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<p>As one of the world’s largest coal producers, China has shifted its coal mining focus from the eastern to the western regions, which has, however, resulted in a more significant impact on the fragile ecological environment in the western areas. Among the influencing factors, surface subsidence due to coal mining is the primary cause of damage to the ecological environment of mining areas and negatively impacts sustainable mining practices. Therefore, monitoring the deformation of the primary mining face and analyzing its characteristics is crucial for reconstructing the coal mine subsidence area and mitigating geological hazards. However, due to the shallow mining depth, significant thickness of coal seams and relatively harsh observation environment in the western mining areas of China, it is challenging to study the characteristics of surface subsidence merely by setting up observation points on the main cross-section of the mining face and adopting traditional data collection methods (such as traverse surveying and leveling). In this context, this paper proposes a method for extracting high-precision surface subsidence basins by combining Unmanned Aerial Vehicle Laser Scanning (ULS) and Interferometric Synthetic Aperture Radar (InSAR) data. This method employs Small Baseline Subset InSAR (SBAS) to capture small deformation areas within the subsidence basin, ULS for larger deformation areas, and polynomial fitting to interpolate the remaining regions, thereby creating a complete subsidence basin. This study focuses on the 3-1501 mining face of Hongqinghe Mine. Using this method, two epochs of point cloud data and 35 Sentinel-1 images were used to derive the high-precision surface subsidence basin of the mining face. Surface mobile observation station data were then used for accuracy assessment, and the results indicate that the root mean square error of the subsidence basin is 0.022&#xa0;m. Finally, combined with experimental analysis of the subsidence basin and the rock fragmentation coefficient, it was found that the surface subsidence basin of the 3-1501 mining face at Hongqinghe Mine exhibits steep edges, a limited impact range, and a low surface subsidence coefficient. This phenomenon is attributed to the weakly cemented overburden, which has a high fragmentation coefficient and a prolonged compaction cycle. Based on multi-source measured data from mining areas in western China, this study proposes an integrated approach to construct a high-precision and complete subsidence basin. By incorporating the rock mechanics parameters of the mining area, the characteristics and formation mechanisms of surface subsidence are systematically analyzed. The findings enhance the accuracy and integrity of subsidence basin prediction and provide a solid theoretical foundation and practical guidance for safe mining operations and disaster prevention under similar mining conditions in western China.</p>

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Subsidence characteristics and mechanism study of Hongqinghe Mine based on multi source data

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Xiao Wang,
  • Xilin Zhan,
  • Dawei Zhou

摘要

As one of the world’s largest coal producers, China has shifted its coal mining focus from the eastern to the western regions, which has, however, resulted in a more significant impact on the fragile ecological environment in the western areas. Among the influencing factors, surface subsidence due to coal mining is the primary cause of damage to the ecological environment of mining areas and negatively impacts sustainable mining practices. Therefore, monitoring the deformation of the primary mining face and analyzing its characteristics is crucial for reconstructing the coal mine subsidence area and mitigating geological hazards. However, due to the shallow mining depth, significant thickness of coal seams and relatively harsh observation environment in the western mining areas of China, it is challenging to study the characteristics of surface subsidence merely by setting up observation points on the main cross-section of the mining face and adopting traditional data collection methods (such as traverse surveying and leveling). In this context, this paper proposes a method for extracting high-precision surface subsidence basins by combining Unmanned Aerial Vehicle Laser Scanning (ULS) and Interferometric Synthetic Aperture Radar (InSAR) data. This method employs Small Baseline Subset InSAR (SBAS) to capture small deformation areas within the subsidence basin, ULS for larger deformation areas, and polynomial fitting to interpolate the remaining regions, thereby creating a complete subsidence basin. This study focuses on the 3-1501 mining face of Hongqinghe Mine. Using this method, two epochs of point cloud data and 35 Sentinel-1 images were used to derive the high-precision surface subsidence basin of the mining face. Surface mobile observation station data were then used for accuracy assessment, and the results indicate that the root mean square error of the subsidence basin is 0.022 m. Finally, combined with experimental analysis of the subsidence basin and the rock fragmentation coefficient, it was found that the surface subsidence basin of the 3-1501 mining face at Hongqinghe Mine exhibits steep edges, a limited impact range, and a low surface subsidence coefficient. This phenomenon is attributed to the weakly cemented overburden, which has a high fragmentation coefficient and a prolonged compaction cycle. Based on multi-source measured data from mining areas in western China, this study proposes an integrated approach to construct a high-precision and complete subsidence basin. By incorporating the rock mechanics parameters of the mining area, the characteristics and formation mechanisms of surface subsidence are systematically analyzed. The findings enhance the accuracy and integrity of subsidence basin prediction and provide a solid theoretical foundation and practical guidance for safe mining operations and disaster prevention under similar mining conditions in western China.