Electrical Resistivity Tomography Inversion Research Articles

Electrical resistivity tomography inversion combining deep variational autoencoder and stochastic adaptive sampling

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.

Application of electrical resistivity tomography for groundwater evaluation in Yirgacheffe Town and its environs, Main Ethiopian Rift

Exploring groundwater is crucial in areas with poor surface water quality, such as Yirgacheffe Town and its surroundings. This study aims to assess aquifer materials and groundwater potential using Electrical Resistivity Tomography (ERT) in the region,within the Main Ethiopian Rift. Four electrical resistivity profiles were gathered using a Wenner array with a WDA-1 digital resistivity and induced polarization meter from Chongqing, China. Each profile extended 236 m with 4-m electrode spacing. Data were analyzed and inverted with Res2DinV software and validated against lithological logs. The ERT inversion revealed three subsurface geoelectric layers: (1) massive rhyolite ignimbrite and basalt (>2000 Ωm); (2) weathered rhyolite ignimbrite and basalt (200–2000 Ωm); and (3) topsoil (4–200 Ωm). These results are key for effective water resource management and highlight ERT's utility in similar geological settings.

A case study of canal seepage quantification using gain/loss method and electrical resistivity tomography in an intensively managed water resource system in the Treasure Valley, Idaho, United States

Monitoring groundwater-surface water (GW-SW) interactions is essential for effectively managing the available water resources in semi-arid and arid environments. The focus of this study was to quantify how much SW is being exchanged with the shallow GW aquifer in the Treasure Valley (TV), Idaho, USA. Previous water budgets estimated regional canal seepage without incorporating canal variability and flow measurement uncertainty. To address this, we applied both direct (gain/loss) and indirect (electrical resistivity tomography (ERT)) techniques. Direct seepage measurements were taken on canals capturing a range of different characteristics in the TV. The discrete measurements were then used to estimate the total seepage anticipated to be lost from these canals during the irrigation season. Our findings showed high seepage variability across the canals. The ERT inversion approach was utilized before and after the irrigation season by applying an advanced inversion scheme to better constrain the canal seepage spatial variability and uncertainty by quantifying changes in the saturated zone with 2D-ERT results. Temporal changes in the subsurface resistivity were observed due to the lateral flow from the nearby surface canal during the irrigation season. The combination of these approaches improves our understanding of SW-GW interactions in intensively managed irrigation systems.

Uncertainty quantification in electrical resistivity tomography inversion: hybridizing block-wise bootstrapping with geostatistics

SUMMARY 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 data set from the previous step. Finally, we employ a globally convergent homotopic continuation method on each bootstrapped data set 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.

Multifaceted anomaly detection framework for leachate monitoring in landfills

The imperative to preserve environmental resources has transcended traditional conservation efforts, becoming a crucial element for sustaining life. Our deep interconnectedness with the natural environment, which directly impacts our well-being, emphasizes this urgency. Contaminants such as leachate from landfills are increasingly threatening groundwater, a vital resource that provides drinking water for nearly half of the global population. This critical environmental threat requires advanced detection and monitoring solutions to effectively safeguard our groundwater resources. To address this pressing need, we introduce the Multifaceted Anomaly Detection Framework (MADF), which integrates Electrical Resistivity Tomography (ERT) with advanced machine learning models—Isolation Forest (IF), One-Class Support Vector Machines (OC-SVM), and Local Outlier Factor (LOF). MADF processes and analyzes ERT data, employing these hybrid machine learning models to identify and quantify anomaly signals accurately via the majority vote strategy. Applied to the Chaling landfill site in Zhuzhou, China, MADF demonstrated significant improvements in detection capability. The framework enhanced the precision of anomaly detection, evidenced by higher Youden Index values (≈ 6.216%), with a 30% increase in sensitivity and a 25% reduction in false positives compared to traditional ERT inversion methods. Indeed, these enhancements are crucial for effective environmental monitoring, where the cost of missing a leak could be catastrophic, and for reducing unnecessary interventions that can be resource-intensive. These results underscore MADF's potential as a robust tool for proactive environmental management, offering a scalable and adaptable solution for comprehensive landfill monitoring and pollution prevention across varied environmental settings.

