Quantitative Two‐Layer Conductivity Inversion of Multi‐Configuration Electromagnetic Induction Measurements

A common practice used by the Germans and collaborators in World War II, as part of the Holocaust, was to use existing Jewish cemeteries as places for mass burial. Research was completed at the Old Jewish Cemetery in Riga, Latvia, the Livas Jewish Cemetery in Liepaja, Latvia, and the Zaliakalnis Jewish Cemetery in Kaunas, Lithuania. The Old Jewish Cemetery in Riga was adjacent to the Riga Ghetto and was used to bury individuals murdered in the ghetto. In Kaunas, an area of the Zaliakalnis Jewish Cemetery is devoid of grave stones, and literature sources and testimony indicate that this area was used for the mass burial of Jews from the Kaunas Ghetto and other mass killings. In Liepaja, the local Jewish Heritage Foundation believes that there are mass graves within the Livas Cemetery. Methodologies for this research include the use of a pulseEkko Pro 500-megahertz ground-penetrating radar (GPR) system. Electrical resistivity tomography (ERT) data were collected through a linear array of electrodes coupled to a direct current (DC) resistivity transmitter and receiver. Analysis of aerial photography and satellite images was also employed at each location. ERT and GPR data indicate three separate trench anomalies in the Old Jewish Cemetery in Riga. The presence of these anomalies corroborates Holocaust survivor testimony that bodies were buried in mass graves in that area. In the Zaliakalnis Jewish Cemetery in Kaunas, ERT and GPR data indicate an anomaly in the western part of the cemetery, and ERT data further indicate two other possible mass graves. In Liepaja, preliminary GPR analysis indicates an anomaly in a cleared section of the cemetery. Based on the presence of geophysical anomalies in all three cemeteries, which correlate with literature sources and Holocaust survivor testimony, there is a high probability that mass graves are present at each site. Future research directions include expanding the search areas in each cemetery, additional literature and testimony-based research, and the addition of other geophysical methodologies.

Subsurface stratigraphy interpretation often relies on sparse borehole data. Due to the inherent spatial variability and limited borehole data, uncertainty inevitably exists in the interpreted subsurface stratigraphy. Geophysical approaches such as electrical resistivity tomography (ERT) provide continuous subsurface data in the concerned cross-section. Though the reliability of the geophysical data derived is much lower than that of the solid borehole data, the continuous subsurface data obtained from geophysical investigations might be taken as a complement to the sparse borehole data in the subsurface stratigraphy interpretation. This paper proposes an innovative subsurface stratigraphy interpretation approach, which takes advantage of the high reliability of sparse borehole data and the abundance of ERT data. This approach is partially developed based on the random field approach recently advanced by the authors, and the relationship between ERT data and borehole stratigraphies is mapped with the algorithm of the support vector machine. The uncertainty of the interpreted subsurface stratigraphy arising from the random selection of the training and testing datasets (for training the mapping relationship between ERT data and stratigraphies) is analyzed by a “ bootstrapping” method. Furthermore, field tests of site investigation at four benchmark stratigraphic cross-sections are conducted, and high-quality ERT and borehole data are collected. Based on the high-quality site investigation data obtained, illustrative applications of the proposed approach are presented.

