46 / 2023-03-30 15:28:54

Study on Prediction Model of sSurface Subsidence Driven by Starry Multi-platform Monitoring Data

starry ground monitoring,InSAR monitoring, UAV/LiDAR, data-driven, settlement prediction model

Aiming at the large-scale surface subsidence disaster caused by coal mining, multi-platform monitoring data such as GNSS, InSAR, UAV/LiDAR, leveling survey and borehole peeper are adopted to conduct fusion algorithm analysis of multi-platform data by using Kalman filter model. GNSS and level point data were used to fit the point cloud measurement data obtained by InSAR and UAV/LiDAR platforms, and the deep learning method was used to model the time series data, and the inversion method of surface subsidence prediction model parameters was established. Through the comprehensive analysis of the starry ground multi-platform monitoring data of Zhaozhuang Coal industry 2319 working face and Changping Coal industry 5201 first mining face, the experiment shows that the established starry ground multi-platform monitoring data surface subsidence prediction model has high accuracy, which provides a scientific basis for obtaining high-precision prediction parameters under the terrain mining conditions in mountainous areas.