A Deep Learning Based Method for Local Subsidence Detection and InSAR Phase Unwrapping: Application to Mining Deformation Monitoring

Mining induced subsidence seriously damages the ecological environment and may cause casualties. Therefore, the rapid and reliable monitoring is particularly important. However, due to severe noise and dense fringes, traditional InSAR methods often severely underestimate the deformation rate. Here, we propose a new processing flow and develop two deep convolutional neural networks for fast detection and phase unwrapping of local subsidence cones. The proposed method is applied to Datong City, Shanxi Province, which is rich in mining activates. The processing results verify the reliability of the method.