Summary Electrodes installed on active landslides and vulnerable earthworks to monitor changes in resistivity associated with moisture dynamics can be subject to movement. This affects the geoelectrical data and leads to errors in the resulting Electrical Resistivity Tomography (ERT) images. This paper demonstrates the selection of appropriate ERT measurements to provide sensitivity to electrode displacements in both directions on a plane. A combination of linear and equatorial dipole-dipole measurements are considered, which permit use on rectangular grids of any aspect ratio. A Gauss-Newton inversion scheme is developed that allows for the incorporation of constraints based on the magnitude and direction of movement. The effects of the constraints are demonstrated using synthetic data designed to simulate a planned laboratory experiment. In the presence of realistic levels of data noise and uncertainty in the preferred directions of movement, the inversion method is able to recover the displacements of the electrodes with a root-mean-square misfit of less than 1% of the electrode spacing.
Abstract Electrical resistivity tomography (ERT) is a minimally invasive geophysical method that produces a model of subsurface resistivity from a large number of electrical resistance measurements. Strong resistivity contrasts usually exist between frozen and unfrozen earth materials, making ERT an effective and increasingly utilized tool in permafrost research. In this paper, we review more than 300 scientific publications dating from 2000 to 2022 to identify the capabilities and limitations of ERT for permafrost applications. The annual publication rate has increased by a factor of 10 over this period, but several unique challenges remain, and best practices for acquiring, processing, and interpreting ERT data in permafrost environments have not been clearly established. In this paper, we make recommendations for ERT surveys of permafrost and highlight recent advances in the field, with the objective of maximizing the utility of existing and future surveys.
Landslides are a major natural hazard, threatening communities and infrastructure worldwide. The mitigation of these hazards relies on the understanding of their causes and triggering processes, which depends directly on soil properties, land use, and their changes over time. In this study, we propose a novel framework to estimate the probability of failure in highly developed urban areas. The framework combines remote sensing and geophysical data to estimate soil properties and land covers. Such estimate properties are then integrated into a hydro-geomechanical model to provide a robust estimate of the probability of failure. To assess the importance and sensitivity of the input parameters to the probability of failure assessment, a sensitivity analysis was performed on the seven main parameters (density, friction angle, cohesion, soil thickness, slope, water recharge and saturated hydraulic conductivity) of the hydro-geomechanical model. Slope angle, soil thickness and cohesion are shown to be the most important parameters. While the slope angle can be derived from high-resolution digital elevation models, soil thickness and cohesion cannot be assessed. To incorporate the variability of these two parameters into the model, seismic noise measurements were performed to estimate soil thickness. Supervised classification of remote sensing data was used to map vegetation type and related root cohesion, which can impact the cohesion significantly. The results show that slopes with relatively thick soil layers (above 2 m) have up to four times higher probability of failure. Slopes with tall vegetation cover, and hence comparably high root cohesion, reduce the probability of failure, particularly when the soil layer is relatively thin (< 3 m). The developed approach makes use of rapid to acquire geophysical and easily to obtain remote sensing data, and hence is transferable to other study sites. This approach may be of particular importance to areas of active vegetation management that may cause considerable changes in landslide hazard maps.
Summary We present results of a laboratory study of novel electrical resistivity tomography (ERT) sensor materials, whose performance has been assessed in terms of suitability for long-term geoelectrical monitoring. The study has addressed concerns over the longevity of buried ERT sensors required to support nuclear decommissioning at the Sellafield Site in the UK. Electrodes made from three candidate materials and installed in a bentonite grout were subjected to accelerated measurements and electrochemical analyses were carried out on both pristine and used electrodes after extraction from the laboratory tanks. Electrical contact resistance showed significantly different behaviour for stainless steels compared with platinised titanium. Pt-Ti sensors displayed outstanding properties and their stability under operational conditions was remarkable. Their susceptibility to ERT noise, which was expected to be worse due to their higher nobility, was only marginally greater than that of stainless steels. No tangible advantage in terms of electrical performance was found for using higher-grade varieties of stainless steel over a conventional 316L-based design. Crucially, both steel types were affected by the growth of carbonate scales when buried in bentonite. This fundamental process may well be (at least partially) responsible for the frequently encountered increase in contact resistance of stainless-steel electrodes over time.
Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.
Summary Wetlands or groundwater dependent ecosystems in general provide vital habitats for diverse aquatic and terrestrial flora and fauna. Such systems usually show a highly complex hydrological regime and are very sensitive to environmental changes. Thus non-invasive methods have to be used to investigate those processes. This paper presents the application of 2D geoelectrical monitoring to such environments which is, due to its high sensitivity to changes in moisture content and pore water resistivity, aiding in improving our hydrological understanding of these systems. After correcting the resistivity data for the seasonal temperature variations, our results highlight the need to divide the alluvium into two hydrological layers showing different characteristics. While the uppermost layer shows significant responses from biogeochemical cycling, with decreasing resistivities during spring and summer, the lower part of the alluvium shows increasing resistivities due to upwelling of more resistive pore waters from the underlying gravels. These processes, and thereby also the resistivity changes, were proven by sensors at different locations and depths, showing the same results, but with higher accuracy and sampling rate.
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