Xuwen Tian

Under global warming conditions, assessing snow avalanche susceptibility and identifying critical influencing factors in the snow-covered regions of the Tibetan Plateau is fundamental to preventing snow avalanche disaster risks. This study focuses on the Eastern Himalayan Syntaxis (EHS) region and collected 449 snow avalanche events through event collection, field investigations, and remote sensing interpretation. Using these data, the study employed four machine learning models—Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Gradient Boosting Decision Tree (GBDT)—to produce snow avalanche susceptibility maps. Furthermore, the SHAP algorithm was utilized to identify the critical factors affecting snow avalanche susceptibility in the EHS. Under the random split, the RF model achieved the highest AUC value (0.912). Under spatial block cross-validation, the AUC values decreased to varying degrees, with SVM and RF still performing best (AUC = 0.855 and 0.850, respectively). The total area of extremely high and high-risk snow avalanche zones was 4036.4 km², accounting for 21.0% of the study area. High-risk snow avalanche areas were primarily concentrated in the narrow valley regions near the entrances of the Doxiongla and Galongla tunnels. Additionally, the use of the SHAP algorithm enhanced the interpretability of the RF model. The study found that NDVI, glacial kernel density, January average temperature, land use, slope, and aspect were the main factors predicting snow avalanche occurrence, reaching a cumulative contribution rate of 66.4%. These findings not only confirm the effectiveness of the combination of machine learning models and the SHAP algorithm in snow avalanche susceptibility assessment but also delineate critical areas for enhancing local avalanche disaster prevention and mitigation strategies. The results of this study can serve as a reference for other mountainous regions with insufficient snow avalanche research data.

The southeastern Tibetan engineering corridor hosts the densest transportation network in Tibet, China, and is traversed by large-scale railway and power corridor projects under construction. This region is home to numerous glacial lakes, some of which are prone to glacial lake outburst floods (GLOFs), posing potential threats to the infrastructure. However, the spatiotemporal evolution and GLOF susceptibility of these lakes remain unclear. Using Landsat 5–9 and Sentinel-2 satellite imagery, we analyzed the spatiotemporal characteristics of glacial lakes from 1990 to 2020. Based on historical GLOF events, we established a susceptibility assessment criterion and determined the susceptibility levels of all glacial lakes in the study area. Results show that the number and area of glacial lakes increased by 40.4% and 26.2%, respectively, from 1990 to 2020, with expansion rates of 2.47 lakes/year and 0.26 km²/year. We identified 31 very highly and 48 highly susceptible lakes, mainly distributed along the Gongrigabu River and the Parlung Tsangpo River. Among them, 35 lakes are most likely to impact National Highways G219 and G318 within the study area. Additionally, three channels with glacial lake clustering amplification effects were found, which may lead to the superposition and amplification of flood impacts, significantly increasing GLOF risks and hazards. Our findings provide crucial references for ensuring the safe operation of local transportation networks and reducing GLOF risks in ongoing large-scale construction projects.

Geological disasters are frequently triggered in steep mountainous mining areas due to the coupling effects of underground excavation and slope stability, yet the applicability of traditional rock movement theories in such terrains remains unclear. This study investigates an extremely steep coal mine in southwestern China, integrating engineering geological surveys, unmanned aerial vehicle (UAV) measurements, InSAR monitoring, and rock movement theoretical calculations to analyze the impact of mining on mountain deformation and slope stability. The results show that the study area exhibits steep slopes (55–85°) and gently inclined, reverse-layered rock masses controlled by structural fracture zones, creating a geological environment prone to mining-induced landslides. The 1151 working face lies at a depth of 286–470 m, with a protective coal pillar of approximately 160 m left between the excavation and the cliff zone. InSAR monitoring indicates cumulative LOS deformation rates of −0.98 to 0.55 cm/a, with subsidence concentrated above the working face, while existing landslides in the cliff zone show no significant deformation. Comparison between theoretical calculations and InSAR inversion reveals that InSAR boundary angles (downslope 61–68°, upslope 67–73°) exceed theoretical predictions (downslope 48–52°, upslope 55°), indicating that complex topography and rock mass structure constrain mining-induced deformation propagation. The findings demonstrate that appropriately designed protective coal pillars and avoidance of unstable slopes can effectively mitigate the impact of mining-induced disturbances on existing hazards. This study provides valuable reference for landslide risk assessment and disaster prevention in extremely steep mining regions.

