pose a significant risk to tunneling operations and any damage to the machine can be disastrous to a project. There is a need for tools which look ahead of the TBM for potential hazards during tunneling, such as water saturated zones, faults, boulders and metal pipes. Geophysical methods offer the capability to image unexcavated material in order to avoid such hazards and thus improve tunneling operations. In particular, the DC resistivity method is useful because it is sensitive to a large range of conductivity variations in geological and man-made materials. The research presented in our paper consists of three parts: (1) a laboratory study of a DC resistivity system mounted on a scale model TBM within a simulated tunneling environment, (2) a series of forward models studying different DC resistivity survey designs, and (3) the inversion and imaging of synthetic DC resistivity data under different constraints. We introduce several new survey designs that attach electrodes on a probe (or probes), which are then pushed into the earth in front of the machine each time excavation stops. Our laboratory data and forward modeling results show that using probes reduces interference caused by the metallic TBM body, and increases the distance ahead of the machine at which a target may be detected. The TBM influence on the data is significantly reduced once the probe is pushed 25% of the TBM diameter ahead of the machine and negligible once the probes are pushed 50% ahead of the machine. Depending on the specific survey design, targets can be detected from up to 70% of the TBM diameter away. Finally, we invert synthetic data to produce ahead-of-tunneling images using different amounts of prior information (e.g. TBM geometry and host resistivity) and also study time-lapse inversion. Numerical results show the target can be imaged with these methods from distances up to 45% TBM diameter.

… technique and its applicability to the resistivity problem, is investigated. Several … resistivity estimation of both 1D and 2D DC resistivity data. The main advantage of using NN for resistivity …

… On the other hand, surface geophysical measurements using electric, electromagnetic and … geophysical monitoring approach using the direct current (DC) electrical resistivity technique …

… of science, such as geophysics. In geophysics, the inversion of 2D DC resistivity imaging data is complex due to its non-linearity, especially for high resistivity contrast regions. In this …

A VLF and DC resistivity investigation was conducted in the Kestelek area, … resistivity images were obtained by the inversion of tipper and resistivity data for VLF and DC resistivity …

Image-guided inversion is an efficient approach to utilize secondary data as soft constraint. It extracts structure information from a secondary image (e.g. seismic/GPR/geological cross-section) and uses it to constrain the inversion, so that the predicted model will honor the subsurface structure. More importantly, we can have a better estimation of the model, which can be used in turn for a better petrophysical interpretation. The structure features are quantified and represented by a tensor field calculated from the image. The structural information is added to the inversion during the construction of the inverse of model covariance.

… Magnetotelluric (MT) and Schlumberger (DC) surveys were … Menderes Graben with a total of 18 MT and 19 DC stations. … regions with very low resistivity (< 3 Ω m) are imaged at depth of …

… Electrical and electromagnetic geophysical methods are especially well-… DC resistivity models tend to smooth out small-scale heterogeneity and may underestimate the depth of resistive …

… Among all geophysical techniques dedicated to image the near surface, 2D or 3D resistivity surveying has been increasingly used for environmental, engineering and geological …

… and resistivity, respectively, DC resistivity soundings constitute one of the traditional geophysical methods … The increase of application of modern geophysical methods also in mountain …

… of geophysical monitoring data, such as DC resistivity or … using synthetic data from DC resistivity tomography surveys. In … algorithm to cross-well resistivity monitoring data acquired by a …

The characterisation of subsurface electrical resistivity is a fundamental requirement for geoscientific and engineering applications, including groundwater exploration and structural assessments. This study examines the sequential cooperative inversion of direct current resistivity and frequency-domain electromagnetic data and compares the results to the inverse models obtained from separate (individual) inversions of the datasets. The proposed cooperative framework is applied to both synthetic datasets generated through forward modelling and field data acquired at the Morgenzon Farm site, South Africa, to delineate a dolerite dyke of hydrogeological significance. Individual inversions identified distinct features but exhibit limitations: direct current resistivity highlights a two-layered medium with minor anomalies, while frequency-domain electromagnetic data identify a resistive anomaly. In contrast, the sequential cooperative inversion approach, which uses the output of one dataset to constrain the other, provides improved subsurface imaging results, reduces ambiguity, and enables the integration of complementary information from both methods. The results indicate that resistivity models constrained by inverse frequency-domain electromagnetic data provide improved representation of subsurface geometry and amplitude compared to individual approaches. These findings support the use of a non-destructive testing approach for improved subsurface imaging, facilitating better-informed decision-making in infrastructure projects and resource management

Abstract The fractured aquifers have complicated physical and hydrogeological properties due to fractures and their geometries. Therefore, studying of these aquifers is not easy. Accordingly, it focused at this study on the electrical conductivity of the saturated fractures as conductive zones and the role of these zones in changing and reducing the host rock resistivity to estimate the hydro-electrical characteristics of these aquifers. So that, the 3D numerical modeling (Comsol Multiphysics Model, CMM) was developed for simulating the resistivity and hydro-electrical properties of these aquifers by applying the Vertical Electrical Sounding technique (VESs). The calibration between the analytical solution and numerical model was carried out and shown the percentage error of less than 0.03%. The resulted VESs-curves depending on porosity increasing, which it depends on increasing in fractures density, distribution and their orientation, confirmed that the resistivity of fractured aquifer decreases with increasing the fractures density and porosities. This decreasing was clear in changing of the resistivity values of the VESs-curves. The results were in excellent agreement with the expected logical results. The relationship between the porosity and resistivity was linear and inversely, the evidence and correlation were robust, and also the conductivity of these aquifers increases with increasing fracture density and porosity. Therefore, it can use the resulted empirical equation in calculating the porosity with taking into account the saturated weathered and fractured basement rocks resistivity is ranged from 7 to 85 Ω.m and the expected percentages of errors ranges from 0.0015%–0.0668%. Accordingly, this model produces reliable solutions of the resistivity method forward problem for arbitrary electrical resistivity in the subsurface depending on the fractures density and porosity of fractured aquifers. The apparent resistivity curve from this model was compared with the measured field apparent resistivity curve of the fractured aquifer. So, it can use Comsol Multiphysics Model successfully in studying, solving, and understanding the different electrical and hydrogeological conditions of these aquifers and the other aquifers.

