An InSAR Deformation Monitoring Method for Mining Subsidence Constrained by the IPIM Prediction Model

The emergence of large-gradient deformation within a short period is a typical characteristic of mining subsidence. InSAR phase unwrapping can readily fail as a consequence of this deformation characteristic, and it is challenging to achieve effective monitoring of mining subsidence over mining sites with InSAR techniques. To monitor mining subsidence, we developed an InSAR strategy with constraints derived from the IPIM (Improved Probability Integration Method). As the initial step, this method modifies the PIM model to improve its precision in predicting the surface deformation basin edge. Furthermore, by investigating the functional relationship between InSAR-observed line-of-sight (LOS) deformation and the three-dimensional surface displacement in subsidence regions, we develop a new IPIM-constrained InSAR observation condition equation for mining subsidence. Subsequently, taking the GA (genetic algorithm) as the optimization solver, we establish an IPIM parameter inversion model driven by the GA. In the final stage, using LOS displacement measurements acquired from InSAR observations, the surface displacement field over mining subsidence zones can be extracted using the presented InSAR monitoring scheme constrained by the IPIM forecasting model. Using field measurements collected at the 2111(3) working face of the Xieqiao Coal Mine, the error in subsidence fitting is ±178.76 mm. The presented technique has been validated as feasible and can supply fundamental datasets for geological hazard prevention and control in mining areas.

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Journal
Applied Sciences
Published
2026-10-30
DOI
https://doi.org/10.3390/app162110763
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

An InSAR Deformation Monitoring Method for Mining Subsidence Constrained by the IPIM Prediction Model

Zhongchen Guo, Chuang Jiang, Lei Wang
4 links
Applied Sciences
Synthetic Aperture Radar (SAR) Applications and Techniques
article

An InSAR Deformation Monitoring Method for Mining Subsidence Constrained by the IPIM Prediction Model

Zhongchen Guo, Chuang Jiang, Lei Wang
article en

Abstract

The emergence of large-gradient deformation within a short period is a typical characteristic of mining subsidence. InSAR phase unwrapping can readily fail as a consequence of this deformation characteristic, and it is challenging to achieve effective monitoring of mining subsidence over mining sites with InSAR techniques. To monitor mining subsidence, we developed an InSAR strategy with constraints derived from the IPIM (Improved Probability Integration Method). As the initial step, this method modifies the PIM model to improve its precision in predicting the surface deformation basin edge. Furthermore, by investigating the functional relationship between InSAR-observed line-of-sight (LOS) deformation and the three-dimensional surface displacement in subsidence regions, we develop a new IPIM-constrained InSAR observation condition equation for mining subsidence. Subsequently, taking the GA (genetic algorithm) as the optimization solver, we establish an IPIM parameter inversion model driven by the GA. In the final stage, using LOS displacement measurements acquired from InSAR observations, the surface displacement field over mining subsidence zones can be extracted using the presented InSAR monitoring scheme constrained by the IPIM forecasting model. Using field measurements collected at the 2111(3) working face of the Xieqiao Coal Mine, the error in subsidence fitting is ±178.76 mm. The presented technique has been validated as feasible and can supply fundamental datasets for geological hazard prevention and control in mining areas.

Applied SciencesVol. 16(21)
Anhui University of Science and Technology (CN), Soochow University (CN), Suzhou University (CN)
National Natural Science Foundation of China, Anhui University of Science and Technology, Scientific Research Foundation of Education Department of Anhui Province of China, Suzhou University
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Synthetic Aperture Radar (SAR) Applications and Techniques
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