Soil compaction is a regarded as a major environmental and economical hazard, degrading soils across the world. Changes in soil properties due to compaction are known to lead to decrease in biomass and increase in greenhouse gas emissions, nutrient leaching and soil erosion. Quantifying adverse impacts of soil compaction and developing strategies for amelioration relies on an understanding of soil compaction extent and temporal variability. The main indicators of soil compaction (i.e., reduction of pore space, increase in bulk density and decrease in soil transport properties) are relatively easy to quantify in laboratory conditions but such traditional point‐based methods offer little information on soil compaction extent at the field scale. Recently, geophysical methods have been proposed to provide non‐invasive information about soil compaction. In this work, we developed an agrogeophysical modelling framework to help address the challenges of characterizing soil compaction across grazing paddocks using electromagnetic induction (EMI) data. By integrative modelling of grazing, soil compaction, soil processes and EMI resistivity anomalies, we demonstrate how spatial patterns of EMI observations can be linked to management leading to soil compaction and concurrent modifications of soil functions. The model was tested in a dairy farm in the midlands of Ireland that has been grazed for decades and shows clear signatures of grazing‐induced compaction. EMI data were collected in the summer of 2021 and autumn of 2022 under dry and wet soil moisture conditions, respectively. For both years, we observed decreases of apparent electrical resistivity at locations that with visible signatures of compaction such as decreased vegetation and water ponding (e.g., near the water troughs and gates). A machine learning algorithm was used to cluster EMI data with three unique cluster signatures assumed to be representative of heavy, moderately, and non‐compacted field zones. We conducted 1D process‐based simulations corresponding to non‐compacted and compacted soils. The modelled EMI signatures agree qualitatively and quantitatively with the measured EMI data, linking decreased electrical resistivities to zones that were visibly compacted. By providing a theoretical framework based on mechanistic modelling of soil management and compaction, our work may provide a strategy for utilizing EMI data for detection of soil degradation due to compaction.

Durusu Lake is the biggest and most important freshwater source supplying drinking water to the European side of Istanbul. In this study, electrical resistivity tomography (ERT) and transient electromagnetic (TEM) measurements were applied to detect a possible salt water intrusion into the lake and to delineate the subsurface structure in the north of Durusu Lake. The ERT and TEM measurements were carried out along six parallel profiles extending from the sea coast to the lake shore on the dune barrier. TEM data were interpreted using different 1-D inversion methods such as Occam, Marquardt, and laterally constrained inversion (LCI). ERT data were interpreted using 2-D inversion techniques. The inversion results of ERT and TEM data were shown as resistivity depth sections including topography. The sand layer spreading over the basin has a resistivity of 150–400 Ωm with a thickness of 5–10 m. The sandy layer with clay, silt, and gravel has a resistivity of 15–100 Ωm and a thickness of 10–40 m followed by a clay layer of a resistivity below 10 Ωm. When the inversion of these data is interpreted along with the hydrogeology of the area, it is concluded that the salt water intrusion along the dune barrier is not common and occurs at a particular area where the distance between lake and sea is very close. Using information from boreholes around the lake, it was verified that the common conductive region at depths of 30 m or more consists of clay layers and clay lenses.

Electromagnetic induction (EMI) systems measure the soil apparent electrical conductivity (ECa), which is related to the soil water content, texture, and salinity changes. Large‐scale EMI measurements often show relevant areal ECa patterns, but only few researchers have attempted to resolve vertical changes in electrical conductivity that in principle can be obtained using multiconfiguration EMI devices. In this work, we show that EMI measurements can be used to determine the lateral and vertical distribution of the electrical conductivity at the field scale and beyond. Processed ECa data for six coil configurations measured at the Selhausen (Germany) test site were calibrated using inverted electrical resistivity tomography (ERT) data from a short transect with a high ECa range, and regridded using a nearest neighbor interpolation. The quantitative ECa data at each grid node were inverted using a novel three‐layer inversion that uses the shuffled complex evolution (SCE) optimization and a Maxwell‐based electromagnetic forward model. The obtained 1‐D results were stitched together to form a 3‐D subsurface electrical conductivity model that showed smoothly varying electrical conductivities and layer thicknesses, indicating the stability of the inversion. The obtained electrical conductivity distributions were validated with low‐resolution grain size distribution maps and two 120 m long ERT transects that confirmed the obtained lateral and vertical large‐scale electrical conductivity patterns. Observed differences in the EMI and ERT inversion results were attributed to differences in soil water content between acquisition days. These findings indicate that EMI inversions can be used to infer hydrologically active layers.

