Abstract
Underground coal mines are widely distributed across China, and underground mining is highly concealed. The rapid and accurate identification of the spatial distribution of coal mining subsidence over large areas is of significant importance for the reuse of land resources in mining areas and the detection of illegal mining activities. The traditional method of monitoring subsidence basins has limitations in terms of monitoring range and timeliness. The development of synthetic aperture radar (InSAR) technology has provided a valuable tool for monitoring mining subsidence areas. However, this method faces challenges in quickly and effectively monitoring subsidence basins using wide-swath SAR images. With the rapid development of deep learning and computer vision technologies, leveraging advanced deep learning models in combination with InSAR technology has become a crucial research direction to enhance the monitoring efficiency of surface subsidence in mining areas. Therefore, this paper proposes a new method for the rapid identification of mining subsidence basins in mining areas, which integrates Deformable Detection Transformer (Deformable DETR) and InSAR technology. First, the real deformation sample set of the mining area, obtained through interference processing, is combined with simulated deformation samples generated using the dynamic probability integral method, elastic transformation, and various noise synthesis techniques to construct a mixed InSAR sample set. This mixed sample set is then used to train the Deformable DETR model and compared with common deep learning methods. The experimental results show that the monitoring accuracy is significantly improved, with the model achieving a Precision of 0.926, Recall of 0.886, F1-score of 0.905, and mean Intersection over Union (mIoU) of 0.828. The detection model was applied to monitor the dynamically updated mining subsidence in the Huainan mining area from 2023 to 2024, detecting 402 subsidence basins. Further training demonstrates that the model exhibits strong robustness. Therefore, this method reduces the construction cost of the target detection training set and holds significant application potential for monitoring geological disasters in large-scale mining areas.