Studying and determining the physical properties and hydraulic parameters of vadose zone sediments is an important key to evaluate the infiltration rate into them and assessing the extent to which aquifer sediments benefit from rainwater harvesting in arid and semi-arid areas. Due to the lack of sufficient data on the characteristics of this zone depths, a numerical modeling was used to simulate the electrical resistivity of these sediments by applying the electrical resistivity method, because it is the most affected by the physical properties of dry and wet sediments. This study was applied as a proposal for application in northwestern KSA to calculate the vertical hydraulic conductivity and transmissivity for the vadose zone. This was implemented by assuming a three-layer model using COMSOL Multiphysics model with different electrical resistivity values depending on some in situ electrical resistivity measurements for shallow depths. Hence, the infiltration rate of sediments in this area can be predicted with depth and its effect on aquifer recharge. The focus was on calculating the vertical hydraulic parameters of the most widespread surface sediments with depth and comparing the results of calculating these parameters for some sediments laboratory-wise to ensure their accuracy. Then, their infiltration rate was inferred separately with depth, predicting their ability to aquifer recharge and make the most of rainwater harvesting. Finally, this study can be considered as a preliminary study to determine the expected forward model of electrical resistivity and hydraulic parameters values for the vadose zone sediments with depth along the area and in any other areas, and then apply them accurately in situ to estimate the extent of its usefulness in rainwater harvesting, especially aquifer recharge.

A numerical technique has been developed to solve the three-dimensional (3-D) potential distribution about a point source of current located in or on the surface of a half-space containing an arbitrary 3-D conductivity distribution. Self-adjoint difference equations are obtained for Poisson's equation using finite-difference approximations in conjunction with an elemental volume discretization of the lower half-space. Potential distribution at all points in the set defining the subsurface are simultaneously solved for multiple point sources of current. Accurate and stable solutions are obtained using full, banded, Cholesky decomposition of the capacitance matrix as well as the recently developed incomplete Cholesky-conjugate gradient iterative method.A comparison of the 2-D and 3-D simple block-shaped models, for the collinear dipole-dipole array, indicates substantially lower anomaly indices for inhomogeneities of finite strike-extent. In general, the strike-extents of inhomogeneities have to be approximately 10 times the dipole lengths before the response becomes 2-D. The saturation effect with increasing conductivity contrasts appears sooner for the 3-D conductive inhomogeneities than for corresponding models with infinite strike-lengths.A downhole-to-surface configuration of electrodes produces diagnostic total field apparent resistivity maps for 3-D buried inhomogeneities. Experiments with various lateral and depth locations of the current pole indicate that mise-a-la-masse surveys give the largest anomaly if a current pole is located asymmetrically and, preferably, near the top surface of the buried conductor.

Fractured-rock aquifers represent an important part of the groundwater that is used for domestic, agricultural, and industrial purposes. In these natural systems, the presence and properties of fractures control both the quantity and quality of water extracted, meaning that knowledge about the fractures is critical for effective water resource management. Here, we explore through numerical modeling whether electrical resistivity (ER) geophysical measurements, acquired from the Earth’s surface, may potentially be used to identify and provide information about shallow bedrock fractures. To this end, we conduct a systematic numerical modeling study whereby we evaluate the effect of a single buried fracture on ER-profiling data, examining how the corresponding anomaly changes as a function of the fracture and domain characteristics. Two standard electrode configurations, the Wenner-Schlumberger (WS) and dipole-dipole (DD) arrays, are considered in our analysis, with three different spacing factors. Depending on the considered electrode array, we find that the fracture dip angle and length will impact the resistivity anomaly curves differently, with the WS array being better adapted for distinguishing between sub-horizontal and sub-vertical fractures, but the DD array leading to larger overall anomaly magnitudes. We also find that, unsurprisingly, the magnitude of the resistivity anomaly, and thus fracture detectability, is strongly affected by the depth of overburden and its electrical resistivity, as well as the fracture aperture and contrast between the fracture and bedrock resistivities. Further research into the electrical properties of fractures, both above and below the water table, is deemed necessary.

