Mining Subsidence Monitoring with Modified Time-Series SAR Interferometry Method Based on the Multi-Level Processing Strategy

Abstract

The supply of coal is related to the stability of social development in China. Coal mining faces are generally located under farmland and wasteland to avoid destroying the livelihood and property of residents, which seriously affects the selection of permanent scatterer (PS) with time-series interferometric synthetic aperture radar (TS-InSAR) method. The advanced TS-InSAR (ATS-InSAR) method combining PS and distributed scatterer (DS) monitoring modules is a useful tool to increase measurement points. However, the number of DS and the accuracy of measurement are different with different thresholds of temporal coherence. On the basis of ATS-InSAR, we propose a modified method applying the multi-level processing strategy to obtain more reliable deformation information in this paper. 16 sentinel-1A images are used to monitor mining subsidence in Peixian, China. The results of three different methods are cross-verified. Meanwhile, reliable DS pixels are identified by using the thresholding of both temporal coherence and Pearson correlation coefficient through the hierarchical processing. The results show that the deformation of the modified method reveals a large subsidence with the maximum rate of -563 mm/yr. The number of measurement points selected by the modified method is about 6.6 times that of the TS-InSAR method, and 1.3 times that of the ATS-InSAR method. The modified strategy can extract a great number of reliable pixels and reduce error propagation to ensure measurement accuracy. This research offers information to relevant departments for risk management purpose.