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.

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.

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.

A theory for modelling the evolution of elastic moduli of grain packs under increasing pressure is combined with a method that accounts for the presence of fine-grained particles to develop a new conceptual framework for computing the seismic velocities of compacting sediments. The resulting formulation is then used to construct a seismic velocity model for California's Central Valley. Specifically, a set of 44 sonic logs from the San Joaquin Valley are combined with soil textural data to derive the 3-D velocity variations in the province. An iterative quasi-Newton minimization algorithm that allows for bounded variables provided estimates of the nine free parameters in the model. The estimates low- and high-pressure exponents that resulted from the fit to the sonic log velocities are close to 1/2 and 1/3, respectively, values that are observed in laboratory experiments. Our results imply that the grain surfaces are sufficiently rough that there is little or no slip between grains. Thus, the deformation may be modelled using a strain energy function or free energy potential. The estimated Central Valley velocity model contains a 27 per cent increase in velocity from the surface to a depth of 700 m. Lateral variations of around 4 per cent occur within the layers of the model, a consequence of the textural heterogeneity within the subsurface.