Time-lapse Electrical Resistivity Tomography Research Articles

Brief communication: Mountain permafrost acts as an aquitard during an infiltration experiment monitored with electrical resistivity tomography time-lapse measurements

Abstract. Frozen layers within the subsurface of rock glaciers are generally assumed to act as aquicludes or aquitards. So far, this behavior has been mainly defined by analyzing the geochemical characteristics of spring waters. In this work, for the first time, we experimentally confirmed this assumption by executing an infiltration test in a rock glacier of the Southern Alps, Italy. Time-lapse electrical resistivity tomography (ERT) technique monitored the infiltration of 800 L of saltwater released on the surface of the rock glacier; 24 h ERT monitoring highlighted that the injected water was not able to infiltrate into the underlying frozen layer.

Estimating the Specific Yield and Groundwater Level of an Unconfined Aquifer Using Time-Lapse Electrical Resistivity Imaging in the Pingtung Plain, Taiwan

This study aims to apply geophysical methods to determine the Specific Yield (Sy) and Groundwater Level (GWL) in an unconfined aquifer of the Pingtung Plain in South Taiwan. Sy is an important hydraulic parameter for assessing groundwater potential. Obtaining specific yield for a large area is impractical due to the limited coverage and the high cost of the pumping test, which limits the potential evaluation of regional groundwater. Therefore, we used time-lapse Electrical Resistivity Imaging (ERI) to determine the Sy and GWL. Seasonal variations were considered when measuring time-lapse resistivity for five different months in 2019. We calculated the Sy and GWL from inverted resistivity data using empirical formulas and the soil–water characteristic curve (SWCC). We first used Archie’s law to calculate the relative saturation change with depth for each ERI profile, and then we used the Van Genuchten (VG) and Brooks–Corey (BC) empirical equations to estimate Sy and GWL. Finally, we compared the obtained GWL to the existing observation well to verify the findings of our study. The results showed that the VG and BC are able to predict Sy and GWL; however, the BC result is less consistent with the observation well result. In the study area, the dry season GWL ranged from 24.5 m to 35.2 m for the VG results and from 25.7 m to 35.5 m for the BC results. The wet season GWL ranged from 26.5 m to 38.9 m for the VG and from 26.4 m to 38.2 m for the BC results. The spatial distribution of the GWL shows a high gradient of GWL in the northeastern region, induced by significant proximal fan recharge. The determined spatial distribution of Sy varies from 0.15 to 0.21 for the VG and 0.14 to 0.20 for the BC results, indicating the study area has significant potential for groundwater resources. Therefore, nondestructive resistivity imaging can be used to aid in the determination of hydraulic parameters.

Spatio-temporal monitoring of leachates dispersion beneath a solid wastes dump in basement complex of southwestern Nigeria

For real-time monitoring and modeling of leachates dispersion beneath Arada wastes dump, Ogbomoso geologically located within Southwestern Nigeria basement complex, an integrated technique involving time-lapse Electrical Resistivity Tomography (ERT), hydrogeology, and geotechnic was used. ERT was performed using Wenner electrodes configuration in a Northeast-Southwest azimuth on the wastes dump monthly from March to September, and twice in March and September, at the control site, during the wet season. The Total Dissolved Solids (TDS), Electrical Conductivity (EC) and static water level of two monitoring wells on the dumpsite were measured during every ERT surveying. Twenty four soil samples were taken at 0.5 m intervals from 2.0 to 3.0 m deep bores on the anomalously low resistivity spots discovered on the subsurface resistivity distribution of the wastes dump, and sieve analysis and permeability tests were performed in the laboratory on these samples. Using the approach of grain-size analysis, the hydraulic conductivity and porosity were calculated empirically. On the wastes dump and control site, the resistivities of spatially distributed lenses of material ranged within 1.45–37.3 Ωm and 22.5–52.6 Ωm, respectively, suggesting leachates and clay anomalies. The wastes dump subsurface might be regarded as a porous medium since its porosity ranged between 38.2 and 44.2%, and the permeability range of (5.4–9.6) x 10−5 m/s showed that water could freely drain from it. Water samples taken from the monitoring wells on the waste dump had TDS and EC that ranged from 520 to 970 ppm and 1.05 to 2.37 mS/cm, respectively, but decreased in the southwest direction of groundwater flow, indicating leachates dispersion. The hydraulic conductivity ranged within 0.00285–0.176 m/s but varied anomalously and spatially indicating heterogeneous hydraulic conductivity field capable of initiating local variation in velocity that caused leachate to disperse longitudinally and transversely. The rate of longitudinal and transverse leachates dispersion as revealed by subsurface resistivity distribution images ranged within 0.000–0.250 m/day and 0.001–0.045 m/day, respectively. The former rate was 5.6 folds the latter rate and hence fell within the stipulated 5–100 for dispersion process that conformed to natural consequence of dispersivity. The conductivity-time curves (CTCs) of the leachates anomalies' steepness and doubly-peaked properties indicated Fickian dispersion, while different proportion times; t-1.588 and t-2.478, which are not equal to t-0.5, showed that the CTCs described non-Fickian dispersion at a significantly later time.

