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Fluid flow through fractured media is typically governed by the distribution of fracture apertures, which are in turn governed by stress. Consequently, understanding subsurface stress is critical for understanding and predicting subsurface fluid flow. Although laboratory‐scale studies have established a sensitive relationship between effective stress and bulk electrical conductivity in crystalline rock, that relationship has not been extensively leveraged to monitor stress evolution at the field scale using electrical or electromagnetic geophysical monitoring approaches. In this paper we demonstrate the use time‐lapse 3‐dimensional (4D) electrical resistivity tomography to image perturbations in the stress field generated by pressurized borehole packers deployed during shear‐stimulation attempts in a 1.25 km deep metamorphic crystalline rock formation. Plain Language Summary Time‐lapse electrical geophysical sensing is used to image 3D changes in rock stress generated by an isolated and pressurized interval of a borehole in a deep, dense, fractured rock formation. Key Points Remotely monitoring stress is challenging but important for relating geomechanical behavior to flow pathways during energy production Bulk electrical conductivity is sensitive to stress in crystalline rock Time‐lapse electrical resistivity tomography can be used to remotely monitor 3D changes in effective stress

Rocky desertification is a significant threat in the karst regions of southwest China. Studies of soil distribution can contribute to protecting and recovering the fragile karst ecosystem that is prevalent in this region. With an underlying aim of being able to assess soil stocks in karstic environments, this study evaluates the use of electrical resistivity tomography (ERT) for delineating the soil–rock interface. Using a synthetic model (that recognizes the three-dimensional nature of the subsurface), experiments are performed to assess the impact of measurement errors and measurement configuration on recovery of the interface. The inverted results show that the accuracy of the delineation of the soil–rock interface decreases with the increase of measurement error and dipole spacing. The results also show the importance of reliable estimation of measurement errors. Field-based applications of ERT at five exposed profiles in southwest China are also reported. For the field data, three-dimensional modelling was necessary to account for the exposed face. The field experiments show that ERT can be effective at delineating the interface between soil and bedrock, but resolution can be limited due to the scale of features or lack of contrast between soil and bedrock. The method shows great promise as a means of assessing, in a non-invasive manner, the soil–bedrock interface, and, perhaps, more significantly, quantifying estimates of total soil stocks, as we seek to quantify the vulnerability or resilience of this important landscape to anthropogenic and natural stresses.

The mechanical strength and electrical conductivity (EC) in Al–Mg–Si 6xxx conductor alloys are key characteristics but mutually exclusive properties. This review paper critically elaborates the methods to enhance the mechanical strength and electrical conductivity. These methods include an optimization of the main alloying elements (Mg and Si), the addition of the other alloying elements, melt treatment, applying the modified thermomechanical treatments, and maximizing the area reduction (severe plastic deformation) in the wire drawing. Besides, the impact of the various microstructural features on the mechanical and electrical properties is addressed in detail using strength and electrical resistivity models. A principal conclusion drawn in this review is that the strength ought to be enhanced by creating barriers (such as sessile dislocations and precipitates) for dislocation movement while having a less detrimental effect on the electrical conductivity. Considering the potential of Al–Mg–Si conductor alloys, future directions are outlined. Graphical abstract

Light non-aqueous phase liquids (LNAPLs) are commonly used in industrial processes, and they are well known for their potential to contaminate groundwater and their toxic effects on ecosystems. The adequate delineation of contaminant plume distribution is critical for the effective remediation of contaminated sites and aquifers. Electrical resistivity tomography (ERT) surveys on LNAPL contaminated soils have shown great potential in this regard. In this study in China, six ERT profiles were conducted at a former perfumery plant with a benzene and ethylbenzene spill history to evaluate whether ERT could be used to delineate the distribution of the LNAPL plume beneath the plant. Based on the survey results, the electrical resistivity was consistent with borehole sampling results, where high resistivity corresponded to increased LNAPL concentration. A linear relationship was built between resistivity and contaminant concentration, with a threshold value of 18 Ω·m used to identify contaminated areas. It was possible to construct a detailed three-dimensional characterization of the LNAPL distribution. In addition, four local sites were excavated to verify the results of the ERT profiles. The contamination sources were further categorized into four types that were considered useful for the selection of remediation strategies. In conclusion, ERT was an effective non-invasive technique for delineating LNAPL plume distribution at a high resolution.

