Monitoring and analysis of mining 3D deformation by multi-platform SAR images with the probability integral method

Abstract

Only one-dimensional (1D) deformation along the radar line of sight (LOS) can be obtained using differential interferometry synthetic aperture radar (D-InSAR), and D-InSAR observation is insensitive to deformation in the north direction. This study inferred three-dimensional (3D) deformation of a mining subsidence basin by combining the north-south deformation predicted by a probability integral method with the LOS deformation obtained by D-InSAR. The 15235 working face in Fengfeng mining area (Hebei Province, China) was used as the object of study. The north-south horizontal movement was predicted by the probability integral method according to the site’s geological and mining conditions. Then, the vertical and east-west deformation fields were solved by merging ascend-orbit RadarSAT-2, descend-orbit TerraSAR, and predicted north-south deformation based on a least squares method. Comparing with the leveling data, the results show that the vertical deformation accuracy of the experimental method is better than the inversed vertical deformation neglecting the horizontal deformation. Finally, the impact of the relationship between the azimuth of the working face and the SAR imaging geometry on the monitoring of the mining subsidence basin was analyzed. The results can be utilized in monitoring mining subsidence basins by single SAR image sources.

Keywords

D-InSAR / ascend-descend orbit data / subsidence prediction / probability integral method / 3D deformation

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Acknowledgements

The research work was funded by the National Natural Science Foundation of China (Grant Nos. 41272389 and 41604005), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (No. SZBF2011-6-B35), Special Fund for Public Projects of National Administration of Surveying, Mapping, and Geoinformation of China (No. 201412016), Project supported by the Basic Research Project of Jiangsu Province (Natural Science Foundation) (No. BK20160218), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (No.SKLGP2016K008).

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