… Numerical modeling of an orthogonal set of 2-D apparent resistivity data acquired in an … configuration was used to create models of subsurface resistivity which converges with the field …

Cavity due to underground old mine workings is one of the major threats to the coal mines and the overlying subsurface and surface properties, which need to be protected. The detection of old mine workings and stability assessment of overlying strata are common problems in most of the Indian coalfields. Several coal mines in India are loss‐making, mainly due to different types of mine hazards. Khandra mine is one such mine at Raniganj Coalfield, Eastern Coalfields Ltd., a subsidiary of Coal India Limited. In the present study, 2‐dimensional and 3‐dimensional electrical resistivity tomography were carried out for detailed subsurface characterization. It supports delineating underground workings, including the nature of voids/cavities (air or water‐filled). Excessive distortions were reported in electrical resistivity tomography application, especially at the near‐surface, owing to large resistivity variations. Refinement of the model by half‐unit electrode spacing was attempted here to reduce the distortions with minimum possible absolute errors. 3‐Dimensional resistivity volumetric model was also developed with the help of five electrical resistivity tomography parallel profiles for better apprehension of the subsurface. Analysis provided important inputs for stability analysis using 3‐dimensional numerical modelling. The physico‐mechanical properties of the overlying strata, pre‐excavation in situ stresses, boundary conditions and the mine geometry simulation were incorporated for understanding the stability analysis. Stability analysis was carried out using the finite difference technique. The analysis of 3‐dimensional numerical modelling indicated that two distinct layers comprising (i) laterite/part of the course to medium‐grained sandstone and (ii) developed galleries of R‐IX seam exhibited a very low safety factor below 1.0, indicating potholing/subsidence susceptibility. The other three layers comprising parts of fine‐grained sandstone exhibited a relatively higher safety factor of around 2.0, indicating moderately stable zones, but not on a long‐term basis. Parts of Siduli stream embankments need suitable retaining walls to avoid water inundation for the stability of the area.

We constructed an apparent geological model with resistivity data from surface resistivity surveys. We developed a data fusion approach by integrating dense electrical resistivity measurements collected with Schlumberger arrays and wellbore logs. This approach includes an optimization algorithm and a geostatistic interpolation method. We first generated an apparent formation factor model from the surface resistivity measurements and groundwater resistivity records with an inverse distance method. We then converted the model into a geology model with the optimized judgment criteria from the algorithms relating the apparent formation factors to the borehole geology. We also employed a non‐parametric bootstrap method to analyze the uncertainty of the predicted sediment types, and the predictive uncertainties of clay, gravel, and sand were less than 5%. Overall, our model is capable of capturing the spatial features of the sediment types. More importantly, this approach can be arranged in a self‐updated sequence to enable adjustments to the model to accommodate newly collected core records or geophysical data. This approach yields a more detailed apparent geological model for use in future groundwater simulations, which is of benefit to multi‐discipline studies.

… evaluation of apparent resistivity curves implies the use of three-dimensional simulation models which … Numerical experiments on resistivity modeling indicate that the size of the …

… Therefore, numerical modelling of 2D resistivity imaging was done in order to gain a better … there is a reasonable agreement between apparent resistivities and the modelled structure, …

… Figure 6 shows the comparison of apparent resistivity of three methods for the second model. The proposed algorithm is consistent with the other two methods with a high coincidence …

… To complement the information content of standard dc resistivity datasets in a cost-… data acquisition method: real-time experimental design. Implementation of this new acquisition …

… survey one simply measures the soil-and feature-associated magnetic fields as they appear on the site surface. Resistivity surveys … Acquisition of low-quality data is probably the single …

… resistivity surveys, we expose the theory and the basic principles of the method, we overview the variation of electrical resistivity as a … surveys, and explain the basic principles of the data …

… We have developed a simple, flexible automated resistivity data acquisition and analysis (ARDAA) system that can be adapted to any resistivity terrameter with four-electrode output and …

… 3D resistivity surveys with a moderate number (25 to 100) of electrodes and the computing time required to interpret the data have been developed. The electrodes in a 3D survey are …

… In the present work, a large 3D acquisition of direct‐current (DC) resistivity and time‐domain induced polarisation (DCIP) was performed to investigate the suburban environment of …

… data acquisition and inversion. The focus of this paper is on inversion of electrical resistivity data from surveys … The following section provides a description of a resistivity data modelling …

The electromagnetic (EM) azimuthal resistivity method is an alternative to the galvanic azimuthal resistivity survey. The advantages of the EM approach include (1) reduced acquisition time, (2) simple field procedure, and (3) a reduced data acquisition area. The primary disadvantage of the EM method is the magnitude of the data noise. Signal processing was applied to assist quantification of noise in EM azimuthal resistivity datasets and to enable noise reduction. Comparison of the energy in the even and odd coefficients of the power spectra allowed the signal-to-noise ratio to be identified. Linear phase filters were used to suppress high frequency noise. These techniques were applied to EM data collected at three study sites. Following data processing, the EM azimuthal resistivity datasets revealed apparent resistivity lobes consistent with the orientation of fracture strike mapped at two of these sites. At one site the processed EM dataset correlated closely with an azimuthal resistivity dataset collected at the same location. Scale modeling of the EM azimuthal resistivity response over anisotropic and heterogeneous features was performed. The EM response obtained from a symmetric and asymmetric array configuration was measured. The results illustrate that an asymmetric array must be used if the presence of heterogeneity is to be distinguished from anisotropy. Signal processing was applied to assist interpretation of anisotropy and heterogeneity. The magnitude of the energy in the odd coefficients of the power spectrum assists recognition of heterogeneity. For an anisotropic medium the odd coefficients were all zero whereas heterogeneity resulted in significant energy in these coefficients. The ability to (1) characterize and remove noise, and (2) differentiate heterogeneity from anisotropy, improves the value of the EM azimuthal resistivity technique in site characterization. The survey design procedures and data processing applied in this study are equally applicable to the azimuthal resistivity method.

