A novel MgO-mixing column was developed for deep soft soil improvement, utilizing in-situ deep mixing of MgO with soil followed by carbonation and solidification via captured CO2 injection. Its low carbon footprint and rapid reinforcement potential make it promising for ground improvement. However, a simple and cost-effective quality assessment method is lacking. This study evaluated the electrical properties of MgO-mixing columns using electrical resistivity measurements, exploring relationships between resistivity parameters and column properties such as saturation, strength, modulus, CO2 sequestration and uniformity. Microscopic analyses were conducted to elucidate the mechanisms underlying carbonation, solidification, and electrical property changes. The life cycle assessment (LCA) was performed to assess its carbon reduction benefits and energy consumption. The findings reveal that the electrical resistivity decreases rapidly with increasing test frequency, remaining constant at 100 kHz, with the average electrical resistivity being slightly higher in the upper compared to the lower section. Additionally, electrical resistivity follows a power-law decrease with increasing saturation. Both electrical resistivity and the average formation factor exhibit strong positive correlations with unconfined compressive strength (UCS) and deformation modulus, enabling predictive assessments. Furthermore, CO2 sequestration in MgO-mixing columns is positively correlated with electrical resistivity, and the average anisotropy coefficient of 0.96 indicates good column uniformity. Microstructural analyses identify nesquehonite, dypingite/hydromagnesite, and magnesite as significant contributors to strength enhancement. Depth-related changes in electrical resistivity parameters arise from variations in the amount and distribution of carbonation products, which differently impede current flow. LCA highlights the significant low-carbon advantages of MgOmixing columns

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.

Earthquakes and rainfall both cause soil damage and strength degradation of cutting slopes, resulting in increased slope instability. However, few studies have been conducted on the failure mechanisms of cutting slopes under earthquakes and rainfall. In this study, field electrical measurements were conducted to evaluate the damage to a cutting slope hit by the Yangbi Earthquake (MS = 6.4) in Yunnan Province, China. After material segmentation using the resistivity probability density statistical method, we observed several damaged areas running along the slope depth direction, forming several potential sliding surfaces. Furthermore, considering the slope damage after the earthquake, a discrete element model of the slope was developed, and the dynamic process of the gravel-soil landslide under rainfall was analyzed. Compared with low cutting slope with thin overburden sliding along one sliding surface, the results indicate that the high cutting slope with thick overburden slides along several sliding surfaces that formed by the earthquake-step sliding mods. Slope sliding can be divided into four stages: First, the slope body at the bottom area slid and accelerated firstly, while several cracks appear on the top area due to tension (initial stage and acceleration stage). Thereafter, the upper slope body gradually slides along its respective sliding surface. The body at the bottom area of the slope was pushed by that at the upper area and slid at a high velocity along the sliding surfaces due to secondary acceleration (secondary acceleration stage). Finally, the sliding velocity of the slope gradually decreases, and an accumulation is formed, entering a stable stage (deceleration stage).

This study employed geo-electrostratigraphic and hydrogeological information to model and assess subsurface structure and hydrogeological properties within a major coastal environment in Nigeria's Niger Delta region, offering a high-resolution approach to groundwater resource management. The selection of the study area was predicated on its critical residential, agricultural, and economic significance, as well as its susceptibility to hydrogeological challenges arising from rapid urbanization and industrial activities. Unlike previous studies that utilized these methods independently, this research combined different geoelectrical technologies to enhance the accuracy of subsurface characterization. The results delineated four distinct geo-layers characterized by specific resistivity values, thicknesses, and depths, providing crucial insights into groundwater infiltration, storage potential, and contamination risks. The first geo-layer (motley topsoil) had resistivity values ranging from 95.2 to 1463.7 Qm. The second layer (sandy clay) exhibited resistivity values ranging from 8.8 to 2485.1 Qm. The third layer, identified as fine sand, exhibited resistivity values ranging from 72.5 to 1332.7 Qm. The fourth layer comprised coarse sands and it exhibited a mean resistivity of 525.98 Qm, indicating a well-drained permeable formation that could serve as an additional aquifer unit. A key innovation of this study was the quantitative assessment of hydrogeological parameters, including anisotropic coefficient, transverse resistance, longitudinal conductance, and groundwater yield potential index. The anisotropic coefficient ranged from 1.0 to 1.78 (mean: 1.17), revealing minimal sediment invasion and confirming the dominance of arenaceous sediments in the Benin Formation. The groundwater yield potential index varied from 3.14 x 102 to 8.1465 x 104 Qm2, highlighting areas of significant aquifer potential. The longitudinal conductance analysis revealed that 69 % of the study area has low aquifer protectivity, underscoring the region's vulnerability to contamination. Another novel contribution was the evaluation of soil corrosivity, which has direct implications for infrastructure longevity. Results indicate that 86 % of the study area is non-corrosive, making it suitable for long-term pipeline installation, a factor rarely integrated into groundwater assessments. The study alsoadvances understanding of the Benin Formation by linking resistivity variations to arenaceous-argillitic intercalations, and this significantly influences groundwater movement and contaminant transport. By synthesizing resistivity models, hydrogeological parameters, and contamination risk assessments, this research provides a more holistic framework for sustainable groundwater management. Furthermore, this research offers a robust framework for similar hydrogeophysical assessments in other regions with comparable geological and hydrological settings. (c) 2025 Guangzhou Institute of Geochemistry, CAS. Published by Elsevier BV. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Characterization of vegetation effect on soil response is essential for comprehending site-specific hydrological processes. Traditional research often relies on sensors or remote sensing data to examine the hydrological properties of vegetation zones, yet these methods are limited by either measurement sparsity or spatial inaccuracy. Therefore, this paper is the first to propose a data-driven approach that incorporates high-temporalresolution electrical resistivity tomography (ERT) to quantify soil hydrological response. Time-lapse ERT is deployed on a vegetated slope site in Foshan, China, during a discontinuous rainfall induced by Typhoon Haikui. A total of 97 ERT measurements were collected with an average time interval of 2.7 hours. The Gaussian Mixture Model (GMM) is applied to quantify the level of response and objectively classify impact zones based on features extracted directly from the ERT data. The resistivity-moisture content correlation is established based on on-site sensor data to characterize infiltration and evapotranspiration across wet-dry conditions. The findings are compared with the Normalized Difference Vegetation Index (NDVI), a common indicator for vegetation quantification, to reveal potential spatial errors in remote sensing data. In addition, this study provides discussions on the potential applications and future directions. This paper showcases significant spatio-temporal advantages over existing studies, providing a more detailed and accurate characterization of superficial soil hydrological response.

