Convex–concave feature-based fragmentation calculation for blast muck piles in open-pit copper mines from 3D laser point cloud data

The convex – concave feature of 3D laser point cloud data (3D LPCD) is introduced in fragmentation calculation for blast muck piles in open-pit copper mines to improve the calculation efficiency and accuracy of the blast fragmentation of muck piles (BFMP). First, a supervoxel segmentation method based on point cloud curvature feature (named as CFSS method) is constructed to overcome drawbacks, such as the color difference of the same blast muck pile surface is not evident and has little effect on the 3D LPCD segmentation. Then, a supervoxel clustering method based on boundary extraction (named as BESC method) is constructed to cluster the supervoxel data of the blast muck piles. Finally, on the basis of the supervoxel clustering results, the automatic fragment recognition and fragmentation calculation of the blast muck piles are realized, and the evaluation metrics for the calculation results of the BFMP are introduced. Dexing Copper Mine, the largest open-pit copper mine in China, is considered the study area. Case study results and discussion tests reveal that the ratios of big blocks in the three selected test areas are 0.91%, 0.21%, and 0.83%, and more than 80% of the fragments in the blast muck piles are less than 300 mm, satisfying the actual situation and confirming the effectiveness and accuracy of the proposed method, which is significant for optimizing blast designs and improving mining efficiency in open-pit operations.