Application Of UAV LiDAR In Mining Subsidence Monitoring

High-intensity coal mining leads to the destruction of the surface and the gradual deterioration of the ecological environment,which violates the principle of green mining and greatly restricts the sustainable development of the mining area.Therefore,it is of great significance to strengthen the monitoring of surface deformation caused by coal mining in mining areas for ecological environment restoration and disaster prevention in mining areas.Unmanned Aerial Vehicle(UAV)Light Detection and Ranging(LiDAR)can quickly collect geographic information in a wide range of three-dimensional space.With its advantages of high efficiency,rapidity and economy,it is widely used in resource protection,communication line inspection,surveying and mapping geographic information and so on.Because of the poor adaptability of LiDAR point cloud filtering algorithm,it is difficult to obtain the ground point cloud robustly,which leads to the poor accuracy of the generated ground Digital Elevation Model(DEM),and it still has certain limitations in mining subsidence monitoring.Combined with the"Research and Application Project of Rock Movement Observation in Shallow Buried Deep Group Mining in Yushen Mining Area ",taking a working face of Liangshuijing Coal Mine as the research area,UAV LiDAR technology is applied to mining Subsidence monitoring,the research contents are as follows:(I)The current mainstream point cloud filtering algorithms are compared and analyzed from three aspects:point cloud error,point cloud profile inspection and visual discrimination.Aiming at the terrain features such as low sandy vegetation and sandy hills in Yushen mining area,the progressive triangulation encryption filtering algorithm is improved,and the errors of point cloud Ⅰ and Ⅱ after improvement are 0.76%and 2.01%respectively,which reduces the phenomenon that steep slopes are usually mistakenly divided into non-ground points in the original algorithm and effectively improves the accuracy of point cloud filtering algorithm.(2)The influence of DEM grid size and point cloud density on DEM accuracy is studied.Through experimental analysis,it is believed that in this study area and similar terrain,the optimal grid size of DEM generated by point cloud interpolation is 0.1m,and the ground point cloud density should be controlled at 90～100 points per square meter.(3)According to the high-precision DEM on the ground,the UAV LiDAR area monitoring results are calculated and the accuracy is verified by the leveling data,and the error rule is analyzed;for the situation of the 4-2 coal seam overlying the 4-3 coal seam of the working face in the study area,the numerical simulation of the multi-coal seam mining subsidence is carried out,the subsidence results are analyzed,and the subsidence law of the multi-coal seam repeated mining is explored.The results show that the RMSE of the single-point measurement by UAV LiDAR is 0.264m;The surface subsidence in the study area is obtained by DEM stacking and difference.Due to the elimination of systematic errors,the RMSE of the surface subsidence is increased to 0.085m.It indicates that this technology has certain applicability in mining subsidence monitoring of shallow buried coal seam and large mining height coal seam;The numerical simulation results show that although the 4-2 coal seam collapsed for a long time after mining,there are still a lot of cracks and cavities.It is difficult for the overlying strata to reach a stable state in a short period of time,the mining of the 4-3 coal seam caused further disturbance to the overlying strata of the 4-2 coal seam,causing the average surface subsidence value to be greater than the average mining height during the mining process of the 4-3 coal seam,indicating that the damage to the overlying strata caused by multi-coal seam mining is further intensified,the subsidence is larger,and the destructibility is stronger,and it is necessary to strengthen the protection of the surface during multi-coal seam mining.