Time-Lapse Resistivity Monitoring of a Simulated Runoff Test in a Bioswale, Philadelphia

As part of the reconstruction of Interstate-95 in Philadelphia, a series of vegetated stormwater infiltration basins (bioswales) have been installed to manage highway runoff. To assess these bioswales, we used simulated runoff tests (SRTs), where the bioswale is flooded from a fire hydrant to simulate a major storm event. SRT monitoring relies on point measurements of inflow, outflow, and soil moisture to determine the volume of stormwater the bioswale can handle and the time to recovery. To provide better spatial coverage for site assessment, we tested the use of time-lapse electrical resistivity tomography (ERT) during a SRT. Using an onsite geophysical monitoring station, we performed ERT surveys every 4 h before, during, and after the SRT test. Inflow and outflow measurements taken during the SRT found that a majority of the water did not exit the bioswale via the outlet box. The time-lapse inversion results indicated that runoff uniformly spread throughout the basin before infiltrating into the heterogeneous urban soil below. The ERT demonstrated that the underlying native soil contributed to the overall performance of the bioswale, a contribution which was previously assumed to be minimal. Recovery to pretest soil moisture levels took roughly 2–3 days, according to both soil moisture sensors and time-lapse geophysical data. The SRT results were consistent with natural storm events recorded by our geophysical monitoring station, which have been monitored continuously for over a year. Use of ERT during SRTs characterize the pattern of infiltration and recovery rate of the soil beneath a stormwater control. Additionally, the heterogeneous infiltration observed during the SRT suggested that ERT surveys preconstruction may improve future planning of stormwater controls by guiding the location of infiltration measurements.

Time-Lapse ERT, Moment Analysis, and Numerical Modeling for Estimating the Hydraulic Conductivity of Unsaturated Rock

In recent years, geophysical techniques have been increasingly used to monitor flow and transport processes in the Earth critical zone (ECZ). Among these, electrical resistivity tomography (ERT) is a powerful tool used to predict hydrological parameters and state variables that influence the mentioned processes in the vadose zone because of the strong correlation between electrical and hydrological properties of the filtering medium. There have been many field tests considering geophysical prospecting in soils, where point scale hydrological sensors measurements are typically collected through sensors for geophysical data validation; on the contrary, when the unsaturated zone is made of hard rocks, the installation of such sensors is not a trivial issue owing to the extreme difficulties to guarantee contact between sensors and the surrounding medium. In this context, the geophysical data combined with appropriate numerical analysis techniques can effectively overcome the lack of information of the unsaturated subsurface, which is otherwise unpredictable with traditional methods. In the proposed case study, hydrogeophysical data were collected to provide a quantitative estimation of the hydraulic conductivity of sandstone through an integrated approach based on the moment analysis technique and numerical modeling.