Sand intrusions pose significant risks to river embankments due to potential flow pathways that can lead to instability during flood events. Visual inspection is a first step to recognize critical segments, but it does not deliver information about the subsurface. In this context, the electromagnetic induction (EMI) technique is a useful method for preliminary zoning at regional scale while the electrical resistivity tomography (ERT) method, widely used for hydrological purposes, is considered among the most reliable techniques for local subsurface imaging. A major sand intrusion within the levees of the Brenta River, located near Venice (northern Italy), resulted in water seeping during seasonal floods and posed severe threats to embankment stability. ERT and EMI techniques, along with geotechnical investigations, were the best survey choices to address the problem. Resistivity profiling successfully imaged the sand body geometry within and underneath the levee, and results correlated nicely with borehole stratigraphy. A first multiarray EMI device, which represented a faster and less expensive survey, was deployed to map further anomalies along nearby levees, but results were not satisfactory because the inverted profile failed to image the known intrusion. A second multiarray EMI device, with larger coil spacing, was also tested. Although it performed better in detecting the intrusion, results were still below expectations. A calibration procedure based on Pearson's coefficients and using ERT as a reference was then devised and implemented to correct the EMI data prior to carrying out inversion. The procedure was successful for both EMI data sets, leading to realistic subsurface resistivity in the inverted sections. EMI measurements could then be recovered and interpreted correctly to estimate subsurface textures. The possibility of calibrating EMI data and obtaining subsurface resistivity images comparable to standard ERT profiling is an important improvement for cost-effective EMI surveying of river embankments to mitigate flood hazards.

ERT and salinity – A method to determine whether ERT-detected preferential pathways in brackish water-irrigated soils are water-induced or an artifact of salinity

Combined application of GPR and ERT for the assessment of a wall structure at the Heptapyrgion fortress (Thessaloniki, Greece)

Integrated electromagnetic (EM) and Electrical Resistivity Tomography (ERT) geophysical studies of environmental impact of Awotan dumpsite in Ibadan, southwestern Nigeria

Mapping peat layer properties with multi-coil offset electromagnetic induction and laser scanning elevation data

We conducted a field experiment at the agricultural field site Voulund within the Danish hydrological observatory, HOBE, with the purpose of estimating recharge using geophysical methods. In September 2011, a saline tracer was added across a 142‐m2 area at the surface at an application rate mimicking natural infiltration. The movement of the saline tracer front was monitored using cross‐borehole electrical resistivity tomography (ERT); data were collected on a daily to weekly basis and continued for 1 yr after tracer application. The ERT data were inverted and corrected for temperature changes in the subsurface, and spatial moment analysis was used to calculate the tracer mass, position of the center of mass, and thereby the downwardly recharging flux. The recovered mass was underestimated by the ERT data by up to 50%. Mass balance errors are widely recognized and are a result of variable resolution of the tomographic models and smoothing applied in the inversion routine. The results were nonetheless in very good agreement with pore water samples collected and analyzed from five cores extracted within the tracer application area during the same period. Recharge during the 7.5 mo from September 2011 to the end of April 2012 was estimated to be about 500 mm using the ERT data. This value is in good accord with recharge estimates made based on drainage data from buried lysimeters located only meters away from the cross‐borehole ERT array. This suggests that long‐term automated ERT monitoring of a surface‐applied tracer is a promising technique for estimating groundwater recharge.

ABSTRACTA large‐scale electrical resistivity tomography (ERT) survey was undertaken around the Neolithic tell of Szeghalom‐Kovácshalom in southeast Hungary, covering an area of almost 6 ha. High‐resolution ERT data were collected along 28 uniformly distributed transects of variable length using the roll‐along technique. A recently presented two‐dimensional fast non‐linear resistivity inversion algorithm was used to invert the ERT data and recover the true subsurface resistivity distribution along the specific cross‐sections. The algorithm calculates and stores in an efficient manner the part of the Jacobian matrix that is actually important within the inversion procedure, thus rendering it almost 4.8 times faster than the algorithm that calculates the complete Jacobian matrix, without losing quality. The algorithm was further modified to account for any non‐standard electrode configuration. A recently established iterative algorithm for sparse least squares problems (LSMR) was incorporated for the first time into the algorithm to solve the inverse resistivity problem. The effectiveness and robustness of the LSMR solver was highlighted through the processing of all the ERT lines. The processing and evaluation of the ERT data made it possible to map the thickness of the anthropogenic layer below the surface of the tell, to outline the horizontal and vertical dimensions of the palaeochannel adjacent to the tell, and to determine the general stratigraphy of geological layers up to 10 m below the ground surface. The ERT results also were used to update an older topographic map of the site showing the course of the palaeochannel around the tell. A synthetic model verified and enhanced the conclusions based on the field data. This study illustrates the added value that a systematic ERT survey can provide in reconstructing the ancient fluvial geomorphology of a microregion as well as the depth and horizontal extent of deposits associated with human habitation at archaeological sites. Copyright © 2014 John Wiley & Sons, Ltd.

