A robust method for identifying the subsidence basin boundary caused by mining based on SBAS-InSAR and level data fusion

doi:10.13474/j.cnki.11-2246.2026.0210

Abstract

Abstract: InSAR monitoring technology struggles to obtain large deformation data in areas of mining subsidence,and the boundary of subsidence is easily influenced by interference,affecting the accuracy of boundary delineation.To address this,a random sample consensus (RANSAC)algorithm is introduced,and a robust fusion method for mining subsidence data is proposed,which couples the Boltzmann function (BRAN).By randomly sampling SBAS-InSAR pixel points and using the number of inliers as an evaluation criterion,we repeatedly invert the model until a robust parameter solution with the maximum number of inliers is obtained.Finally,the predicted subsidence calculated by Boltzmann model and its robust parameter are used to replace outliers pixels and decoherent data,completing the robust fusion of subsidence data.Simulation experiments and a case study from the Zhujidong coal mine in Huainan show that the BRAN method can effectively identify and eliminate the interference of outliers pixel points,with the relative root mean square error of the fused subsidence values being 6.4%.The obtained subsidence basin boundary aligns with the characteristics of mining subsidence.The research findings provide new insights for robust fusion of multi-source data in mining areas and hold certain application value for monitoring and early warning of subsidence disasters in these regions.

Key words: mining subsidence area, subsidence boundary, SBAS-InSAR, random sample consistency, Boltzmann

CLC Number:

P237

CHEN Yuanfei, WANG Lei. A robust method for identifying the subsidence basin boundary caused by mining based on SBAS-InSAR and level data fusion[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 60-67.

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