Abstract: Accurately monitoring and predicting the surface movement and deformation caused by coal mining in mountainous areas is an important means to prevent damage to buildings a nd structures,landslides,and collapses in mountainous areas. In response to the existing difficulty in achieving integrated detection and prediction for subsidence in mountainous mining areas,which leads to low monitoring efficiency and the inability to achieve efficient prediction of monitoring results,this paper organically integrates the grid method,C2C algorithm,and moving window traversal method based on the echo characteristics of airborne LiDAR,and proposes an integrated method for subsidence monitoring and prediction in mountainous areas based on airborne LiDAR. This method consists of two parts:monitoring and prediction,and realizes a process-oriented operation integrating data collection,point cloud extraction,and parameter calculation. Taking a typical mountainous terrain in a certain mining area in Shanxi as an example,the feasibility of this method is explored. The study results show that this algorithm can achieve high-precision surface monitoring. Compared with the actual measurement data of leveling,the cumulative mean error does not exceed 40 mm,and the overall error is better than 5%. At the same time,it can quickly calculate a large number of surface characteristic parameters required for the subsidence prediction model in mountainous mining areas,which has a good reference significance for subsidence monitoring and prediction in mountainous areas.

Key words: mining subsidence,subsidence monitoring and prediction in mountain area,airborne LiDAR,echo characteristics,surface characteristic parameters