Four-dimensional electrical resistivity imaging for monitoring pumping-induced saltwater intrusion in a coastal aquifer

Conventional views of saltwater intrusion (SWI), where a basal saline wedge extends inland below fresh groundwater, can be complicated by the influence of saltwater cells in the upper part of aquifers in areas affected by tidal cycles. Distinguishing the contribution of each saltwater source may prove fundamental for well design and resource management. Application of time-lapse electrical resistivity imaging (ERI) during a 32-h pumping test in a pristine unconfined coastal sand aquifer, affected by strong tidal ranges (>2 m), aimed to evaluate the potential of the method to characterize the source of induced SWI in four dimensions (three dimensions and time). Water level monitoring during the test revealed that at the end of pumping, the upper 2 m of the aquifer had dewatered in the vicinity of the well field, reversing hydraulic gradients between the aquifer and the sea. This induced SI, with mixing models of well head water quality suggesting that saline water contributions to total discharge rose from 4 % to 8 %. ERI results reflected dewatering through an increase in resistivity in the upper 2-6 m of the aquifer, while a decline in resistivity, relative to background conditions, occurred immediately below this, reflecting the migration of saline water through the upper layers of the aquifer to the pumping well. By contrast no change in resistivity occurred at depth, indicating no significant change in contribution from the basal saline water to discharge. Test findings suggest that future water resource development at the site should focus on close monitoring of shallow pumping, or pumping from deeper parts of the aquifer, while more generally demonstrating the value of time-lapse geophysical methods in informing coastal water resource management.

Noninvasive Monitoring of Subsurface Soil Conditions to Evaluate the Efficacy of Mole Drain in Heavy Clay Soils

Soil degradation and low productivity are among the major agricultural problems facing farmers of the newly reclaimed agricultural area in the Nile Delta region, Egypt. High content of clay and silt characterizes the soil texture of all farms in the area, while farmers still rely on the traditional mole drainage (MD) system to reduce the salinity of the farm soil. We present a comparison of innovative geo-resistivity methods to evaluate mole drains and the salinity affected clay soils. Geoelectrical surveys were conducted on three newly reclaimed farms to image the subsurface soil drainage conditions and to evaluate the efficiency of using the traditional MD systems in these heavy clay environments. The surveys included measuring the natural spontaneous potential (SP), apparent resistivity gradient (RG), and electrical resistivity tomography (ERT). Integrating the results of the three methods reduced the ambiguous interpretation of the inverted ERT models and allowed us to determine the subsurface soil structure. The inverted ERT models were suitable for locating the buried MDs and delineating the upper surface of the undisturbed clay beds. The proximity of these layers to the topsoil reduces the role played by MDs in draining the soil in the first farm and prevents the growth of deep-rooted plants in the second farm. Time-lapse ERT measurements on the third farm revealed a defect in its drainage network where the slope of the clay beds opposes the main direction of the MDs. That has completely obstructed the drainage system of the farm and caused waterlogging. The presented geo-resistivity methods show that integrated models can be used to improve the assessment of in situ sub-surface drainage in clay-rich soils.

Distributed Fuzzy Clustering Analysis of Time-Lapse Electrical Resistivity Tomography for Water Inrush Monitoring in Coal Mines

The majority of water inrush accidents in coal mines are caused by mining engineering activities. To avoid water inrush accidents, the Time-lapse Electrical Resistivity Tomography (TLERT) is applied to monitor water migration in fractured zone. A great challenge for the application of TLERT monitoring is the huge and numerous time series data sets generated by monitoring systems, which are difficult to process manually. This research proposed a distributed fuzzy clustering algorithm based on kernel function estimation to analyze TLERT images automatically. The resistivity date can be classified with different cluster centroids. The fuzzy c-means algorithm was chosen to display resistivity change. The algorithm was validated using a floor water inrush model. The results indicate that the water migration in the fractured zone can be monitored automatically and the edge of the resistivity changing area can be shown clearly.