… The capability that this provides for flexible and remotely configurable data acquisition is ideal for testing optimized survey designs without needing to manually revisit the site. …

… system of data acquisition and inversion process for earth dc resistivity survey in smart and … The objective of this research to design an integrated system of data acquisition tool and data …

… an automatic data acquisition system for all the data‐points … In recent years, we used our automatic DC data acquisition … as receiver, and collected resistivity data successively in normal …

Monitoring subsurface flow and transport processes over a wide range of spatiotemporal scales remains one of the greatest challenges in hydrology. Electrical geophysical techniques have been implemented to noninvasively investigate a broad range of subsurface hydrological processes. Recent advances in instrumentation and interpretational tools highlight the emerging opportunities to adopt long‐term resistivity monitoring (LTRM) to improve understanding of flow and transport processes operating over monthly to decadal timescales that are not adequately captured in short‐term monitoring data sets and are temporally aliased in data sets constructed from occasional reoccupation of a study site. The emergence of LTRM as a robust tool in hydrology represents a paradigm shift in geophysical data acquisition and analysis, with resistivity monitoring now evolving into a hydrological decision support technology. We describe the theoretical basis for adopting LTRM for noninvasive monitoring of hydrological state variables over multiple spatial scales and with higher temporal resolution than achieved from periodic reoccupation of a field site. Instrumentation developments facilitating autonomous data acquisition at off the grid field sites are discussed, along with advances in data processing that enhance the hydrological information content inherent in LTRM data sets. Case studies from a diverse range of hydrology subdisciplines highlight the largely untapped potential for LTRM to provide information beyond the reach of established hydrology tools. Future opportunities and challenges relating to the more widespread adoption of LTRM, including addressing inherent uncertainty in resistivity interpretation, upscaling, computational, and modeling needs are critically discussed.

Electrical resistivity tomography (ERT) is one of the most popular methods in geological exploration. When reconstructing the 3-D resistivity model directly from the apparent resistivity data, it has two main challenges: first, the apparent resistivity data obtained from the survey lines are limited, which is much less than the true model parameters. Second, the sensitivity of the data to the model has uneven spatial distribution. In this article, a novel deep learning algorithm is proposed to reconstruct a 3-D resistivity model directly from apparent resistivity data. The new resistivity inversion deep neural network (DNN) is based on neighborhood feature extraction. By using the limited observational apparent resistivity data profiles, the neighborhood features are extracted through a fully connected network to provide the augmented data so that the spatial correspondence between the input apparent resistivity data and the output resistivity model can be enhanced. A 3-D U-Net convolutional neural network is used to learn the attribute information feature relationship spatially aligned with the resistivity model from these augmented data. After that, the 3-D resistivity model is reconstructed. It is worth to point out that, a depth distance weighting constraint is added into the loss function to balance the sensitivity distribution of the different apparent resistivity data profiles and to improve the imaging effect between apparent resistivity data profiles and areas that far away from these data profiles. Finally, the effectiveness and reliability of the newly proposed DNN are verified through numerical simulations and field tests.

Geophysical inversion attempts to estimate the distribution of physical properties in the Earth's interior from observations collected at or above the surface. Inverse problems are commonly posed as least-squares optimization problems in high-dimensional parameter spaces. Existing approaches are largely based on deterministic gradient-based methods, which are limited by nonlinearity and nonuniqueness of the inverse problem. Probabilistic inversion methods, despite their great potential in uncertainty quantification, still remain a formidable computational task. In this paper, I explore the potential of deep learning methods for electromagnetic inversion. This approach does not require calculation of the gradient and provides results instantaneously. Deep neural networks based on fully convolutional architecture are trained on large synthetic datasets obtained by full 3-D simulations. The performance of the method is demonstrated on models of strong practical relevance representing an onshore controlled source electromagnetic CO2 monitoring scenario. The pre-trained networks can reliably estimate the position and lateral dimensions of the anomalies, as well as their resistivity properties. Several fully convolutional network architectures are compared in terms of their accuracy, generalization, and cost of training. Examples with different survey geometry and noise levels confirm the feasibility of the deep learning inversion, opening the possibility to estimate the subsurface resistivity distribution in real time.

Recently, deep learning-based electrical resistivity inversion (DL-ERI) became popular for its potential to achieve high-accuracy imaging of the subsurface's electrical properties. The most typical way is to directly learn the mapping from apparent resistivity data to the resistivity model with DNN. Therefore, they are doing cross-domain mapping, which usually causes learning difficulty. In this work, we propose to do DL-ERI by one-sided domain mapping that uses gradients domain as the input and target. Specifically, the target is represented as residuals between the resistivity model and a uniform initial model, while input is the gradients calculated from the first step of the traditional linear inversion method. Then, we could obtain the inversion results by adding the predicted target to the initial model. From experiments, our one-sided domain mapping method shows clear superiority over the cross-domain mapping baseline and demonstrates promising performance on physical experimental data.

