Automated method for extracting and analysing the rock discontinuities from point clouds based on digital surface model of rock mass

Abstract

Often when rock discontinuities with complex distributions occur in steep terrain, it is difficult to rapidly survey and accurately measure their spatial distribution by traditional surveying and mapping methods. This paper presents a methodology for automated extraction of rock discontinuities from a point cloud and the resulting 3D digital surface model of the rock mass. First, feature planes of rock discontinuities are identified and classified using both their orientation and position in a double-nested Mean-shift Clustering Algorithm. Second, the points corresponding to feature planes are extracted using a Region Growth Algorithm and seed points. Finally, geological information is acquired by analysing the geometric features of the extracted sub-set of points from the point cloud. This approach can directly extract planar features associated with joints and it eliminates spurious points in a point cloud associated with objected such as vegetation. A case study of a rock slope along a highway is presented using the proposed method. A sensitivity analysis of relevant clustering parameters in the Mean-shift Clustering Algorithm is conducted to acquire their optimal values and to assess their robustness. The proposed method produces results that agree with the traditional survey methods and greatly improves the survey efficiency.