Characterizing seasonal recharge between a river and shallow aquifer in a floodplain based on time-lapse electrical resistivity tomography

Characterizing seasonal recharge between a river and shallow aquifer in a floodplain based on time-lapse electrical resistivity tomography

3D Time-Lapse Electrical Resistivity Imaging of Rock Damage Patterns and Gas Flow Paths Resulting from Two Underground Chemical Explosions

Rock damage from underground nuclear explosions (UNEs) has a strong influence on sub-surface gas movement and on seismic waveform characteristics, both of which are used to detect UNEs. Although advanced numerical simulation capabilities exist to predict rock damage patterns and corresponding detection signals, those predictions are dependent on (generally) unknown properties of the host rock. For example, the effects of in-situ mechanical heterogeneities on the explosively generated damage/fractures that provide gas flow pathways to the surface are not well understood, due largely to the difficulty in accessing and characterizing the near-source region. In this paper we demonstrate the emerging use of electrical resistivity tomography (ERT) for imaging rock damage and gas flow patterns resulting from two relatively small-scale underground chemical explosions. Pre-explosion ERT and crosshole seismic imaging revealed a natural fracture zone within the test bed. Post-explosion imaging revealed that the damage zone was non-symmetric and was focused primarily within the pre-existing fracture zone, located 10 m above the first explosion and 5 m above the second explosion. Time-lapse ERT imaging of heated air injected into the detonation borehole revealed the primary gas flow paths to be within the upper margin of the same primary damage zone. These results point to the utility of ERT imaging for understanding rock damage and gas flow patterns under experimental conditions, and to the importance of understanding the effects of geologic heterogeneity on UNE detection signals, particularly gas surface breakthrough times.

Flow path monitoring by discontinuous time-lapse ERT: An application to survey relationships between secondary effluent infiltration and roots distribution

The infiltration of secondary treated effluent (STE) into the soil downstream of wastewater treatment plants is becoming increasingly common in a climate change context. In STE infiltration, STE is discharged onto the soil over a large surface allowing for a gradual infiltration of the water. This paper investigates a novel time-lapse electrical resistivity tomography strategy to evaluate the impact of STE infiltration on the water pathways of two planted loamy-soil trenches located in a Fluvisol region in southwestern France. The system has been monitored for 3 years using discontinuous monitoring of electrical resistivity tomography during four saline tracer tests. Results show that: 1) the new methodology has successfully highlighted the evolution of water pathways in the soil over time; 2) such evolution is in agreement with reeds root distribution in the trenches which seems to be affected by water quality i.e. sludge losses and TSS, for this study case. Indeed, for the infiltration trench receiving STE with lower pollution levels (2.2 mg TSS. L−1, 26 mg COD. L−1), the infiltration capacity is maintained over the years (4–6 mm h−1) and reed roots developed deeper in the soil. A sludge deposit present at the bottom of the second infiltration trench receiving higher pollution levels (7.2 mg TSS. L−1, 45 mg COD. L−1, plus episodic sludge release) could lead roots to develop close to the surface affecting the infiltration capacity which did not evolve over time. This work highlights the importance of long-term flow pathway monitoring in understanding the hydraulic behavior of infiltration surfaces submitted to STE.

Identification of potential artificial groundwater recharge sites in an alluvial setting: A coupled electrical resistivity tomography and sediment characterization study

Alluvial aquifers are one of the most exploited and easily accessible aquifer networks across the world, as they coincide with regions of intensive human settlements. Over the years, improper management and overexploitation of groundwater has severely stressed the natural replenishing capacity of these aquifer systems. Identification of potential artificial recharge networks and their monitoring are urgently needed for long-term sustainable groundwater management. In this study, an efficient and cost-effective strategy to locate prospective groundwater recharge zones is devised by integrating electrical resistivity tomography (ERT), sediment characterization, and temporal monitoring techniques. The efficacy of the above strategy is evaluated in the central part of Indo-Gangetic basin (IGB). Correlation of 2-D ERT models with borehole lithology and depth-wise variation of hydraulic parameters deciphered the existence of prospective regions composed of highly permeable strata within ∼6–48 m depth. In addition, the behavior of the delineated prospective regions was monitored by applying time-lapse ERT study. The comparison of time-lapse 2-D ERT models highlighted a significant reduction in post-monsoon resistivity profiles, suggesting an increase in saturation levels due to infiltration of rainwater. This study provides an integrated cost-effective approach for identification of prospective artificial recharge sites and monitoring their efficacy in replenishing shallow aquifers, thereby enabling sustainable groundwater management.