Inversion is a fundamental step in magnetotelluric (MT) data routine analysis to retrieve a subsurface geoelectrical model that can be used to inform geological interpretations. To reduce the effect of non-uniqueness and local minimum trapping problems and improve calculation speeds, a data-driven mathematical method with a deep neural network was developed to estimate the subsurface resistivity. In this study, a deep learning (DL) inversion technique using a revised multi-head convolutional neural network (CNN) architecture was investigated for MT data analysis. We created synthetic datasets consisting of 100,000 random samples of resistivity layers to train the network's parameters. The trained model was validated with independent noised datasets, and the predicted results displayed reasonable accuracy and reliability, which demonstrates the potential application of DL inversion for real-world MT data. The trained model was used to analyze MT data collected in the southwestern Athabasca Basin, Canada. The calculated results from the DL method displayed a detailed subsurface resistivity distribution compared to traditional iterative inversion. Since this approach can predict a resistivity model without multiple forward modeling operations after the CNN model is created, this framework is suitable to speed up the computation of multidimensional MT inversion for subsurface resistivity.

Incorporating multiple perspectives makes joint inversion of multiple geophysical data sets an effective way for improving the accuracy of imaging complex geological structures. In this article, drawing inspiration from the inherent nonlinear mapping abilities of deep learning (DL), we introduce a groundbreaking joint inversion framework and network named JointInvNet. Unlike end-to-end networks that directly map geophysical data to models, we propose a hybrid inversion framework that combines insights from the physical laws with data-driven learning, which iteratively updates the independently inverted results simultaneously via JointInvNet. In particular, it is assumed that different geophysical parameters change on both sides of the geological boundary, and the Laplace convolution operator is used to extract boundary information and provide structural constraints for the loss function. To demonstrate the advantages over traditional separate inversion and cross-gradient inversion, numerical experiments are performed on seismic and resistivity data. As illustrated by visual and quantitative comparisons, JointInvNet could lead to satisfactory inversion results, with excellent agreement with ground-truth models and good generalization ability to more complex models. Moreover, weight settings between seismic and resistivity model parameters and applicability when structural similarity assumptions do not hold are discussed to illustrate the potential of the proposed method.

… This paper proposes the tunnel 3D resistivity inversion network … Further, we establish a 3D resistivity inversion dataset of … of the tunnel 3D resistivity deep learning inversion method is …

… a novel deep learning-based electrical method, which jointly inverses resistivity and … from input data, two encoders to output resistivity and chargeability models, respectively, and an …

Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modelling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non‐linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modelling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: A Markov chain Monte Carlo algorithm is applied to synthetic data, while an ensemble‐based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator that is three orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields the most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modelling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources.

The traditional electrical resistivity tomography (ERT) inversion methods typically produce low-resolution imaging results due to the nonlinear and bulk effect of 2-D inversion. In this letter, we propose to directly establish the mapping from the geoelectric model of traditional inversion results (input) to the actual geoelectric models (output) through the fully convolutional networks (FCNs), inspired by the robust nonlinear mapping capabilities of deep learning methods. We designed an ERT resolution enhancement network (ERTReNet) based on the prevailing U-Net architecture, which can conduct end-to-end training and enhance the resolution of traditional inversion imaging results. This methodology has been tested on both synthetic and field measured data. Resolution has been improved, and the resistivity value of both target and geological background is closer to the synthetic model comparing to the tradition method. This work aids in improving the accuracy of subsurface target identification in ERT and serves as a guide for more precise ERT inversion in the future.

… multiple solutions to the inversion results. This paper achieves an efficient inversion of apparent resistivity values, utilizing the non-linear fitting capabilities of deep learning. Buildering a …

… inversion methods are often limited by boundary oversmoothing and high computational cost. Although deep learning has shown promise for ERT inversion, … apparent resistivity point-…

… Electrical resistivity tomography (ERT) is a nonlinear and ill… deep-learning approach to ERT inversion that accounts for uncertainty estimation. Our strategy integrates deep learning with …

… Unsupervised learning methods that incorporate … deep learning (DL) inversion. Firstly, we perform multiple gradient calculations and aggregate them, thereby updating the resistivity …

A 3D joint inversion of 2D electrical prospecting data can effectively solve the shortcomings of the 2D electrical method in detecting the spatial forms of anomalous bodies. However, the direction and interval between 2D electrical prospecting survey lines can affect the workload of field prospectors and the accuracy of the subsequent 3D inversion. This study established a physical model of an indoor water tank. Multiple sections of 2D electrical survey lines were laid and survey line data was acquired. The intersection angles between 2D electrical survey lines and anomalous bodies as well as the relations between the 3D inversion characteristics and 2D electrical prospecting data for different line intervals were studied. The results of this study indicate that the electrical response of cylindrical anomalous bodies tends to become weaker, first slowly and then faster, with a decrease in the angle. When the intersection angle is between 60° and 90°, the inverted forms of the anomalous bodies are unchanged. When the interaction angle is less than 60°, there is a significant twisting of the bodies and the electrical response is obviously weaker. The 3D joint inversion imaging using multi-section resistivity data can effectively improve the recognition on the spatial forms of objects. However, the joint inversion obtained with different survey line intervals varies in how precisely the objects are depicted. The increase in the interval between survey lines can be divided into four main stages in terms of the 3D joint inversion. To maintain the efficiency of practical prospecting and because survey data forming shallow strata are affected by external interference, it was found experimentally that the optimum interval for the effective depiction of the locations and forms of objects ranges from 8 cm to 12 cm. Specifically, the least number of 2D survey lines can be deployed while ensuring the overall accuracy and reliability of the survey result when the survey line interval/2D electrical prospecting inversion depth ranges from 8/17 to 12/17. The study results may help us to guide the arrangement of electrical survey lines in field prospecting and improve construction efficiency and the accuracy of joint inversion.

