Mining Subsidence Based on Integrated SBAS-InSAR and Unmanned Aerial Vehicles Technology  

作　　者：CHEN Xuewei CHEN Jianping WANG Genhou ZHANG Qian ZHENG Yanwei 

机构地区：[1]School of Earth Sciences and Resources,China University of Geosciences,Beijing 100083,China [2]Beijing Key Laboratory of Development and Research for Land Resources Information,Beijing 100083,China

出　　处：《Journal of Ocean University of China》2025年第1期113-129,共17页中国海洋大学学报(英文版)

基　　金：funded by the Project from the Maqu Branch of Gannan Tibetan Autonomous Prefecture Ecological Environment Bureau,China(No.33412021021)。

摘　　要：The Small Baseline Subset InSAR(SBAS-InSAR)and unmanned aerial vehicles(UAVs)as common ocean-land technologies,have been extensively applied in subsidence,glacial movement,surface deformation,and maritime positioning and navigation.A novel method integrating SBAS-InSAR and UAV photogrammetry is used to analyze ground subsidence deformation in the Gesar gold mine located in Maqu,Northwest China.This approach uses SBAS-InSAR to calculate two-dimensional deformation data for capturing ascending and descending measurements.This method can provide precise information on small-sized deformations within mining regions.The deformation data obtained from UAVs and the vertical deformation data derived from InSAR are integrated to generate comprehensive and accurate ground subsidence data from the mining district.Results demonstrate that using a combined InSAR(vertical)and UAV technique to analyze surface subsidence in mining districts resolves inconsistency between the line-of-sight and deformation orientations.Furthermore,the incoherence issue of InSAR in regions with large deformation gradients is addressed,while the inherent errors of UAV monitoring of mining surface subsidence are mitigated.The genetic algorithm(GA)-backpropagation(BP)neural network algorithm is combined with InSAR data to predict subsidence in collapsed areas.As observed,the GA-BP algorithm has the smallest residual under the same training samples.Therefore,the GA-BP neural network model can effectively predict surface subsidence in mining areas and can be used for subsequent subsidence prediction.

关 键 词：SBAS-InSAR two-dimensional deformation solution UAV photogrammetry data fusion subsidence prediction 

分 类 号：TN9[电子电信—信息与通信工程]