Long-term geoelectrical monitoring of landslides in natural and engineered slopes

Developments in time-lapse electrical resistivity tomography (ERT) technology are transforming our ability to monitor the subsurface due to purpose-built monitoring instruments, advances in automation and modeling, and the resulting improvements in spatial and temporal resolution. We describe the development of a novel ERT-based remote monitoring system called PRIME that integrates new low-power measurement instrumentation with data delivery, automated data processing and image generation, and web-based information delivery. Due to the sensitivity of ERT to hydrologic processes in the near surface, we focus on the application of PRIME for moisture-driven landslide monitoring. Case examples are considered of landslides in engineered and natural slopes, including those impacting geotechnical assets in rail and highways, where slope hydrology is seen to be controlled by lithology, vegetation, fissuring, and drainage structures. We conclude by taking a forward look at emerging developments in ERT monitoring relating to hardware, software and modeling, and applications.

Identifying soil-plant interactions in a mixed-age orange orchard using electrical resistivity imaging

Background and aimsThe ever-increasing demand of fresh water from irrigated agriculture and the water scarcity condition that characterizes the Mediterranean region, encourages the adoption of efficient water saving strategies. In this study, the electrical resistivity imaging (ERI) technique was applied for characterizing the mass exchange mechanisms affecting the continuous soil-plant system of heterogeneous micro-irrigated orchards.MethodsTime-lapse ERI surveys were carried out in a multi-variety and mixed-age orange orchard located in Eastern Sicily (insular Italy). The time-lapse ERI monitoring was coupled with the use of independent soil water content (SWC) measurements.Results and conclusionsA clear relationship between the soil electrical resistivity (ER) and the measured SWC changes was identified in the soil under the mixed-age orchard (with an overall coefficient of determination value of 0.63). Specifically, this study highlights the dependency of SWC dynamics as function of tree age. Overall, an increase in ER was obtained in correspondence to the soil domain where the younger trees were located ( < = 3 years). This behaviour was inferred to result from the higher soil evaporation process active at these locations, due to less vegetation cover of the younger trees and, consequently to their lesser root extension in comparison to the mature trees ( > = 8 years). On the other hand, in mature trees, characterized by denser root biomass, there were more evident decreasing patterns of ER (i.e., greater changes in SWC), due to greater transpiration rates that caused lower initial SWC under these conditions in comparison to the younger trees.

Optimization of Aquifer Monitoring through Time-Lapse Electrical Resistivity Tomography Integrated with Machine-Learning and Predictive Algorithms

In this paper, an integrated workflow aimed at optimizing aquifer monitoring and management through time-lapse Electric Resistivity Tomography (TL-ERT) combined with a suite of predictive algorithms is discussed. First, the theoretical background of this approach is described. Then, the proposed approach is applied to real geoelectric datasets recorded through experiments at different spatial and temporal scales. These include a sequence of cross-hole resistivity surveys aimed at monitoring a tracer diffusion in a real aquifer as well as in a laboratory experimental set. Multiple predictive methods were applied to both datasets, including Vector Autoregressive (VAR) and Recurrent Neural Network (RNN) algorithms, over the entire sequence of ERT monitor surveys. In both field and lab experiments, the goal was to retrieve a determined number of “predicted” pseudo sections of apparent resistivity values. By inverting both real and predicted datasets, it is possible to define a dynamic model of time-space evolution of the water plume contaminated by a tracer injected into the aquifer system(s). This approach allowed for describing the complex fluid displacement over time conditioned by the hydraulic properties of the aquifer itself.

