Research on conductive models of damaged soil that consider the effect of microcrack expansion (the degree of saturation and suction) is weak. By assuming an equivalent conductive path a unit series-parallel conductive model of damaged soil under environmental loads was proposed. This model shows the change in soil porosity and fractal dimension. To verify that, the soil was damaged by rainfall cycles (simulated natural drying and rainfall). Electrical measurements and X-ray microscopy tests were performed to obtain the damaged soil resistivity, porosity, and fractal dimension variation. The resistivity was calculated based on the conductive model, and the error was approximately 7.9% compared with that of the test. In addition, the soil damage variable related to soil porosity and fractal dimension was introduced, and it exhibited a logarithmic relationship with soil resistivity. Variations in soil damage during the rainfall cycles were observed. In the top layer, the soil porosity increased and the fractal dimension decreased owing to microcrack expansion, resulting in an increase in soil damage. In contrast, in the bottom layer, the soil porosity decreased and the fractal dimension increased, resulting in a decrease in soil damage due to particle migration from the top area and pore fill.

Frozen soil resistivity exhibits high sensitivity to temperature variations and ice-water distribution. The conversion of soil water content (SWC) and resistivity based on petrophysical relationships enables the characterization of spatial distribution and changes in freezing and thawing states. Monitoring ground resistivity is essential for understanding frozen soil structure and evaluating climate change and ecosystems. The previous studies demonstrate that estimating soil resistivity below zero degrees based on the empirical model has significant errors. This work proposes a capillary bundle fractal model for frozen soil resistivity estimation based on SWC hydrologic parameters. The fractal theory describes the geoelectrical features of frozen porous media through the variable pore geometry and representative elementary volume. The sensitivity analysis discusses the potential relationships between pore parameters, conductance components, and fractal geometric parameters within frozen soil resistivity and reconstructs the hysteresis separation of freeze-thaw processes. The field test application in the seasonal freeze-thaw monitoring site demonstrates that the estimated resistivity and experimental samples are consistent with the field monitoring resistivity data. By combining unified conceptual assumptions, we established the connection between electrical permeability and thermal conductivity, offering a basis for exploring coupled hydro-thermal mechanisms in frozen soil. The proposed model accurately estimates the variations in seasonal frozen resistivity, providing a reliable reference for quantitatively analyzing the mechanisms of freeze-thaw processes.

Soil erosion poses a considerable threat to ecosystem services around the world. Among these, it is extremely problematic for archaeological sites, particularly in arable landscapes where accelerated soil degradation has been widely observed. Conversely, some archaeological deposits may obtain a certain level of protection when they are covered by eroded material, thereby lessening the impacts of phenomena such as plow damage or bioturbation. As a result, detailed knowledge of the extent of colluvial deposition is of great value to site management and the development of appropriate methodological strategies. This is particularly true of battlefield sites, where the integrity of artifacts in the topsoil is of great importance and conventional metal detection (with its shallow depth of exploration) is relied upon as the primary method of investigation. Using the Napoleonic battlefield of Waterloo in Belgium as a case study, this paper explores how different noninvasive datasets can be combined with ancillary data and a limited sampling scheme to map colluvial deposits in high resolution and at a large scale. Combining remote sensing, geophysical, and invasive sampling datasets that target related phenomena across spatial scales allows for overcoming some of their respective limitations and derives a better understanding of the extent of colluvial deposition.

The Tibetan Plateau, a critical region influencing both local and global atmospheric circulation, climate dynamics, hydrology and terrestrial ecosystems, is undergoing climate-driven changes, including glacial retreat, permafrost thaw and groundwater changes. Despite its importance, implementing continuous and systematic observations has been challenging due to the area's high altitude and extreme climate conditions. In this context, seismic interferometry emerges as a cost-effective method for the continuous monitoring of subsurface structural changes driven by environmental factors and internal geophysical processes. We investigate subsurface evolution using four years of seismic data from nine stations on the northeastern Tibetan Plateau, by applying coda wave interferometry across multiple frequency bands. Our findings highlight seismic velocity changes within the frequency bands 5-10, 0.77-1.54, and 0.25-0.51 Hz, revealing depth-dependent seasonal and long-term changes. Near-surface and deeper strata exhibit similar seasonal patterns, with velocities increasing in winter and decreasing in summer driven by changes in hydrological processes, while intermediate ice-water phase strata show contrasting behaviour due to thermal elastic strain. Long-term trends suggest that the upper subsurface layer is affected by melting water and precipitation originating from Kunlun Mountains, whereas deeper layer reflect groundwater level variations influenced by climate change and human activities. This study provides insights into the environmental evolution of the Tibetan Plateau and its impact on managing local groundwater resources.

Slope failures are an ongoing global threat leading to significant numbers of fatalities and infrastructure damage. Landslide impact on communities can be reduced using efficient early warning systems to plan mitigation measures and protect elements at risk. This manuscript presents an innovative geophysical approach to monitoring landslide dynamics, which combines electrical resistivity tomography (ERT) and low-frequency distributed acoustic sensing (DAS), and was deployed on a slope representative of many landslides in clay rich lowland slopes. ERT is used to create detailed, dynamic moisture maps that highlight zones of moisture accumulation leading to slope instability. The link between ERT derived soil moisture and the subsequent initiation of slope deformation is confirmed by low-frequency DAS measurements, which were collocated with the ERT measurements and provide changes in strain at unprecedented spatiotemporal resolution. Auxiliary hydrological and slope displacement data support the geophysical interpretation. By revealing critical zones prone to failure, this combined ERT and DAS monitoring approach sheds new light on landslide mechanisms. This study demonstrates the advantage of including subsurface geophysical monitoring techniques to improve landslide early warning approaches, and highlights the importance of relying on observations from different sources to build effective landslide risk management strategies.

