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
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.
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2022, Vol. 34 
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