Electrical resistivity tomography (ERT) monitoring of leachate seepage from an animal carcass waste (ACW) facility, South Korea

The South Korean government culled and buried numerous pigs to contain the 2011 outbreak of foot-and-mouth disease. Pollutants were detected in groundwater samples collected from an observation well near animal carcass waste (ACW) facilities used to dispose of some of the culled animals. Pollutants appeared to be leachate seeping through damaged shields installed in the ACW facilities prior to burial of the carcasses. Because electric conductivity of leachate is relatively low compared to fresh groundwater, we assumed that electrical resistivity tomography (ERT) is effective to investigate the spatial distribution of the conductive materials. This research demonstrated the use of 3D ERT monitoring to characterize spatial and temporal changes of the leachate. Low-resistivity anomalies <23 Ωm were distributed adjacent to the ACW facilities in time-lapse ERT images. Although the size of the initial low-resistivity anomalies was the largest the time-lapse ERT images, the average resistivities were gradually reduced. We also identified that the resistivities of the low-resistivity anomalies gradually increased, whereas the resistivities decreased away from the ACW facilities using resistivity ratio maps. It was considered that these temporal and spatial changes were related to dilution of the leachate with rainwater and the leachate migration along the northwest slope. We therefore believed that the 3D ERT monitoring could be a useful tool for detecting both the distribution and migration of the leachate.

Monitoring the perturbation zone near a foundation excavation with electrical resistivity tomography: Comparison between time-lapse 3D and 2D inversions in single-profile study

On construction sites, ground loosening can trigger land collapses or sinkholes that compromise safety. Electrical resistivity tomography (ERT) is a very useful non-destructive method to monitor subsurface ground stability during the construction of foundation pits. We used inclined drilling to construct an artificial loosening zone near a test-bed excavation pit and performed time-lapse (TL) ERT surveys along lines lying over the loosening zone. The surveys were performed before and after excavation of the pit, and after construction of the loosening zone. As performing three-dimensional (3D) inversion for the ERT data, independent 3D inversion sequences monitor subsurface changes only when an appropriate initial model is available, which can be obtained via 2D inversion of ERT data. To overcome the limitations of 3D inversion, we subjected all TL ERT data to four-dimensional inversion, with consideration of the pit, to ensure that changes were accurately detected regardless of the initial model chosen. Our inversion strategy aids interpretation of TL ERT data obtained to monitor ground loosening on construction sites.

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R2-score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(104) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(105) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈O(105)−O(107) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

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.

Time-Lapse Electrical Resistivity Tomography Imaging of Buried Human Remains in Simulated Mass and Individual Graves

Electrical resistivity has been used as a noninvasive geophysical technique for locating clandestine graves and monitoring human decay within the subsurface. Detailed studies assessing resistivity anomalies due to soil disturbances and decay products associated with graves have relied on the use of proxies, such as pigs, with limited studies using real human cadavers and simulating a mass grave setting and none assessing the anomalies in 3D. In this study, we used time-lapse 2D and quasi-3D electrical resistivity distribution measured over an experimental mass grave and individual graves containing human cadavers to assess resistivity anomalies resulting from graves and the presence of decaying human remains in them. This study is part of a novel multidisciplinary mass grave experimental study with six graves consisting of a mass grave with six human cadavers, a controlled mass grave with none, three individual graves with one human cadaver each, and a control individual grave with none. Nine parallel resistivity transects which allow us to image these graves in their 3D context were acquired prior to excavation and 2 days, 1, 2, and 6 months after burial using a dipole–dipole electrode array, a unit electrode spacing of 0.5 m, and an interprofile spacing of 1m. The value of different electrode arrays and spacings in identifying the contrast between the graves was also assessed using forward models and field data. Soil sensors were installed at different locations in the graves to monitor soil electrical conductivity, moisture content, and temperature. The results of this study show an increase in electrical resistivity 2 days after burial in all graves with human remains and the control graves, which we attribute to increased soil aeration where disturbed pores are filled with air. The resistivity decreases thereafter in graves with human remains which we attribute to the formation of conductive leachates. This study validates the potential of electrical resistivity as a forensic search tool for locating both clandestine mass and individual graves and as a noninvasive monitoring technique to support human decomposition research.