Evaluating the mechanical properties of deep soil mixing (DSM) requires destructive borehole coring because they are mainly situated underground. Although surface wave method offers potential for quality assurance, its complexity arises from multi-mode phenomena and the need to re-evaluate inversion results, often necessitating manual interpretation. This paper presents a data-driven surface wave framework to retrieve field DSM profile Vs over time, incorporating a mode-free forward operator and the Monte Carlo Tree Search (MCTS) inversion. Validations using synthetic data affirmed the framework's accuracy and efficiency in tracking subsurface shear wave velocity. In a real-world DSM site, the proposed method successfully captures the mechanical properties evolution across two DSM layers over the curing period, aligning well with site investigations and borehole coring. This pioneering monitoring framework integrates geotechnical engineering with geophysics expertise, underscoring the value of non-destructive seismic methods for measuring subsurface property evolution.

Mega retrogressive thaw slumps (MRTS, >10(6) m(3)) are a major threat to Arctic infrastructure, alter regional biogeochemistry, and impact Arctic carbon budgets. However, processes initiating and reactivating MRTS are insufficiently understood. We hypothesize that MRTS preferentially develop a polycyclic behavior because the material is thermally and mechanically prepared for subsequent generation failure. In contrast to remote sensing, geophysical reconnaissance reveals the inner structure and relative thermal state of MRTS decameters beneath slump surfaces, potentially controlling polycyclicity. Based on their life cycle development, five (M)RTS were studied on Herschel Island, an MRTS hotspot on the Canadian Beaufort coast. We combine >2 km of electrical resistivity tomography (ERT), 500 m of ground-penetrating radar (GPR) and annual monitoring of headwall retreat from 2004 to 2013 to reveal the thermal state, internal structure, and volume loss of slumps. ERT data were calibrated with unfrozen-frozen transitions from frost probing of active layer thickness and shallow boreholes. In initial stage MRTS, ERT displays surficial thermal perturbations a few meters deep, coincident with recent mud pool and mud flow development. In early stage polycyclic MRTS, ERT shows decameter deep-reaching thermal perturbations persisting even 300 years after the last activation. In peak-stage polycyclic MRTS, 3D-ERT highlights actively extending deep-reaching thermal perturbations caused by gully incisions, mud slides and mud flows. GPR and headwall monitoring reveal structural disturbance by historical mud flows, ice-rich permafrost, and a decadal quantification of headwall retreat and slump floor erosion. We show that geophysical signatures identify long-lasting thermal and mechanical disturbances in MRTS predefining their susceptibility to polycyclic reactivation.

Characterizing permafrost is crucial for understanding the fate of arctic and subarctic archaeological archives under climate change. The loss of bio-physical integrity of archaeological sites in northern regions is still poorly documented, even though discontinuous permafrost is particularly vulnerable to global warming. In this study, we documented the spatial distribution of the permafrost-supported Inuit archaeological site Oakes Bay 1 on Dog Island (Labrador, Canada) while employing a novel approach in northern geoarchaeology based on non-invasive geophysical methods. ERT and GPR were successfully used to estimate active layer thickness and image permafrost spatial variability and characteristics. The results made it possible to reconstruct a conceptual model of the current geocryological context of the subsurface in relation to the site topography, hydrology, and geomorphology. The peripherical walls of Inuit semi-subterranean sod houses were found to contain ice-rich permafrost, whereas their central depressions were identified as sources of vertical permafrost degradation. The geophysical investigations were used to classify the permafrost at Oakes Bay 1 as climate-driven, ecosystem-protected permafrost that cannot regenerate under current climate conditions. This work highlights how the permafrost at Oakes Bay 1 is currently affected by multi-point thermal degradation by both conduction and advection, which makes it highly sensitive to climate warming.

Electrical resistivity tomography is a non-destructive and efficient geophysical exploration method that can effectively reveal the geological structure and sliding surface characteristics inside landslide bodies. This is crucial for analyzing the stability of landslides and managing associated risks. This study focuses on the Lijiazu landslide in Zhuzhou City, Hunan Province, employing the electrical resistivity tomography method to detect effectively the surrounding area of the landslide. The resistivity data of the deep strata were obtained, and the corresponding geophysical characteristics are inverted. At the same time, combined with the existing drilling data, the electrical structure of the landslide body is discussed in detail. The inversion results reveal significant vertical variations in the landslide body's resistivity, reflecting changes in rock and soil physical properties. Combined with geological data analysis, it can be concluded that the sliding surface is located in the sandy shale formation. Meanwhile, by integrating various geological data, we can conclude that the landslide is currently in a creeping stage. During the rainy season, with rainfall infiltration, the landslide will further develop, posing a risk of instability. It should be promptly addressed through appropriate remediation measures. Finally, based on the results of two-dimensional inversion, this article constructs a three-dimensional surface morphology of the landslide body, which can more intuitively compare and observe the internal structure and material composition of the landslide body. This also serves as a foundation for the subsequent management and stability assessment of landslides, while also paving the way for exploring new perspectives on the formation mechanisms and theories of landslides.

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.