Abstract

Regional surface subsidence induced by underground coal mining is very common and is a serious environmental problem in China. The mining subsidence not only causes damages to surface buildings but also change the pattern of surface drainage in a densely urbanized area. Monitoring and Analyzing the spatial distribution of the endangered surface may be helpful for land-use planning and for land reclamation. Interferometry SAR (InSAR) can be used to effectively monitor the succession of the spatial extent and the magnitude of subsidence in coal mining areas. In this paper, the multi-temporal D-InSAR method was applied for the generation of deformation map in coal mining area. With the "Interferometry Coherence Estimation Minimum Span Tree (ICEMST)" model, the optimized SAR images combination for D-InSAR processing for long term surface subsidence monitoring were predicted. With the estimation of ICEMST, several scenes SAR SLC data with time spanned more than half a year and spatial baseline more than 400m long were combined for D-InSAR processing to study the succession of land subsidence induced by underground coal mining and groundwater exploration in the test site, Kailuan coal mine area, a typical mining industrial area in north China, which has 125 years coal mining history. After being processed with the conventional "2 Pass" differential InSAR method, the deformation caused by underground coal mining in the line of sight (LOS) was transformed into vertical subsidence map. The experiment shows that the short time span is more suitable for D-InSAR application in mining areas than the long time span, for the lower correlation due to densely growing vegetation, seasonal changing factors and large water plashes. The time decorrelation, spatial decorrelation and the D-InSAR error resources were analyzed and discussed, and the Connor Reflectors method integrated with D-InSAR and GPS are presented, which will be a key practical technology for information obtain in digital mine.

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