Investigating Mining-Induced Surface Subsidence in Mountainous Areas Using Integrated InSAR and GNSS Monitoring

Leveraging the complementary advantages of InSAR and GNSS, this study proposes a refined method for monitoring mining-induced surface subsidence by integrating both technologies. The method begins with calculating the time-series cumulative subsidence basin from InSAR. Subsequently, a constraint condition is established to identify large-gradient deformations, thereby distinguishing the subsidence edge from the subsidence center. For the subsidence edge with minor deformation, the InSAR results are retained. For the large-gradient subsidence center, the subsidence basin around the mining panel is reconstructed by integrating InSAR and GNSS models. Continuous surface deformation information in a geographic coordinate system is then obtained through spatial interpolation, ultimately yielding comprehensive surface subsidence results across the mining area. Taking a mining area in Shanxi Province as the study region, the feasibility and accuracy of the proposed method were validated using 35 SAR images acquired between April 2016 and September 2017, along with leveling measurement data from the mining panel. The maximum surface subsidence rate of the settlement basin obtained from the solution is -186.68 mm/year, and the maximum surface subsidence amount is 248 mm. Compared with the InSAR monitoring results, the root mean square error of the data collaborative monitoring is reduced by 96.8%, and it is reduced by 64.4% compared with the GNSS probability integral method. The results demonstrate that the proposed method can achieve subsidence results consistent with the actual situation. Its monitoring capability is significantly superior to that of using either InSAR or GNSS alone, effectively compensating for the limitations inherent in each individual technology when applied to mining subsidence monitoring. Consequently, this integrated approach provides more accurate and reliable information on surface subsidence in mining areas.

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Publication Details

Journal
Sensors
Published
2026-02-13
DOI
https://doi.org/10.3390/s26041222
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Investigating Mining-Induced Surface Subsidence in Mountainous Areas Using Integrated InSAR and GNSS Monitoring

Wenkai Liu, Yingchao Kou, Huaizhan Li, Qingfeng Hu et al.
Sensors
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Investigating Mining-Induced Surface Subsidence in Mountainous Areas Using Integrated InSAR and GNSS Monitoring

Wenkai Liu, Yingchao Kou, Huaizhan Li, Qingfeng Hu, Qifan Wu, Xinjing Wang, Fan Zhang, Zhaomeng Zhou, Xinlei Li, Peng Wang, Zilin Liu, Sihai Yi, Mingyang Zhang, Runjin Hou
article en

Abstract

Leveraging the complementary advantages of InSAR and GNSS, this study proposes a refined method for monitoring mining-induced surface subsidence by integrating both technologies. The method begins with calculating the time-series cumulative subsidence basin from InSAR. Subsequently, a constraint condition is established to identify large-gradient deformations, thereby distinguishing the subsidence edge from the subsidence center. For the subsidence edge with minor deformation, the InSAR results are retained. For the large-gradient subsidence center, the subsidence basin around the mining panel is reconstructed by integrating InSAR and GNSS models. Continuous surface deformation information in a geographic coordinate system is then obtained through spatial interpolation, ultimately yielding comprehensive surface subsidence results across the mining area. Taking a mining area in Shanxi Province as the study region, the feasibility and accuracy of the proposed method were validated using 35 SAR images acquired between April 2016 and September 2017, along with leveling measurement data from the mining panel. The maximum surface subsidence rate of the settlement basin obtained from the solution is -186.68 mm/year, and the maximum surface subsidence amount is 248 mm. Compared with the InSAR monitoring results, the root mean square error of the data collaborative monitoring is reduced by 96.8%, and it is reduced by 64.4% compared with the GNSS probability integral method. The results demonstrate that the proposed method can achieve subsidence results consistent with the actual situation. Its monitoring capability is significantly superior to that of using either InSAR or GNSS alone, effectively compensating for the limitations inherent in each individual technology when applied to mining subsidence monitoring. Consequently, this integrated approach provides more accurate and reliable information on surface subsidence in mining areas.

SensorsVol. 26(4)
North China Institute of Science and Technology (CN), North China University of Water Resources and Electric Power (CN), China University of Mining and Technology (CN), Henan Provincial Institute of Communications Planning Survey & Design (China) (CN), Xinxiang University (CN)
National Natural Science Foundation of China, Henan University, National Key Research and Development Program of China
Openalex Percentile: Top 58%
Synthetic Aperture Radar (SAR) Applications and Techniques
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