School of Environment Science and Spatial Informatics,China University of Mining and Technology

针对传统开采损害鉴定中缺乏实测数据,仅靠预计结果划定开采影响边界位置存在争议的问题,提出了一种融合分布式目标InSAR(DS-InSAR)和概率积分法(PIM)的开采损害鉴定方法,该方法利用快速同质点选取、特征值分解、分时段累加等方法提取研究区长时序地表形变信息,对概率积分法预计边界进行验证和修正。以153景Sentinel-1A影像为数据源,获取了研究区2016年7月至2021年10月期间的地表沉降信息,结合概率积分法,划定了开采影响范围。结果表明:村庄内最大下沉值约为576mm,煤矿3个工作面的开采对村庄影响较大,开采影响边界位于村庄南部。融合DS-InSAR和PIM的方法划定开采影响边界得到了矿村双方共同认可,为开采损害鉴定提供了新的参考。

It is disputed that the boundary of mining impact is determined solely based on predicted results because of the lack of measured data in traditional mining damage identification. A method combining Distributed Scatterer InSAR (DS-InSAR) and Probability Integration Method (PIM) was proposed in this study for mining damage assessment. This method employed several techniques such as fast homogeneous point selection,eigenvalue decomposition,and temporal accumulation to extract long-term surface deformation information in the study area. It was applied to validate and correct the predicted boundaries using the PIM. The surface subsidence information from July 2016 to October 2021 in the study area was obtained based on the 153 scenes of Sentinel-1A images data. The mining impact range of the mine was then delineated by combining with the PIM. The results showed that the maximum subsidence value within the village was approximately 576 mm,indicating significant impacts of three working faces on the village,with the mining impact boundary located in the southern part of the village. The methodintegrating DS-InSAR and PIM for delineating mining impact boundaries gained mutual recognition from both the mine and the village,providing a novel approach for mining damage identification.

国家自然科学基金资助项目(42274054);江苏高校优势学科建设工程资助项目(PAPD)