Permafrost is a widespread phenomenon in the cold regions of the globe and is under‐represented in global monitoring networks. This study presents a novel low‐cost, low‐power, and robust Autonomous Electrical Resistivity Tomography (A‐ERT) monitoring system and open‐source processing tools for permafrost monitoring. The processing workflow incorporates diagnostic and filtering tools and utilizes open‐source software, ResIPy, for data inversion. The workflow facilitates quick and efficient extraction of key information from large data sets. Field experiments conducted in Antarctica demonstrated the system's capability to operate in harsh and remote environments and provided high‐temporal‐resolution imaging of ground freezing and thawing dynamics. This data set and processing workflow allow for a detailed investigation of how meteorological conditions impact subsurface processes. The A‐ERT setup can complement existing monitoring networks on permafrost and is suitable for continuous monitoring in polar and mountainous regions, contributing to cryosphere research and gaining deeper insights into permafrost and active layer dynamics. Plain Language Summary Permafrost, frozen ground in cold regions, has significant impacts on the global environment. Monitoring of permafrost is crucial because it influences the global carbon cycle, hydrology, contaminant movement, and ecosystem stability. However, current monitoring systems have limitations, particularly in remote regions like Antarctica. To tackle this challenge, a new monitoring system, Autonomous Electrical Resistivity Tomography (A‐ERT), was introduced. A‐ERT is a geophysical technique that employs electrical signals to study ground freezes and thaws with high precision over time. Alongside this, open‐source processing tools were developed to process obtained A‐ERT data and efficiently extract essential information from large data sets. The developed A‐ERT system is robust, low‐cost, low‐power, and designed to operate in harsh conditions. Tested in Antarctica, our findings show that A‐ERT data combined with processing pipelines offers a valuable tool for examining freezing and thawing processes in extreme environments. The proposed setup can contribute to a network of autonomous permafrost monitoring systems, important for cryosphere research and advancing our understanding of climate change's impact on permafrost dynamics. Key Points We present a robust low‐cost Autonomous Electrical Resistivity Tomography system for permafrost monitoring in polar and mountainous regions We introduce an open‐source tool for processing and inverting large data sets, enabling quick and efficient extraction of key information Field experiments conducted in Antarctica show high‐temporal‐resolution imaging of ground freezing and thawing dynamics

Mining operations generate large amounts of wastes which are usually stored into large-scale storage facilities which pose major environmental concerns and must be properly monitored to manage the risk of catastrophic failures and also to control the generation of contaminated mine drainage. In this context, non-invasive monitoring techniques such as time-lapse electrical resistivity tomography (TL-ERT) are promising since they provide large-scale subsurface information that complements surface observations (walkover, aerial photogrammetry or remote sensing) and traditional monitoring tools, which often sample a tiny proportion of the mining waste storage facilities. The purposes of this review are as follows: (i) to understand the current state of research on TL-ERT for various applications; (ii) to create a reference library for future research on TL-ERT and geoelectrical monitoring mining waste; and (iii) to identify promising areas of development and future research needs on this issue according to our experience. This review describes the theoretical basis of geoelectrical monitoring and provides an overview of TL-ERT applications and developments over the last 30 years from a database of over 650 case studies, not limited to mining operations (e.g., landslide, permafrost). In particular, the review focuses on the applications of ERT for mining waste characterization and monitoring and a database of 150 case studies is used to identify promising applications for long-term autonomous geoelectrical monitoring of the geotechnical and geochemical stability of mining wastes. Potential challenges that could emerge from a broader adoption of TL-ERT monitoring for mining wastes are discussed. The review also considers recent advances in instrumentation, data acquisition, processing and interpretation for long-term monitoring and draws future research perspectives and promising avenues which could help improve the design and accuracy of future geoelectric monitoring programs in mining wastes.

Hydrocarbon contaminants can enter the soil for various reasons and in addition to the potential for groundwater pollution, altering the characteristics of the soil, endangering already-existing structures on the soil, and endangering the health of living organisms, they can remain in the soil for a long time and have long-term effects on the environment. Therefore, it is crucial to manage, control, and treat the contaminated soil. To detect and monitor the contamination is a prerequisite of management and planning for the control and treatment of contaminated soil. One technique that has established its effectiveness in this area is electrical resistivity tomography (ERT). Despite the previous studies that demonstrate the proper performance of this method in identifying crude oil contamination in the soil, the accuracy of this method in identifying different concentrations of contaminants and the impact of soil moisture on the performance of this method remain two open questions that require further research. In this study, laboratory tests were conducted to answers to the two concerns mentioned above as well as the potential for developing an alternative technique of sampling and chemical testing to determine the contaminant concentration based on one-dimensional electrical resistivity. The findings of this study demonstrated that, despite this method’s good performance in locating crude oil contamination in sandy soil, it cannot be used to locate pollutants with low concentrations. Additionally, 1D tests along with the pseudo-section obtained from ERT were suggested as a suitable alternative (cheaper and faster) for sampling and chemical tests to identify the pollutant concentration and also the increase in soil moisture increases the minimum pollutant concentration that can be detected by this method.

Landslide damming is a widespread phenomenon worldwide and significantly affects the evolution of fluvial landscapes. However, it is rarely witnessed from an antiquities perspective, and the case for observing their internal structure is challenging. We attempt to visualize the subsurface structure and understand the likely breaching mechanism of the late Pleistocene Diexi gigantic landslide dam (longevity of ~ 10 ka), using electrical resistivity tomography (ERT) method. Eight ERT measurements on the Diexi dam body revealed high resistivity zones near the periphery and lower resistivity zones in the middle portion of the profiles. Geomorphological mapping based on the LiDAR data determined the boundary of the landslide. Field investigation found that zones of low resistivity were connected to a ditched gully. Because breaching such an enormous lake with a total area of 21.4 km2 dammed by a gigantic landslide body with intact rocks was not likely by overtopping alone. The authors postulate that differential seepage of water from the gullies through the landslide debris could have accelerated the undercutting erosion of the otherwise stable Diexi dam. Utilizing geophysical techniques, along with field geomorphology works, can provide valuable information on the evolution of a gigantic paleo-landslide dam, which has real implications for the stability evaluation and forecast of future landslide dams.