Abstract We present a 3-D cross-gradient joint inversion algorithm for seismic refraction and DC resistivity data. The structural similarity between seismic slowness and resistivity models is enforced by a cross-gradient term in the objective function that also includes misfit and regularization terms. A limited memory quasi-Newton approach is used to perform the optimization of the objective function. To validate the proposed methodology and its implementation, tests were performed on a typical archaeological geophysical synthetic model. The results show that the inversion model and physical parameters estimated by our joint inversion method are more consistent with the true model than those from single inversion algorithm. Moreover, our approach appears to be more robust in conditions of noise. Finally, the 3-D cross-gradient joint inversion algorithm was applied to the field data from Lin_an ancient city site in Hangzhou of China. The 3-D cross-gradient joint inversion models are consistent with the archaeological excavation results of the ancient city wall remains. However, by single inversion, seismic slowness model does not show the anomaly of city wall remains and resistivity model does not fit well with the archaeological excavation results. Through these comparisons, we conclude that the proposed algorithm can be used to jointly invert 3-D seismic refraction and DC resistivity data to reduce the uncertainty brought by single inversion scheme.

ABSTRACT The impact of untreated sewage irrigation and waste disposal practices on groundwater is investigated by 3D joint inversion of radio magnetotelluric and electrical resistivity tomography data. In this case study, electrical resistivity tomography and radio magnetotelluric field measurements were carried out on several profiles near a waste disposal site which was irrigated with untreated sewage water for agriculture purpose. In addition, radio magnetotelluric and electrical resistivity tomography measurements were carried out, far away from the waste site, to derive the uncontaminated geology. The data were analysed earlier using 2D inversion techniques. However, for the 2D inversion of the electrical resistivity tomography and radio magnetotelluric data, assumptions about the strike direction are required. As no clear strike direction is evident for the contamination, we considered the problem as 3D and interpreted the present data set using the 3D inversion algorithm ‘AP3DMT‐DC’. The inverted 3D resistivity model shows an unconfined aquifer of low resistivity which is overlain by an unsaturated slightly resistive near surface formation. With increasing distance from the waste sites, an increase in the resistivity of the shallow unconfined aquifer is observed. Furthest away from the waste site undisturbed geology is expected. We derived consistent and meaningful 3D resistivity models. The uncontaminated reference site indicates an increased resistivity for the aquifer layer. A synthetic 3D study was carried out to demonstrate and validate algorithm performance as well as convergence capabilities. The study demonstrates that the two methods, electrical resistivity tomography and radio magnetotelluric, complement each other. Besides, a better resolved inverted model is obtained through a 3D joint inversion, in comparison to individual 2D and 3D inversions.

Abstract Using the unstructured mesh, a new two-dimensional joint inversion algorithm has been developed for Radiomagnetotelluric and Direct current resistivity data. The unstructured mesh is generated with triangular cells, whose vertical and lateral lengths increase towards the depths. The Finite Element Method (FEM) has been used in the forward modelling part of the developed joint inversion algorithm. In the previous studies, structured grid-based joint inversion algorithms have been developed using the Finite Difference Method (FDM). In the structured grid-based algorithms, when the mesh is being generated with rectangular cells, the vertical lengths of the cells get bigger towards the depths while the lateral lengths remain constant. With the structured mesh, the undulated surface topography cannot be represented well enough. Also, because of the incompatible aspect ratio of model cell sizes in deeper model sections, the resolution of the model parameters will get smaller and cannot be resolved well with the structured grids. Imaging of surface topography and underground resistivity structures by the new algorithm requires fewer elements than those using structured grids. Therefore, the developed algorithm is faster than traditional 2D inversion algorithms. Furthermore, the resolution of the deeper model parameters has been increased by using the definition of the unstructured grid. A regularized inversion scheme with a smoothness-constrained stabilizer has been employed to invert the data. First, we have tested the developed joint inversion algorithm using synthetic data simplified from archaeological and mine site scenario and the results have been compared with the conventional algorithms using structured grids. We have also tested our algorithm with the real data which were collected from mineral investigation site at approximately 10 km east of the Elbistan district of Kahramanmaras province, in the west of the Taurus Mountains, Turkey. The results show that the developed joint inversion algorithm is a powerful tool to detect both resistive and conductive targets.

Addressing the significant impact of undulating terrain on 3-D direct current (dc) inversion, the unreliability of traditional linearized polarizability inversion for highly polarizable anomalies, and the nonuniqueness of separate resistivity and time-domain-induced polarization (TDIP) inversion, this article conducts a joint inversion study of 3-D dc resistivity and TDIP with structural constraints in undulating topography. A regularly arranged deformed hexahedron mesh simulates undulating surface terrain, transformed into regular hexahedron elements for 3-D undulating terrain dc resistivity modeling. Based on the exact inversion of polarization calculated from the inversion results of apparent resistivity and equivalent apparent resistivity data, a joint inversion of resistivity and polarization constrained by cross-gradient is implemented. Synthetic data examples show that the application of arbitrary hexahedron elements significantly reduces the influence of terrain on inversion, and the implementation of joint inversion markedly improves the recovery of high-polarization anomalies while enhancing both the model resolution and the inversion accuracy. The proposed algorithm is applied to the joint inversion of resistivity and polarizability in the lead–zinc mining area of Xiagalaiaoyi River in Huzhong area, the Great Khingan Mountains, and Northwestern Heilongjiang Province, achieving good results.

