Predictable Condition Analysis and Prediction Method of SBAS-InSAR Coal Mining Subsidence

Abstract

The forward prediction of mining subsidence in coal mining areas is key to evaluating mining risk and improving mine management plans. At present, the conditions that ensure successful application of the time function method to predict future subsidence in coal mining areas remain poorly known, and a prediction method suitable for large-scale subsidence prediction during coal mining has not been established. Based on the characteristics of logistic model and simulation experiments, we determined its predictable conditions for coal mining subsidence prediction. We propose a small baseline subset-synthetic aperture radar interferometric (SBAS-InSAR) coal mining subsidence prediction method with a predictable dynamic range based on the predictable condition obtained. The method uses time-series subsidence data obtained by SBAS-InSAR as the fitting data. The logistic model parameters are obtained pixel-by-pixel via the Levenberg–Marquardt (LM) algorithm. The predictable range is subsequently determined based on the predictable condition. Finally, future subsidence in the predictable range is predicted. The methodology was tested in two coal mining areas in Inner Mongolia–one a single working face mine and one a parallel double working face mine. The predicted results agree well with the InSAR monitoring results. The average root mean square error (RMSE) of the predicted results was 0.0119 m. In addition, we used the Knothe model to conduct comparative experiments without considering predictable conditions. The results reflect the advantages of our proposed method and the necessity of predictable conditions. The new prediction method is beneficial for risk assessment and coal mining planning.