Two important mechanisms affect our ability to estimate solute concentrations quantitatively from the inversion of field-scale electrical resistivity tomography (ERT) data: (1) the spatially variable physical processes that govern the flow of current as well as the variation of physical properties in space and (2) the overparameterization of inverse models, which requires the imposition of a smoothing constraint (regularization) to facilitate convergence of the inverse solution. Based on analyses of field and synthetic data, we find that the ability of ERT to recover the 3D shape and magnitudes of a migrating conductive target is spatially variable. Additionally, the application of Archie's law to tomograms from field ERT data produced solute concentrations that are consistently less than 10% of point measurements collected in the field and estimated from transport modeling. Estimates of concentration from ERT using Archie's law only fit measured solute concentrations if the apparent formation factor is varied with space and time and allowed to take on unreasonably high values. Our analysis suggests that the inability to find a single petrophysical relation in space and time between concentration and electrical resistivity is largely an effect of two properties of ERT surveys: (1) decreased sensitivity of ERT to detect the target plume with increasing distance from the electrodes and (2) the smoothing imprint of regularization used in inversion.

Landslides occur worldwide. Global damages range in billions of dollars and cost hundreds of lives each year; Sweden is not an exception. In this study, we have acquired radiomagnetotelluric (RMT), electrical resistivity tomography (ERT) and high-resolution reflection seismic along 4 profiles with the main aim of imaging the geometry of quick clays and associated unconsolidated sediments that have caused landslides in a pilot study area in southwest Sweden. Comparison between the electrical resistivity models from the 2D inversions of RMT and ERT data with the reflections observed in the seismic data suggests a good correlation between the small-scale structures and those observed in the RMT model. However, the ERT model better delineates the resistive bedrock than the RMT model. The resistivity models are also compared with the CPT-R data collected in two geotechnical boreholes and further confirm some of the layer boundaries imaged with the acquired surface geophysical data.

<p>A full understanding of the moisture regimes in agricultural soils is critical when considering a climatic future that is potentially both drier and hotter. The study of near-surface hydrodynamics necessitates a high temporal frequency of measurements to allow the capture of fast-moving processes. In addition to this, heterogeneities in soils, coupled with spatially variable moisture contents, mean that point sensors alone can miss valuable information when studying soil hydrodynamics.</p><p>The sensitivity of Electrical Resistivity Tomography (ERT) to soil water content means that it is ideally suited to studying soil hydrodynamics. As a non-invasive technique, it is able to effectively capture spatial heterogeneities, while repeating ERT measurements at short time intervals enables the monitoring of rapid soil moisture changes.</p><p>In order to study Conservation Agriculture (CA) - an agricultural technique shown to have increased water and heat stress resilience - we collect frequent ERT measurements (≤ daily) over a long period of time (> 2 years) using PRIME - a low-cost, low-power, ERT monitoring instrument developed by the British Geological Survey. Our equipment is permanently installed at three agricultural observatories in southern Africa (Malawi, Zimbabwe, and Zambia), and is complemented by many co-located point sensors monitoring soil temperature, water content, and matric potential.</p><p>We compare the hydrodynamics - derived from ERT data - found under CA, with those under conventional agricultural tillage methods, to better understand the root of CA’s resilience to hot and dry weather conditions. We use laboratory measurements together with the high number of co-located point sensor measurements to build pedological / geophysical relationships for each site, comparing those derived from laboratory scale samples to those derived from in-field measurements.</p>