Mining subsidence monitoring using distributed scatterers InSAR based on Goldstein filter and Fisher information matrix-weighted optimization

Due to the shortcomings of traditional interferometric synthetic aperture radar (InSAR) technologies in mining subsidence monitoring, such as a low density of monitoring points and difficulty obtaining fine surface deformation information, this paper proposes a mining area surface deformation monitoring method based on distributed scatterers InSAR while improving the phase information optimization strategy. This method obtains homogeneous points using the hypothesis test of confidence interval algorithm and constructs an adaptive phase optimization method based on the Goldstein principal phase filtering and Fisher information matrix weighting. It effectively preserves the information of deformation fringes, particularly in regions with dense interferometric fringes, and obtains detailed deformation information from the study area through time processing. In the experiments, 63 Sentinel-1 images were used to extract surface subsidence information for the Peibei mining area from September 24, 2018, to November 12, 2020. Compared with the Permanent Scatterers InSAR (PS-InSAR) results, the point density increased by a factor of 4.2. The correlation coefficient between the homonymous points obtained with the two methods and the deformation rate is 0.94, indicating that they have a good consistency. The monitoring results show that the six mining areas in Peibei have different degrees of subsidence during the monitoring period with a clear nonlinear trend, and the maximum cumulative subsidence over time exceeds 350 mm. The analysis shows that the improved DS-InSAR monitoring results are in line with the general law of mining subsidence and have practical application value.