PyMERRY: A Python solution for an improved interpretation of electrical resistivity tomography images

Electrical resistivity tomography (ERT) is a widely used geophysical method for studying geologic hazards, civil engineering, and environmental remediation. It provides information about the subsurface’s resistivity distribution by analyzing electrical data collected at the surface or in boreholes. However, interpreting ERT images can be complex due to ambiguities in their resolution. To address this issue, we develop a postprocessing method called Python iMprovement of Electrical Resistivity tomography ReliabilitY (PyMERRY) to improve the reliability of ERT images. The PyMERRY code can be applied to any 2D resistivity model obtained from ERT inversion software. It computes a coverage mask that defines the domain well constrained by the data and the inversion process. It also evaluates the resistivity uncertainties in the ERT models. In addition to the sensitivity approaches, PyMERRY provides low- and high-resistivity values for all covered cells. Synthetic tests indicate that the approach is efficient and highlight the importance of resistivity contrasts, mesh selection, electrode spacing, and profile length in the reliability of ERT images. Compared with previous studies, using PyMERRY in south-central Bhutan allows a more accurate interpretation of ERT images. It confirms a high-resistivity contrast across the topographic frontal thrust and reveals the existence of small-scale variations.

Processing of electrical resistivity tomography data using convolutional neural network in ERT-NET architectures

Electrical resistivity tomography (ERT) inversion has emerged as an effective method for predicting resistivity in complex geological structures. In most cases, traditional ERT inversion problems are posed as nonlinear optimization problems. Solving distribution resistivity inversion can be computationally challenging for two reasons: one is the significant cost of software and the other is the issue of local minima. The ERT-NET architecture was developed in this study to learn the parameter regression relationship between geophysical ERT datasets and subsurface models. We developed a novel convolutional neural network (CNN) technique that comprised of a fully connected network (ERT-INET) and a fully convolutional network (ERT-UNET); both train ambiguity information of the inverted resistivity based on processing ERT datasets. We also output our network segmentation of pixel-wise prediction for ERT-INET and structured prediction segmentation. The noise assessments of our network inversion were managed by employing depth of investigation (DOI) and statistical analysis for evaluation performance. The DOI appeared to be effective in conveying the breadth of possibility within our networks. Moreover, the performances are either the synthetic resistivity model or the field resistivity data, both of which have an average of greater than 95%. The inversion results of both architectures are precisely and accurately expressed, containing approximately the ground truth models and thereby also the field observation models. We conclude that these ERT-NET architectures could be one approach to ERT interpretation handling, and we strongly suggest alternatives that promote the geoelectrical method of interpretation.

Sub-surface configuration in the northern part of Lembang groundwater basin recharge area

Lembang groundwater basin has an area of 209 km2. Increased tourism activities in the Lembang Groundwater Basin require sufficient groundwater resources to support the sustainability of these tourism activities. The purpose of this study was to analyze the hydrostratigraphy of the groundwater recharge zone of the Lembang groundwater basin in the Cikole-Lembang. Electrical Resistivity Tomography (ERT) with dipole–dipole electrode array and seismic refraction was used to analyze the hydrostratigraphy. The results of ERT and seismic refraction inversion show that Tangkubanparahu Pyroclastic Fall 2 can be characterized as aquifers. This layer consists of low (123–292 Omega m) and intermediate (293–700 Omega m) resistivity value and also has low (300–1350 m/s) to medium (2700–1350 m/s) velocity. Tangkubanparahu lava (Tl) layer has a high resistivity value (701–3875 Omega m) and high (1350–2999 m/s) velocity. The Tangkubanparahu Pyroclastic Fall 1 has a resistivity of 300–700 ohms. Tangkubanparahu (Tl) lava deposits can be characterized as fracture aquifer and impermeable layers in the Tangkubanparahu volcanic hydrogeological system depend on the historical structural geology event.

Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys

Clays in fault zones have low electrical resistivity, making electrical resistivity tomography (ERT) effective for fault investigations. However, traditional ERT inversion methods struggle to find a unique solution and produce unstable results owing to the ill-posed nature of the problem. To address this, a workflow integrating deep-learning (DL) technology with traditional ERT inversion is proposed. First, a deep-learning model named DL-ERT inversion that maps apparent resistivity data to subsurface resistivity models is developed. To create target-oriented training data, we use approximately 150 field borehole data acquired from various survey areas in South Korea. The DL-ERT inversion algorithm is based on a U-Net structure and includes an additional network called the borehole mixer to incorporate borehole information when available. The DL-ERT inversion model is trained in three stages: base model training, borehole mixer training, and fine-tuning. Results showed that the fine-tuning model provided the highest prediction accuracy for all test datasets. Next, the prediction of the trained model is used as the initial model for the deterministic inversion method to predict the final subsurface model. The efficiency and accuracy of the proposed workflow are demonstrated in fault detection using a field data example compared with traditional deterministic inversion.

4D interpretation of time-lapse electrical resistivity monitoring data to identify preferential flow path in a landfill, South Korea.

Monitoring the leakage of leachate from a landfill is critical in preventing possible contamination in the surrounding area. Time-lapse (TL) electrical resistivity tomography (ERT) has been performed along eleven survey lines at four different time points in a landfill in Korea. The TL data sets were interpreted using an in-house 4D inversion algorithm. Changes in 4D inversion results were analyzed in order to interpret a leachate-contaminant region. Since the rainy season started during obtaining TL ERT data sets, the effects of precipitation on TL ERT data are also analyzed. Changes in electrical resistivity (ER) showed that precipitation increases ER of contaminant zones. As hydrogeochemical data offer contamination information in some areas where boreholes are located, these are helpful to interpret and compare with ERT inversion results to evaluate the extent of the contaminated plume. We also classified soil textures from particle size analysis on soil samples and analyzed electrical conductivity (EC) and dissolved oxygen (DO) using groundwater samples obtained from observation wells in the survey site. The information on soil structure as well as the results of 4D inversion provided insight into the location of a preferential flow path.

Application of the electrical resistivity tomography in groundwater detection on loess plateau

Agricultural irrigation of the South Jingyang tableland in Shaanxi Province, China has led to a continuous rise of the groundwater level and has triggered a series of loess landslides, thereby seriously affecting the life and property safety of local residents. Research shows that the major cause of the landslide in the loess layer of the South Jingyang tableland is the rising groundwater level. Therefore, the research on the formation mechanism of landslide in this area should include the investigation of the stratigraphic structure and groundwater level distribution characteristics. On this basis, a series of approaches, such as electrical resistivity tomography (ERT), borehole, and laboratory tests, was carried out on the South Jingyang tableland, and the groundwater level distribution and stratigraphic structure in the study area were determined. The qualitative relationship between resistivity value and water content at different depths was detected using the inversion results of ERT and borehole data. Through laboratory tests, the quantitative relationship between resistivity values under different water contents was established. The precise depth of the groundwater level was inferred by connecting the qualitative relationship with the quantitative relationship, and then a detailed 3D geological model was established by linking the inversion results of ERT with the field borehole lithology data and geological survey data. The detection results show that when the qualitative and quantitative analyses of the ERT inversion results were combined, the distribution of the groundwater level was accurately judged. The ERT is effective in reflecting the stratigraphic structure and hydrological characteristics of the Loess Plateau, and its potential as a supplementary technology for detecting the groundwater level is reasonable. This study addresses the limitation and inaccuracy in determining the stratum structure and groundwater level by solely relying on borehole information or ERT. The established 3D geological model not only provides a basis for the study of groundwater table fluctuation, but also a technical guidance for the stability evaluation of loess slope, landslide prediction, and early warning in the study area.

Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN

Abstract. Electrical resistivity tomography (ERT) is a broadly accepted geophysical method for subsurface investigations. Interpretation of field ERT data usually requires the application of computationally intensive forward modeling and inversion algorithms. For large-scale ERT data, the efficiency of these algorithms depends on the robustness, accuracy, and scalability on high-performance computing resources. In this regard, we present a robust and highly scalable implementation of forward modeling and inversion algorithms for ERT data. The implementation is publicly available and developed within the framework of PFLOTRAN, an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The forward modeling is based on a finite-volume discretization of the governing differential equations, and the inversion uses a Gauss–Newton optimization scheme. To evaluate the accuracy of the forward modeling, two examples are first presented by considering layered (1D) and 3D earth conductivity models. The computed numerical results show good agreement with the analytical solutions for the layered earth model and results from a well-established code for the 3D model. Inversion of ERT data, simulated for a 3D model, is then performed to demonstrate the inversion capability by recovering the conductivity of the model. To demonstrate the parallel performance of PFLOTRAN's ERT process model and inversion capabilities, large-scale scalability tests are performed by using up to 131 072 processes on a leadership class supercomputer. These tests are performed for the two most computationally intensive steps of the ERT inversion: forward modeling and Jacobian computation. For the forward modeling, we consider models with up to 122 ×106 degrees of freedom (DOFs) in the resulting system of linear equations and demonstrate that the code exhibits almost linear scalability on up to 10 000 DOFs per process. On the other hand, the code shows superlinear scalability for the Jacobian computation, mainly because all computations are fairly evenly distributed over each process with no parallel communication.

Evaluation of Artifacts and Misinterpretation in 2D Electrical Resistivity Tomography Caused by Three-Dimensional Resistive Structures of Regular or Irregular Shapes

Electrical resistivity tomography (ERT) is a well-known geophysical method applied to geological, hydrogeological and geoenvironmental research. To date, 2D ERT is still used much more than 3D ERT, thanks to its greater immediacy, survey speed and lower complexity in processing and inversion. However, the assumption of two-dimensionality of the underground structures can mean that the effects of 3D structures on the 2D ERT can sometimes lead to gross errors in interpretation. This work aims to evaluate these effects by testing synthetic and experimental models. Numerical simulations are performed starting from different resistivity models, and from the results, 2D data sets are derived to study and quantify the effects of 2D inversion on 3D structures. Tests simulating prismatic resistive blocks with a vertical square section are presented. Prisms extend orthogonally to the survey line. Depending on their length, they range from a minimum equal to the length of the section (cubic resistive block) to infinity (2D prism). On these models, 2D and 3D electrical resistivity tomography (ERT) surveys are simulated. The results show that resistive blocks with a limited extension orthogonal to the profile are not effectively resolved by 2D ERT. Additionally, resistivity values obtained from a 2D ERT inversion on a 3D resistive prism are underestimated more than those obtained on the corresponding 2D prism when compared with the true value. This underestimation increases with the three-dimensional characteristics. Furthermore, resistive blocks located near the survey line but not crossed by it create artifacts that can lead to erroneous interpretations. A field test performed on a calcarenite quarry, of which the three-dimensional geophysical model was reconstructed, confirmed the results obtained by the synthetic tests, highlighting that the effects of three-dimensionality can lead to the identification of artifacts in the vertical section or produce strong errors in the estimation of depth and size, thus causing misleading statements.

Salinity distribution in the subterranean estuary of a meso-tidal high-energy beach characterized by Electrical Resistivity Tomography and direct push technology

Understanding the interaction of terrestrial freshwater and seawater in the subterranean estuary (STE) is an important factor when considering nutrient fluxes from land to sea. State-of-the-art research describes the STE by a tide-induced upper saline recirculation cell, a freshwater discharge tube and a deep saltwater wedge. However, recent numerical modelling and shallow hydrogeochemical investigations for high-energy beaches indicate that multiple saline recirculation cells may exist and affect the land-sea interaction. Electrical Resistivity Tomography (ERT) and Direct Push (DP) technologies are common tools to explore the subsurface. Due to their sensitivity to the electrical conductivity of pore water, they permit investigating the STE. This study combines ERT and DP to image the salinity distribution within the STE of a meso-tidal, high-energy beach. We actively incorporate the DP data into the ERT inversion and use geostatistical regularization for closing the resolution gap. For the first time, our experimental results confirm the existence of several 10–20 m deep reaching upper saline recirculation cells and corresponding brackish discharge locations generated by a pronounced runnel-ridge beach system in 2019, whereas in 2021 only a single cell was displayed for a flat topography at the time.

Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.1098. The results generated by Logcosh are better than MSE, MAE, and Huber. ERTInvNet with Selu activation function can better learn the nonlinear relationship between voltage and resistivity. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, the best choices for Relu, Selu, Leaky_Relu, and Softplus. Compared with Adadelta, Adagrad, and Aadmax, Adam has the best performance in ERTInvNet with the optimizer. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, respectively. By optimizing the critical parameters of deep learning, the accuracy of ERT in identifying hydrate distribution is improved.

Time-lapse adaptive inversion on resistivity monitoring data with Lp norm regularization and cross-time weight

Time-lapse electrical resistivity tomography (ERT) can describe the dynamic processes occurring underground and has been widely employed in the fields of hydrology, environmental management, and engineering. The time-lapse inversion method is based on the space- and time-dimension regularization technique as it considers complementary information about more than one time interval and reduces the potential causes of the geophysical data inversion of the artifact. However, the L2 norm inversions smear out the models and cannot distinguish between the resistivity interfaces. Moreover, the determination of the two optimal regularization parameters for time-lapse inversion remains challenging, and the fixed-time regularization parameter smooths out the low-sensitivity area and inhibits the real variation on the target body of the deep region. To overcome this problem, we propose a time-lapse ERT inversion method of adaptively imaging the time-lapse model and focusing on the resistivity variation on the time dimension. This method is based on the Lp norm regularization provided by the M-estimator and uses the adaptive regularization parameters based on the model roughness and data misfit. In addition, a cross-time matrix improving the sensitivity in the deep region of model space is added to the time regularization term. We present two synthetic examples and a field dataset to validate the method. Numerical simulation and field dataset inversion results revealed that the proposed time-lapse inversion strategy can avoid the excessive smoothness caused by the low sensitivity in the model deep region and can thus enhance the response from the true ground variation to improve the accuracy of the inversion results. Owing to the adaptive regularization parameters that can preserve the dynamic balance between the objective function components, the inversion iteration progress can remain stable and can maintain the trend toward convergence.

The Extended Data-Adaptive Probability-Based Electrical Resistivity Tomography Inversion Method (E-PERTI) for the Characterization of the Buried Ditch of the Ancient Egnazia (Puglia, Italy)

A geoelectrical survey was carried out outside the walls of the ancient Egnazia (Puglia, Italy) with the aim of enriching the knowledge about its defense system. Nine Electrical Resistivity Tomographies (ERTs) were realized using the dipole-dipole (DD) electrode array, approximately transversal to the walls and equally spaced. The new Extended data-adaptive Probability-based Electrical Resistivity Tomography Inversion Method (E-PERTI) was applied, for the first time, to model the resistivity distribution of a large dataset. Considering some peculiar aspects of the general theory of probability, an optimization of results was reached giving major emphasis to one dataset portion rather than another and inspecting selectively vertical or lateral resistivity variations. In this way, sets of aligned resistivity lows attributable to the trace of an ancient ditch were found.

Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders

ABSTRACTTo reduce both the computational cost of probabilistic inversions and the ill‐posedness of geophysical problems, model and data spaces can be reparameterized into low‐dimensional domains where the inverse solution can be computed more efficiently. Among the many compression methods, deep learning algorithms based on deep generative models provide an efficient approach for model and data space reduction. We present a probabilistic electrical resistivity tomography inversion in which the data and model spaces are compressed through deep convolutional variational autoencoders, while the optimization procedure is driven by the ensemble smoother with multiple data assimilation, an iterative ensemble‐based algorithm. This method iteratively updates an initial ensemble of models that are generated according to a previously defined prior model. The inversion outcome consists of the most likely solution and a set of realizations of the variables of interest from which the posterior uncertainties can be numerically evaluated. We test the method on synthetic data computed over a schematic subsurface model, and then we apply the inversion to field measurements. The model predictions and the uncertainty assessments provided by the presented approach are also compared with the results of a Markov Chain Monte Carlo sampling working in the compressed domains, a gradient‐based algorithm and with the outcomes of an ensemble‐based inversion running in the uncompressed spaces. A finite‐element code constitutes the forward operator. Our experiments show that the implemented inversion provides most likely solutions and uncertainty quantifications comparable to those yielded by the ensemble‐based inversion running in the full model and data spaces, and the Markov Chain Monte Carlo sampling, but with a significant reduction of the computational cost.

Evaluation of lithostratigraphic units and groundwater potential using the resolution capacities of two different electrical tomographic electrodes at dual-spacing

The selection of a choice electrode is pertinent to attenuating noise and improving geophysical tomographic inversion results. Besides, the detailed understanding of the geodynamic condition of subsurface formation is crucial to sustainable potable groundwater abstraction. Hence, the subsurface lithostratigraphic units and groundwater potential of two sites (i.e., Site 1 and Site 2) within the Universiti Sains Malaysia were evaluated using borehole-constrained electrical resistivity tomography (ERT) and induced polarisation (IP) tomography. Both methods employed the resolution capacities of stainless-steel and copper electrodes at dual-spacing. The ERT and IP field data and inversion results for copper electrodes were generally robust due to the generated higher positive data points and lower RMS errors, percentage relative differences, and mean absolute percentage errors (MAPE) than the stainless-steel electrodes, especially at Site 1 with a profile length of 200 m and an electrode spacing of 5 m. However, both electrodes tend to produce inversion models with almost the same parameters at Site 2, using half the profile length and electrode spacing of Site 1, i.e., 100 m and 2.5 m, respectively. Thus, the sensitivities and resolution capacities of the tomographic electrodes are heavily influenced by electrode spacing, profile length, amount of injected current, and depth of investigation. The borehole lithostratigraphic units, typically sandy silt, sand, and silty sand, have good correlations with the ERT and IP inversion results. The variability in observed resistivity and chargeability values were due to heterogeneous weathered materials and saturating water fills within the fractured and deeply-weathered granitic bedrock, with <200 Ωm and a chargeability of >1.8 msec. The models' median depth of >40 m mapped for the weathered and/or fractured sections was suggestive of high groundwater-yielding capacity in boreholes to sustain a part of the university community.

Hybrid parametric/smooth inversion of electrical resistivity tomography data

The standard smooth electrical resistivity tomography inversion produces an estimate of subsurface conductivity that has blurred boundaries, damped magnitudes, and often contains inversion artifacts. In many problems the expected conductivity structure is well constrained in some parts of the subsurface, but incorporating prior information in the inversion is not a trivial task. In this study we developed an electrical resistivity tomography inversion algorithm that combines parametric and smooth inversion strategies. In regions where the subsurface is well constrained, the model was parameterized with only a few variables, while the rest of the subsurface was parameterized with voxels. We tested this hybrid inversion strategy on two synthetic models that contained a well constrained highly resistive or conductive near-surface horizontal layer and a target beneath. In each testing scenario, the hybrid inversion improved resolution of feature boundaries and magnitudes and had fewer inversion artifacts than the standard smooth inversion. A sensitivity analysis showed that the hybrid inversion successfully recovered subsurface features when a range of regularization parameters, initial models, and data noise levels were tested. The hybrid inversion strategy can potentially be expanded to a range of applications including marine surveys, permafrost/frozen ground studies, urban geophysics, or anywhere that prior information allows part of the model to be constrained with simple geometric shapes.