… to the 3D resistivity inversion, challenging … a joint algorithm to optimize the mutation direction and improve search efficiency, so that GA becomes feasible for the 3D resistivity inversion. …

… inversion algorithm for dc resistivity data, using conjugate gradient (CG) relaxation techniques. Firstly, to solve the minimum structure inverse … in each inverse iteration. Therefore the …

… and resolve the joint inversion system. To solve the above problems, we propose a new joint inversion strategy by combining the model updates from separate inversions and cross-…

… direct current resistivity data were compared and we demonstrated that the joint inversion result … more accurately than the inversion results of each individual method. The developed 2D …

In this work the effectiveness of 2D and 3D algorithms for inverting Electrical Resistivity Tomography (ERT) data deriving from 3D structures is studied. Further, an analysis of data-collection strategies in the case of 3D structures is being carried out. Dense 2D measurements are considered a practical tool for mapping 3D structures given the current limitations in ERT hardware. To perform the tests 2D and a 3D inversion programs are used. Both schemes use a forward model based on a 2.5D and 3D finite element scheme respectively. For both the 2D and 3D cases a fully non-linear inversion scheme based on a smoothness constrained algorithm is used. The Jacobian matrix is calculated using the adjoined equation technique. Comparisons are being carried out by means of synthetic examples for 3D models and dense 2D measurements with their axis parallel to the X (X-lines) and/or Y (Y-lines) directions. For the case of 3D structures and 2D inversion tests results illustrate that both X-line, Y-line measurements are required to delineate the modeling body. However, when 3D inversion is considered either Xline or Y-line measurements are adequate to produce good quality reconstructions of the subsurface. Overall, results clearly illustrate the superiority of 3D over 2D inversion schemes in the case of 3D structures both in view of quality and logistics. Despite the increased computational time required by 3D inversion schemes, good quality results can be produced. Further, 2D inversion techniques require effectively a double amount of measurements to produce acceptable results. The ongoing advancement of fast computers renders the described approach of combining dense 2-D measurement with 3D inversion practical for routine data treatment.

… as well as the joint inversion of gravity and magnetic … resistivity inversion and consequently not in its joint inversion. Here, we will show their usefulness for joint inversion of DC resistivity …

… Jegen, MD, Hobbs, RW, Tarits, P., and Chave, A., 2009, Joint inversion of marine magnetotelluric and gravity data incorporating seismic constraints Preliminary results of sub-basalt …

ObjectiveThe conventional inversion of surface direct-current (DC) resistivity data faces challenges such as limited exploration accuracy and depth. This study aims to enhance the accuracy and reliability of exploration in complex engineering and geological settings through the joint inversion of borehole-surface DC resistivity data. MethodsFirst, different geoelectric models were constructed through three-dimensional (3D) forward modeling of full-space DC resistivity. Then, the potential response characteristics under surface and borehole observation methods were systematically analyzed to determine the differences in detection sensitivity between surface and borehole observations. Accordingly, a joint 3D inversion technique for borehole-surface DC resistivity data was developed using the Gauss-Newton method. Finally, the effectiveness and superiority of the joint inversion technique were verified using theoretical models and actual data. Results and Conclusions The inversion of surface observations yielded high lateral resolution but a limited vertical resolution, while the inversion of borehole observations exhibited a high vertical resolution. In contrast, the joint inversion of borehole-surface data provided more accurate resistivity structures and physical property values. The effectiveness of the joint inversion technique was verified using both theoretical models and actual data. The verification results demonstrate that the joint inversion technique combines the advantages of the surface and borehole observation methods, thereby enhancing both the spatial resolution of inversion results and the capability for identifying deep anomalous body. Therefore, the developed joint inversion technique provides technical support for improving the exploration accuracy in complex engineering and geological settings.

… of the static shift factor using the joint inversion. In addition, we also used … our joint inversion code. With the availability of our new joint inversion software, we expect the number of joint …

The stable occurrence of natural gas hydrates depends on specific temperature and pressure conditions. Disturbances to these conditions readily trigger extensive hydrate decomposition, potentially inducing reservoir instability and engineering geological hazards such as landslides. To elucidate the dynamic patterns of reservoir phase transitions and the evolution of engineering geological risks during heat stimulation of natural gas hydrate extraction, this paper conducted targeted research through indoor physical model experiments. A water tank and sandy soil mixture simulate the seabed sedimentary environment, with high‐resistivity ice blocks substituting for hydrates. A high‐density electrical resistivity metre records dynamic resistivity data during ice melting, simulating reservoir phase transitions and spatial responses during heat stimulation of hydrate extraction. Results show that the initial hydrate‐bearing zone exhibited a resistivity 67% higher than the background, confirming effective identification of hydrate. During decomposition, resistivity decreased significantly, eventually dropping 21% below the background, demonstrating full‐cycle tracking potential. Additionally, the low‐resistivity weak layer expanded to 256 cm 2 , indicating significant soil settlement and pore development that pose structural instability risks. This study provides experimental reference for reservoir dynamic monitoring and disaster risk prevention during heat stimulation of hydrate extraction.

