With the exploitation of coal resources, ground surface subsidence continues to occur in mining areas, destroying the ecological environment and significantly affecting the daily productivity and life of humans. The differential synthetic aperture radar interferometry (D-InSAR) technique is widely used to monitor ground surface deformation because of its unique advantages such as high accuracy and wide coverage. However, conventional D-InSAR technology provides only one-dimensional (1D) displacement monitoring along the radar line of sight (LOS). This article proposes a method based on an analysis of the mining subsidence law for true three-dimensional (3D) displacement monitoring by combining D-InSAR and a subsidence prediction model based on the probability integral method. In this approach, 1D displacement, obtained using D-InSAR, is then combined with the prediction model to obtain the 3D displacement of ground surface target points. Here, 3D displacement curves were obtained for the Fengfeng mining area (China) using RadarSat-2 images obtained on 9 January and 2 February 2011. True ground surface displacement was measured simultaneously by levelling when the 152under31 s working face was being exploited in Jiulong mine. Vertical displacement and inclined deformation calculated using the proposed method were compared with levelling survey data and the results showed average differences of 3.2 mm and 0.1 mm m⁻¹, respectively; the calculated maximum displacement in the east–west and south–north directions were 106 and 73 mm, respectively. The spatial distribution of the displacements was in accordance with the mining subsidence law. Thus, the new method can retrieve highly accurate 3D displacements caused by mining subsidence.

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.

Under normal geological and mining conditions, surface subsidence caused by underground coal mining generally adheres to current subsidence theory. However, due to the presence of complex geological structures, abnormal mining-induced surface deformation has become increasingly frequent. In this study, we investigate abnormal surface deformation and building damage caused by the excavation of working face 162601 in Xinsan Mine, Fengfeng Mining District, using seven RadarSat-2 SAR images from October 2013 to March 2014. The spatial distribution of surface deformation is identical to the strike of the F29 fault, indicating that the abnormal damage suffered by buildings in Shiqiao Village is related to the existence of this fault. A theoretical analysis and similitude material simulation experiments are conducted to determine the relationship between the fault and the mining-induced subsidence. In both instances, fault “activation” is the root cause of the abnormal surface deformation and building damage. This study reveals the mechanism behind abnormal damage in a mining-induced subsidence area by analyzing the InSAR derived spatial distribution of surface deformation. The findings of this study will contribute towards improving subsidence control theory, developing preventive measures against abnormal damage in subsidence-affected areas, and conserving the ecological environment of such areas.

Ground subsidence and surface cracks caused by coal mining are typical man-made geological hazards that can severely damage the ecological environment and buildings. In China, within the theme of sustained and stable development, accurate assessment of mining-related building damage is paramount in order to address the contradiction between coal mining enterprises and building owners. Previous research in China focused mainly on the mining areas of plains, and only a few studies have considered building damage caused by intensive mining in mountainous areas. First, based on field investigation, this study located ground surface cracks and assessed the damage to buildings in the village of Nanyetou in Shanxi Province (China) attributable to the exploitation of the 15110 working face of the Baiyangling coal mine. Second, based on the mining subsidence law and boundary angle, the surface influenced boundary caused by underground mining was determined. However, as the existing subsidence theory cannot adequately explain the phenomenon of building damage, the damage was investigated from the perspective of slope stability analysis, and the slope safety factor before and after working face mining were calculated using the Janbu method. The analytical results showed that slope instability due to a decrease of the safety factor because of the coal mining activity was the principal reason for damage to the village buildings, a finding that was confirmed by field survey and InSAR monitoring displacement. The results of this study could provide guidance and reference for the assessment of building damage caused by underground mining in mountain areas.

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.