Abstract: To extract rapidly the occurrence information from three-dimensional point cloud data of geological structural plane, we completed a set of algorithms that automatically fit the plane and calculate the structural plane occurrence by programming using Python. First of all, we expounded the principles of least squares and principal component analysis as well as the solution of plane equations; secondly, we designed the above two algorithms using Python language, and introduced singular value decomposition to help solve the principal component vector, and gave the key code and program flow; finally, we compared the two algorithms and analyzed their errors, and converted the plane equation into occurrence information. We then applied the present method to international public experimental data. Results manifested that for specified structural planes manually intercepted, the calculation error of structural plane occurrence was less than 1° on average compared with the actual value, not exceeding 2°; for irregular structural planes generated by unsupervised clustering segmentation, the calculation error was less than 4° on average compared with the actual value, not exceeding 8°. The error of principal component analysis method was even smaller. The results demonstrated that the present method is of high accuracy and convenience, and hence meeting practical engineering requirements.

Key words: structural plane occurence, three-dimensional point cloud data, plane fitting, least square method, principal components analysis method, Python