Abstract:

In order to study the problem of surface subsidence caused by underground mining, a study on the methods of surface subsidence monitoring and prediction was carried out in Yangcheng County, Jincheng City, Shanxi Province. Firstly, Sentinel-1 SAR images (81 views) from January 2018 to December 2020 were acquired, combined with digital elevation model (DEM), generic atmospheric correction online service (GACOS), and precision orbit data, and SBAS-InSAR technique was used to monitor the regional surface deformation in a refined way, revealing its temporal evolution and spatial distribution characteristics (the maximum subsidence rate reached -27.84 mm/a). Then, a hybrid prediction model (VMD-BP) based on the combination of variational modal decomposition (VMD) and back propagation (BP) neural network was constructed; finally, the prediction performance of this model was compared with that of the traditional LSTM model and VMD-LSTM model. The results show that the VMD-BP model significantly improves the prediction accuracy, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) at the test point (point a) are as low as 0.278 01 mm, 0.234 29 mm, and 0.39%, respectively, which are much better than the LSTM and the VMD-LSTM models.

Key words: surface subsidence, deformation prediction, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique, long short-term memory (LSTM) model, variational mode decomposition-back propagation (VMD-BP) model