Prediction of insar urban surface time-series deformation using deep neural networks

Surface deformation is a complex geological phenomenon with potential threats to urban construction and human safety. Time-series prediction of surface deformation has an important role in mitigating the impact of such hazards. This study utilizes the small baseline subset interferometric synthetic aperture radar method (LiCSBAS) to monitor the long-time surface deformation in the main urban area of Kunming, and constructs a deep neural network model (TCN-GRU) to predict typical surface deformation points in the study area. The results show that Kunming city experienced a surface deformation rate of -44.23-17.65 mm/y from March 2018 to July 2022, along with the presence of five subsidence funnels. The TCN-GRU model demonstrates the best short-term prediction performance on different datasets, and significantly outperforms other traditional deep learning models. The results of this paper can serve as a valuable reference for the study of urban surface deformation.