Subsidence Monitoring Using Lidar and Morton Code Indexing

Abstract

Point-based data-acquisition technology in traditional survey engineering does not provide complete data about mining-induced subsidence basins. To overcome these shortcomings, this research applied light detection and ranging (LIDAR) data, obtained with a terrestrial laser scanner (TLS) for monitoring the surface deformation of mining areas, to acquire full data about mining-induced subsidence basins. First, to improve the organization efficiency of LIDAR data, the decimal Morton code–based indexing method was proposed for discrete-grid indexing to organize LIDAR data according to original point coordinates, to avoid the generation of grids without data, and to build a topological relationship among scattered points. Thus, this approach enabled highly efficient access of LIDAR data and restoration of coordinates for each point. In the end, the processed data were applied in engineering practice. The subsidence curves of two sections of the subsidence basin, in both strike and dip directions measured with a high-grade control survey, were compared with the subsidence curves extracted from the LIDAR data. The good coincidence effect suggests that using LIDAR data to build the subsidence basin model can result not only in richer surface-deformation information than the traditional methods but also in higher monitoring precision.