Time series prediction method of large-scale surface subsidence based on deep learning

Surface subsidence not only affects the sustainable development of social economy, but also threatens the safety of human life. High precision prediction of surface subsidence is of great significance for the prevention of geological disasters. However, the existing prediction methods are difficult to obtain reliable prediction results because of the model parameters or the lack of relevant data. For this problem, a method of surface subsidence prediction based on deep learning is proposed. Firstly, the multiple master-image coherent target small-baseline InSAR (MCTSB-InSAR) is used to obtain the inversion results of large area and high precision ground deformation time series. Secondly, the cyclic neural network is used as the network framework, and the long short-term memory (LSTM) model is used to learn the characteristics of ground settlement. Finally, the grid search method is used to adjust the model parameters, then get the optimal combination scheme of model parameters. The actual observation results show that the average absolute error (0.3 mm) of the prediction model proposed in this paper is reduced by 27.3% at least, and the average prediction accuracy of differential settlement is improved by 8.9% at least. The results of spatial pattern analysis show that the LSTM model is effective for the short-term prediction of large-scale time series deformation