Coal mining in plain regions and its related surface subsidence and geological hazards have been extensively studied, whereas research on mining-induced hazards in mountainous areas remains limited. This knowledge gap has contributed to the frequent occurrence of mining disasters, particularly under steep ridge-type mountain geometry, where deformation characteristics, large-scale slope failure risks, and mining-induced hazard mechanisms remain poorly understood. In this study, a mining area in Zhenxiong, Zhaotong, Yunnan Province, China, is investigated using SBAS-InSAR, GNSS observations, UAV surveys, optical satellite imagery, and detailed field investigations. Surface hazards triggered by coal extraction are identified, and the response relationship between surface subsidence and mining activities is analyzed to reveal the development mechanisms of surface deformation beneath steep ridge-type mountain geometry. The results show that: (1) deep coal mining can still induce significant surface deformation due to the combined amplification effects of steep slopes and lithological conditions; (2) mining-induced deformation does not necessarily evolve into large-scale slope collapse and may gradually stabilize through natural adjustment processes; (3) SBAS-InSAR, validated by GNSS and field observations, provides an effective approach for detecting mining-related subsidence; (4) surface deformation in the study area is jointly influenced by multiple working faces; and (5) strong coupling between the unique steep ridge-type mountain geometry and underlying coal extraction leads to a compound disaster chain under multi-source interactions. These findings offer a critical scientific understanding of mining-induced deformation beneath steep ridge-type mountain geometry and provide important guidance for geological hazard prevention and control in similar mountainous mining areas.

In the semi-desert aeolian sand areas of Northern China, surface deformation monitoring with SAR is challenged by loss of coherence due to mobile dunes, seasonal vegetation changes, and large-gradient, nonlinear subsidence from underground mining. This study utilizes PALSAR-2 (L-band, 3 m resolution) and Sentinel-1 (C-band, 30 m resolution) data, applying InSAR and Offset tracking methods combined with differential, Stacking, and SBAS techniques to analyze deformation monitoring effectiveness and propose an efficient dynamic monitoring strategy for the Shendong Coalfield. The main conclusions can be summarized as follows: (1) PALSAR-2 data, which has advantages in wavelength and resolution (L-band, multi-look spatial resolution of 3 m), exhibits better interference effects and deformation details compared to Sentinel-1 data (C-band, multi-look spatial resolution of 30 m). The highly sensitive differential-InSAR (D-InSAR) can promptly detect new deformations, while Stacking-InSAR can accurately delineate the range of rock strata movement. SBAS-InSAR can reflect the dynamic growth process of the deformation range as a whole, and SBAS-Offset is suitable for observing the absolute values and morphology of the surface moving basin. The combined application of Stacking-InSAR and Stacking-Offset methods can accurately acquire the three-dimensional deformation field of mining-induced strata movement. (2) The spatiotemporal process of surface deformation caused by coal mining-induced strata movement revealed by InSAR exhibits good correspondence with both the underground mining progress and the development of ground fissures identified in UAV images. (3) The maximum displacement along the line of sight (LOS) measured in the mining area is approximately 2 to 3 m, which is close to the 2.14 m observed on site and aligns with previous studies. The calculated advance influence angle of the No. 22308 working face in the study area is about 38.3°. The influence angle on the solid coal side is 49°, while that on the goaf side approaches 90°. These findings further deepen the understanding of rock movement and surface displacement parameters in this region. The dynamic monitoring strategy proposed in this study is cost-effective and operational, enhancing the observational effectiveness of InSAR technology for surface deformation due to coal mining in this area, and it enriches the understanding of surface strata movement patterns and parameters in this region.

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards.

《地球科学》(ISSN 1000-2383;CN42-1874/P) (原刊名《地球科学——中国地质大学学报》ISSN 1000-2383;CN42-1233/P)，创刊于1957年，是由教育部主管、中国地质大学主办的综合性地学学术理论期刊，月刊，2015年改名为《地球科学》,她以反映中国地质大学教学与科研最新的、高水平的基础地质、应用地质、资源与环境地质及地学工程技术研究成果为主要任务。