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D-InSAR Monitoring Method of Mining Subsidence Based on Boltzmann and Its Application in Building Mining Damage Assessment

  • Surveying and Geo-Spatial Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Monitoring and predicting mining area subsidence caused by coal mining help effectively control geological disasters. Information regarding small surface deformations can be obtained using a differential interferometric synthetic aperture radar (D-InSAR), which exhibits high monitoring accuracy and can cover large areas; thus, D-InSAR are being applied for mining area settlement monitoring. However, mining areas are prone to large gradient deformations in short durations; obtaining information regarding such deformations is outside the scope of D-InSAR monitoring. To adapt to the characteristics of D-InSAR monitoring, this study selected a Boltzmann function model with a slow boundary convergence rate to address the problem of probabilistic integration methods exhibiting a high boundary convergence rate. The three-dimensional surface deformation of the mining area can be accurately obtained. According to the projection relationship between D-InSAR line of sight (LOS) directional deformation, subsidence, and horizontal movement, a D-InSAR monitoring equation for mining subsidence assisted by Boltzmann is derived. This equation is combined with the shuffled frog leaping algorithm (SFLA) to obtain the parameters to be estimated from the equation. The D-InSAR LOS deformation information of the 13121 working face in the Huainan mining area was obtained from July 14 to October 30, 2019. The proposed method was then used to obtain the predicted parameters of the working face under insufficient mining. The predicted parameters of mining subsidence were calculated to obtain the predicted parameters when it was fully mined. Then, the revised parameters were used to predict the subsidence and horizontal movement of the mining area and compared with the actual leveling observations. The results show that the mean square error of predicted subsidence is 97.1 mm, which is about 3.09% of the maximum subsidence value; the mean square error of predicted horizontal movement is 46.1 mm, which is about 4.1% of the maximum horizontal movement value. The predicted results of the aforementioned method were used to analyze the damage to the buildings above the working face and determine the damage level of the buildings to provide a reference for the demolition and maintenance of the Zhaimiao village.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China (Grant numbers 52074010, 41602357, 51904008).

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Authors and Affiliations

  1. School of Geomatics, Anhui University of Science and Technology, Huainan, 232001, China

    Lei Wang, Chaoqun Teng, Kegui Jiang & Shangjun Zhu

  2. School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, China

    Chuang Jiang

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  1. Lei Wang
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  2. Chaoqun Teng
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  3. Kegui Jiang
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  4. Chuang Jiang
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  5. Shangjun Zhu
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Correspondence to Lei Wang.

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Wang, L., Teng, C., Jiang, K. et al. D-InSAR Monitoring Method of Mining Subsidence Based on Boltzmann and Its Application in Building Mining Damage Assessment. KSCE J Civ Eng 26, 353–370 (2022). https://doi.org/10.1007/s12205-021-1042-5

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