Abstract Detecting, real-time monitoring and early warning of underground water-bearing structures are critically important issues in prevention and mitigation of water inrush hazards in underground engineering. Direct current (DC) resistivity method is a widely used method for routine detection, advanced detection and real-time monitoring of water-bearing structures, due to its high sensitivity to groundwater. In this study, the DC resistivity method applied to underground engineering is reviewed and discussed, including the observation mode, multiple inversions, and real-time monitoring. It is shown that a priori information constrained inversion is desirable to reduce the non-uniqueness of inversion, with which the accuracy of detection can be significantly improved. The focused resistivity method is prospective for advanced detection; with this method, the flanking interference can be reduced and the detection distance is increased subsequently. The time-lapse resistivity inversion method is suitable for the regions with continuous conductivity changes, and it can be used to monitor water inrush in those regions. Based on above-mentioned features of various methods in terms of benefits and limitations, we propose a three-dimensional (3D) induced polarization method characterized with multi-electrode array, and introduce it into tunnels and mines combining with real-time monitoring with time-lapse inversion and cross-hole resistivity method. At last, the prospective applications of DC resistivity method are discussed as follows: (1) available advanced detection technology and instrument in tunnel excavated by tunnel boring machine (TBM), (2) high-resolution detection method in holes, (3) four-dimensional (4D) monitoring technology for water inrush sources, and (4) estimation of water volume in water-bearing structures.

… subsurface, time-lapse electrical resistivity imaging method was applied in … resistivity over chosen lines at different times were done to monitor the changes in the subsurface resistivity …

… Electrical resistivity tomography (ERT) monitoring provides time-… monitoring an effective tool to evaluate precursory conditions of failure. This work presents the results of ERT monitoring …

… Integrating the electrical resistivity models with geological data, we obtain the geological framework of the landslide (Figure 4b). The red solid line marks the interface between the …

Cross-borehole electrical resistivity tomography was used to detect and image a concealed air-filled mineshaft at a greenfield test site. The measurement configurations and panel combinations were selected using a two-stage optimization process. An optimal set of array configurations was selected for each cross-borehole panel on the basis of the model resolution matrix. Subsequently, various combinations of panels were tested with synthetic and field data to determine the effects of coverage and data density on the resulting tomographic image. In the field trials, complicating factors were introduced by the use of resistive cement linings in the boreholes. A resistive feature was detected between the boreholes using a single panel and a 2.5D inversion, but the image quality was too poor to identify this as a mineshaft. A much-improved image was obtained using eight boreholes and eight panels with a full 3D inversion. Only four of these panels intersected the shaft. Crucially, the other panels provided coverage of outlying regions of the model, enabling the inversion algorithm to distinguish between the resistive effects of the borehole linings and the mineshaft.

… In this paper, to have a rough image of the subsoil electrical structure, an apparent resistivity pseudosection equivalent to the case of the equatorial dipole–dipole on the surface has …

Abstract Cross-hole electrical resistivity tomography is a useful tool in geotechnical, hydrogeological or fluid/gas plume migration studies. It allows better characterization of deep subsurface structures and monitoring of the involved processes. However, due to the large amount of possible four-electrode combinations between boreholes, the choice of the most efficient ones for rapid plume migration experiments (real-time monitoring), becomes a challenge. In this work, a numerical simulation to assess the capabilities and constraints of the most common cross-hole configurations for real-time monitoring is presented. Four-electrode configurations, sensitivity, dependence on the body location and amount of data were taken into account. The analysis of anomaly detection and the symmetry of the sensitivity pattern of cross-hole configurations allowed significant reduction of the amount of data and maintaining the maximum potential resolution of each configuration for real-time monitoring. The obtained results also highlighted the benefit of using the cross-hole AB–MN configuration (with both current – or potential – electrodes located in the same borehole) combined with other configurations with complementary sensitivity pattern.

… resistivity tomography (ERT), which is a technique for imaging the subsurface electrical … cross-hole and in-hole configurations are used for a total of 861 configurations. The crosshole …

… in the resistivity images. Other 3D effects resulting in inversion artifacts are shadow effects caused by use of a 2D code to invert data from a 3D body located outside the image plane. In …

Abstract As the conventional surface ERT method is limited by its low resolution at depth, cross-hole electrical resistivity tomography (CHERT) method is increasingly used in the field of geo-environment and hydrogeology whenever possible. Researches regarding CHERT configurations and some negative effects in this method have been useful in survey planning and data interpretation. Nevertheless, some issues remained to be resolved before a standard guideline can be drawn up for conducting CHERT. The symmetric effect was recently pointed out as a major issue in resistivity tomography involving borehole measurements including both borehole-to-surface and cross-hole methods. Symmetrical artifacts emerge for certain types of electrode configuration, which are often desired for better resolution. In this study, the symmetric effect was further investigated in a general two-hole CHERT layout, which is more frequently used in the field. The influence of symmetric effect is found to manifest when the assumption of boundary condition in the inversion is incorrect. The effect of electrode configuration and inversion scheme was further examined and the extended inversion model was found to be more suitable for CHERT data inversion. In particular, the optimal extended range outside the boreholes on each side was shown to be 0.25 times the borehole depth. To mitigate the symmetric effect, a more practical optimal array was proposed. These new suggestions were further verified by a field example.

… horizontal cross-borehole resistivity tomography imaging results, providing a more accurate … via probe drilling analysis and horizontal cross-hole resistivity tomography is relatively …

… were used to detect and image a concealed mineshaft at a site … mode of ERT deployment suitable for imaging beneath buildings or in … to be made between 2D and 3D imaging methods. …

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration.