Stacking-assisted DS-InSAR method for monitoring surface deformations in complex mining areas
LI Zhi1(), ZHANG Shubi1(), LI Minggeng2, CHEN Qiang3, BIAN Hefang1, LI Shijin1, GAO Yandong1, ZHANG Yansuo1, ZHANG Di1
1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China 2. China Railway Shanghai Design Institute Group Corporation Limited, Shanghai 200040, China 3. Yankuang energy Group Company Limited Jining No.3 coal mine, Jining 272000, China
Interferometric Synthetic Aperture Radar (InSAR) faces the challenges of the insufficient number of monitoring points and low monitoring accuracy when applied to complex environments with dense vegetation and large-gradient surface deformation in a mining area. To address these challenges, this study proposed an improved distributed scatterer InSAR (DS-InSAR) method assisted by stacking technology. This method identified statistically homogenous pixels using a confidence interval hypothesis test and achieved phase optimization utilizing a phase triangulation algorithm. Subsequently, the residual phases were derived by removing the linear deformation phases determined via stacking-based simulation. This step contributed to sparse deformation phase fringes, thereby enhancing the accuracy of spatiotemporal filtering and three-dimensional phase unwrapping within the subsequent DS-InSAR processing framework. Finally, the simulated phases were compensated, and thus complete deformation fields were determined. By processing Sentinel-1A SAR images covering the Xinjulong Coal Mine from October 2015 to March 2016, this study interpreted the time-series surface deformation characteristics in the mining area during this period. The findings revealed three significant deformation sites in the mining area, with a maximum cumulative radar line-of-sight (LOS) deformation of up to -313 mm. Compared to conventional small Baseline Subset (SBAS) InSAR, the proposed method yielded more uniformly distributed monitoring points via inversion, with a density approximately 12.9 times higher. The root mean squared error (RMSE) of the inversion was approximately 6.82 mm relative to leveling data, representing an accuracy improvement of about 3.0 mm compared to the SBAS results.
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2025, Vol. 37 
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