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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 20-29     DOI: 10.6046/zrzyyg.2021354
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Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model
XU Zixing1(), JI Min1(), ZHANG Guo2, CHEN Zhenwei2
1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2. National Key Laboratory of Surveying and Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China
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Abstract  

Predicting the subsequent subsidence in mining areas according to the law of mining subsidence is the key to assessing mining risks and adjusting mining planning. This study determined the available conditions of the logistic model for mining subsidence prediction through analysis and simulation experiments and proposed a method for the dynamic prediction of mining subsidence based on small baseline subset (SBAS)-interferometric synthetic aperture radar (InSAR) technology and the logistic model. Firstly, the time-series subsidence data of a mining area was obtained using the SBAS-InSAR technology. Then, taking the time series subsidence data as the data for fitting, the parameters of the logistic model were calculated pixel by pixel by using the trust region algorithm. Then, the pixel range in which the subsequent subsidence can be predicted was determined according to the available conditions of the logistic model. Finally, according to the Logistic model, the subsequent subsidence within the predictable range was predicted. This method was applied to a certain mining area in Erdos City, Inner Mongolia for tests, and the prediction results were verified using the InSAR monitoring results of corresponding dates. The predicted results after 36 days and 108 days of ming had the root mean square error (RMSE) of 0.010 1 m and 0.023 6 m, respectively, and their proportion with prediction errors of less than 0.03 m reached 98.9% and 89.3%, respectively. These results indicate that the method for dynamic prediction proposed in this study has high prediction accuracy.
Keywords SBAS-InSAR      Logistic model      coal mining subsidence      dynamic prediction     
ZTFLH:  TD325  
  P237  
Corresponding Authors: JI Min     E-mail: zixingxu@whu.edu.cn;jamesjimin@126.com
Issue Date: 20 June 2022
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Zixing XU
Min JI
Guo ZHANG
Zhenwei CHEN
Cite this article:   
Zixing XU,Min JI,Guo ZHANG, et al. Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 20-29.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021354     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/20
Fig.1  The influence of different coverage of the fitting data on the subsidence process on the fitting accuracy
Fig.2  Flow chart of calculating Logistic model parameters by trust region algorithm
Fig.3  Flow chart of subsidence dynamic prediction method in mining area
Fig.4  Location of the study mining area
序号 日期 入射角/(°) 序号 日期 入射角/(°) 序号 日期 入射角/(°)
1 2017/9/20 33.914 15 2018/3/19 33.910 29 2018/9/27 33.915
2 2017/10/2 33.913 16 2018/3/31 33.912 30 2018/10/9 33.912
3 2017/10/14 33.912 17 2018/4/12 33.917 31 2018/10/21 33.912
4 2017/10/26 33.910 18 2018/5/6 33.915 32 2018/11/2 33.910
5 2017/11/7 33.909 19 2018/5/18 33.915 33 2018/11/14 33.908
6 2017/11/19 33.909 20 2018/5/30 33.916 34 2018/11/26 33.908
7 2017/12/1 33.908 21 2018/6/11 33.915 35 2018/12/8 33.909
8 2017/12/13 33.908 22 2018/6/23 33.916 36 2018/12/20 33.906
9 2017/12/25 33.907 23 2018/7/5 33.915 37 2019/1/1 33.907
10 2018/1/6 33.907 24 2018/7/17 33.917 38 2019/1/25 33.908
11 2018/1/30 33.909 25 2018/7/29 33.915 39 2019/2/6 33.908
12 2018/2/11 33.909 26 2018/8/22 33.915 40 2019/2/18 33.909
13 2018/2/23 33.909 27 2018/9/3 33.915 轨道号 Path: 11 Frame: 121
14 2018/3/7 33.910 28 2018/9/15 33.917 极化方式 VV
Tab.1  Parameters of Sentinel-1A images
Fig.5-1  Time series subsidence
Fig.5-2  Time series subsidence
Fig.6  Logistic model fitting situation
Fig.7  Prediction subsidence diagram
Fig.8  Consistency verification diagram
Fig.9  Statistical bar graph of prediction error
Fig.10  Spatial distribution of prediction error
Fig.11  Relationship between the occurrence time of minimum acceleration point and prediction error
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