Electrical resistivity tests can potentially be applied in loess damage testing under combined freeze-thaw cycle (FTC) and earthquake conditions, which is crucial for preventing and controlling loess landslides. However, two challenges involving loess electrical resistivity measurements and damage characterization should be addressed. To achieve loess spatial resistivity measurements in extreme environments with low-uncertainty, a novel, multichannel, four-point method utilizing flexible electrodes is proposed. For loess damage characterization, a novel fusion algorithm is developed that integrates the electrical conductivity model with a data-driven process to eliminate the influence of moisture content and temperature on resistivity. To validate this approach, loess resistivity tests and damage characterizations were conducted using a combination of FTCs and earthquakes. The results indicate that the proposed method addresses the challenge of continuous measurement, ensuring that the discrepancy between the calculated and CT test results remains within an acceptable range, where the relative error ranged from 0 to 0.15. In addition, in the top and bottom areas, where considerable soil moisture exists, the calculation error associated with the previous empirical model was reduced considerably, with the relative error primarily ranging from 0.04 to 0.44.

On 1 September 2022, a giant loess landslide occurred in Huzhu Tu Autonomous County, Qinghai Province, China. This catastrophic event brought to light a unique loess fluidisation phenomenon. In specific parts of the landslide, the loess completely transformed into a viscous, fluid-like state, whereas other parts showed a deepseated slide that retained their structural integrity. In this case, loess with different sliding patterns exhibited varying levels of mobility and destructive potential. Based on the field investigation, electrical resistivity tomography was employed to investigate the groundwater condition of the slope. Subsequently, ring-shear tests were carried out to examine the mechanical properties of the sliding zone loess under different saturation degrees and its response to rainfall as a triggering factor. The results indicate that the natural water content in the original slope was unevenly distributed, influenced by local terrain and groundwater runoff. Following the initial slide caused by cumulative rainfall, the overlying sliding material with high degree of saturation was likely to fluidise due to the increase in excess porewater pressure caused by continued shearing, ultimately resulting in flow-like movement features. In contrast, in areas with a deeper groundwater table, the initial shear could only be sustained over a short distance. This study reveals a mechanism of multiple movement patterns that may coexist in giant loess landslides.

Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of Xing'an permafrost (XAP) are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geo-environmental factors. Thus, this study introduces a new approach that incorporates four key driving factors-biotic, climatic, physiographic, and anthropogenic-by integrating multisource datasets and in situ observations. Four machine learning (ML) models [random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGB)] are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models' accuracy, ranked from highest to lowest, is as follows: RF (area under the curve (AUC) =0.88 and accuracy =0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert nonlinear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multisource datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.

Infrastructure development into peat necessitates geotechnical engineers to find a better profiling method. The conventional profiling carried out by either a localized peat augering or a borehole drilling is often subjective, lacking of details, time consuming and high cost. This paper presents results and analysis of in-situ tests that combines Electrical Resistivity Tomography (ERT) and Multichannel Analysis of Surface Wave (MASW) methods. The results show that ERT method is a good tool for delineating the boundary between peat and underlying mineral soil using their large contrast in electrical resistivity values. The ERT enables the peat thickness to be determined accurately, but lacks of information regarding peat mechanical properties. On the other hand, the MASW provides a lower contrast between the peat and the mineral soil layers compared to the ERT, but it can provide the mechanical properties of the two layers based on shear wave velocity measurements. Hence, the combination of these two methods were found to be improving the peat profiling for engineering application. The results correlate well with the existing peat auger and borehole records in the study area. The combined ERT and MASW methods is recommended to be practically used by engineers as the current best solution for peat profiling.

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.