The accuracy of soil moisture retrieval based on traditional microwave remote sensing models in the Qinghai Tibet Plateau (QTP) is unstable due to its unique plateau climate. However, considering the impact of multiple multi-scale factors effectively improves the accuracy and stability of soil moisture inversion. This article uses Sentinel-1 and seasonal climate data to analyze factors and influencing mechanisms of soil moisture in the QTP. First, an artificial neural network (ANN) was used to conduct a significance analysis to screen significant influencing factors to reduce the redundancy of the experimental design and insert information. Second, the normalization effect of each factor on the soil moisture inversion was determined, and the factors with significant normalization influences were input to fit the model. Third, different fitting methods combined the semi-empirical models for soil moisture inversion. The decision tree Chi-square Automatic Interaction Detector (CHAID) analyzed the model accuracy, and the Pearson correlation coefficient between the sample and measured data was tested to further validate the accuracy of the results to obtain an optimized model that effectively inverts soil moisture. Finally, the influencing mechanisms of various factors in the optimization model were analyzed. The results show that: (1) The terrain factors, such as elevation, slope gradient, aspect, and angle, along with climate factors, such as temperature and precipitation, all have the greatest normalized impact on soil moisture in the QTP. (2) For spring (March), summer (June), and autumn (September), the greatest normalized factor of soil moisture is the terrain factor. In winter (December), precipitation was the greatest factor due to heavy snow cover and permafrost. (3) Analyzing the impact mechanism from various factors on the soil moisture showed a restricted relationship between the inversion results and the accuracy of the power fitting model, meaning it is unsuitable for general soil moisture inversion. However, among the selected models, the accuracy of the linear fit was generally higher than 79.2%, the Pearson index was greater than 0.4, and the restricted relationship between the inversion results and accuracy was weak, making it suitable for the general inversion of soil moisture in the QTP.

Accurate determination of the physico-mechanical parameters of rock and soil masses is fundamental to the quantitative stability analysis and engineering mitigation of open-pit mine slopes. However, existing studies often rely on generalized parameters and lack systematic empirical data based on full-hole in situ core sampling to quantitatively verify the link between microscopic mineralogy and macroscopic instability. To address this gap, this study investigates the mineral composition, microstructure, and hydro-mechanical behavior of geotechnical materials, using the XG Open-pit Coal Mine in Inner Mongolia as a case study. Field drilling and sampling with a cumulative depth of 1500.7 m were conducted, combined with systematic laboratory tests. The results reveal significant lithological heterogeneity within the mining area. Specifically, hard rocks (e.g., fine sandstone) constitute the stable framework of the slope, whereas mudstones rich in hydrophilic clay minerals, along with low-strength coal seams, form potential weak sliding interfaces. Quantitative X-ray Diffraction (XRD) analysis reveals that the weak mudstone layers contain up to 32.4% hydrophilic expansive minerals (montmorillonite and illite/smectite). Scanning Electron Microscopy (SEM) and slake durability tests demonstrate that the mudstone is characterized by well-developed micropores (1–2 μm) and loose cementation. Theoretical analysis indicates that upon saturation, the strength of these weak layers is reduced by over 40%, causing the factor of safety (FoS) to drop from a stable 1.48 to a critical 0.89. Based on these findings, the slope instability mechanism driven by “Stiffness Mismatch and Hydro-Weakening” is elucidated. Consequently, targeted reinforcement and drainage measures are proposed to provide a scientific basis for safe mining operations.

To investigate the mechanism by which the rheological properties of drilling fluids affect the stability of the wellbore in brittle mud shale, this study systematically examines the influence of drilling fluids with different rheological properties on the hydration dispersion and rock strength of brittle mud shale through a series of laboratory experiments, including thermal rolling tests and uniaxial compressive strength tests on core samples. The results reveal that for weakly dispersible brittle mud shale, the rheological properties of drilling fluids have a minor effect on hydration dispersion, with rolling recovery rates consistently above 90%. However, the rheological properties of drilling fluids significantly influence the strength of brittle mud shale, and this effect is coupled with multiple factors, including rock fracture intensity index, soaking time, and confining pressure. Specifically, as the viscosity of the drilling fluid increases, the reduction in rock strength decreases; for instance, at 5 MPa confining pressure with an FII of 0.46, the strength reduction after 144 h was 69.8% in distilled water (from an initial 133.2 MPa to 40.2 MPa) compared to 36.3% with 3# drilling fluid (from 133.2 MPa to 88.7 MPa, with 100 mPa·s apparent viscosity). Both increased soaking time and confining pressure exacerbate the reduction in rock strength; a 5 MPa confining pressure, for example, caused an additional 60.9% strength reduction compared to 0 MPa for highly fractured samples (FII = 0.46) in distilled water after 144 h. Rocks with higher fracture intensity indices are more significantly affected by the rheological properties of drilling fluids. Based on the experimental results, this study proposes a strength attenuation model for brittle mud shale that considers the coupled effects of fracture intensity index, soaking time, and drilling fluid rheological properties. Additionally, the mechanism by which drilling fluid rheological properties influence the strength of brittle mud shale is analyzed, providing a theoretical basis for optimizing drilling fluid rheological parameters and enhancing the stability of wellbores in brittle mud shale formations.

The prevalence of ground fissures in deformation-affected areas has intensified, presenting serious risks to both operational safety and the local natural environment. Fissures in these disturbed terrains are typically characterized by elongated morphologies and large-scale variations, which pose substantial challenges to accurate feature extraction. To address these complexities, this paper proposes a semantic segmentation network termed MGF-UNet. In the shallow layers, we integrate multi-scale feature sensing (MFS) and grouped efficient multi-scale attention (EMA) to sharpen anisotropic textures and boundary details under high-resolution representations. For the deeper layers, a Token-Selective Context Transformer (TSCT) is designed to perform selective global modeling on high-level semantic features, effectively capturing long-range dependencies while preserving the structural integrity of elongated fissures. Meanwhile, we employ feature-wise linear modulation (FiLM) to derive pixel-wise affine parameters from shallow structures, which pre-modulate deep features and strengthen cross-level interactions. In the decoder, a Fourier transform-based adaptive feature fusion (AFF) module suppresses background noise and enhances boundary contrast, followed by cross-scale aggregation for final prediction.Benchmark tests conducted on the mining-area fissure dataset (MFD) and road-based datasets demonstrate that MGF-UNet achieves an accuracy of 78.2%, a Dice score of 81.4%, and an IoU of 68.6%, outperforming existing mainstream networks. The results confirm that MGF-UNet provides an effective solution for automatic fissure extraction in deformation-prone environments, offering significant potential for geohazard monitoring and ecological restoration.