Determining soil hydraulic properties is complex, posing ongoing challenges in managing subsurface and agricultural practices. Electrical resistivity tomography (ERT) is an appealing geophysical method to monitor the subsurface due to its non‐invasive, easy‐to‐apply and cost‐effective nature. However, obtaining geoelectrical tomograms from raw measurements requires the inversion of an ill‐posed problem, which causes smoothing of the actual structure. Furthermore, the spatial resolution is determined from the distances in the electrode placement, thus inherently upscaling the obtained structure. This study explores the applicability of physics‐informed neural networks (PINNs) for upscaling permeability and petrophysical relations and monitoring water dynamics at heterogeneous soils using time‐lapse geoelectrical data. High‐resolution numerical simulations mimicking water infiltration were used as benchmarks. Synthetic ERT surveys with electrode spacing 10 times larger than the numerical model resolution were conducted to provide 2D electrical tomograms. The tomograms were fed to a PINNs system to obtain the permeability, petrophysical relations, and water content maps. An additional PINNs system incorporating water content measurements was trained to examine measurement sensitivity. Results have shown that the PINNs system could produce reliable results regarding the upscaled permeability and petrophysical relations fields. Water dynamics at the subsurface was accurately predicted with an average error of ∼3%. Adding water content measurements to PINNs training improved the system outcomes, mainly at the ERT low sensitivity zones. The PINNs system reduced water saturation errors by more than 30% compared to the common practice of directly translating the geoelectrical tomograms to water saturations using known, homogeneous petrophysical relations. Key Points Physics‐informed neural networks were applied for simultaneously upscaling heterogeneous soil physical properties Training data relied on inverted 2D geoelectrical tomograms The PINNs system was able to reproduce the upscaled water saturation maps during an infiltration scenario

Quantitative estimates of hydrological state variables using electrical or electromagnetic geophysical methods are systematically biased by overlooked heterogeneity below the spatial scale resolved by the method. We generalize the high‐salinity asymptotic limit of electrical conduction in porous media at the continuous (e.g., Darcy) scale, by introducing a new petrophysical parameter, the mixing factor M, which accounts for the effect of fluid conductivity heterogeneity on the equivalent electrical conductivity tensor; it is expressed in terms of the volume‐average of the product of mean‐removed fluid conductivity and electric fields. We investigate the behavior of M for static and evolving fluid conductivity scenarios. Considering 2‐D ergodic log‐normal random fields of fluid conductivity, we demonstrate, in absence of surface conductivity, that observing the components of the M‐tensor allows univocally determining the variance and anisotropy of the field. Further, time‐series of the M‐tensor under diffusion‐limited mixing allows distinguishing between different characteristic temporal scales of diffusion, which are directly related to the initial integral scales of the salinity field. Under advective‐diffusive transport and for a pulse injection, the time‐series of M have a strong dependence on the Péclet number. Since M is defined in the absence of surface conductivity, we investigate how to correct measurements for surface conductivity effects. The parameter M provides conceptual understanding about the impact of saline heterogeneity on electrical measurements. Further work will investigate how it can be incorporated into hydrogeophysical inverse formulations and interpretative frameworks. Plain Language Summary Electrical and electromagnetic geophysical methods provide information about the spatio‐temporal distribution of average electrical conductivity of porous media. This property is affected by the transport of electrically conductive solutes, which unfolds over a wide range of spatial scales. However, when translating electrical data into solute concentration, almost all studies to date have ignored solute heterogeneity below the averaging volume inherent to geophysical measurements or modeling, leading to unphysical results. We introduce the mixing factor M, an electrical parameter that links small‐scale solute heterogeneity and average electrical conductivity, via a closed‐form expression depending on the small‐scale features of electric and solute concentration fields. We show that observation of the M‐tensor allows recovering the variance and anisotropy of the solute field in a time‐static setting. For diffusion‐limited transport, the time‐series of M help distinguishing the initial length scales of the fields, whereas for advective‐diffusive transport, these data help distinguishing the Peclet number. The presented framework helps to decode information about solute heterogeneity that is contained in geoelectrical measurements, while also avoid making biased hydrological estimates. Future venues of research will investigate how to incorporate M in available (hydro)geophysical modeling workflows. Key Points Petrophysical parameter generalizing the high‐salinity limit of electrical conduction for heterogeneous fluid conductivity in porous media Formal expression for M provides framework to interpret impact of small‐scale fluid conductivity heterogeneity on electrical measurements The mixing factor M depends on geostatistical properties of solute concentration fields and thus on transport characteristics