Intelligent Extraction of Minimum Burden in Medium-Length Hole Blasting Using Combined Region Growing and DBSCAN

To address the difficulty of directly measuring the minimum burden in medium-length hole blasting and the low accuracy of single-algorithm extraction methods, this study proposes an automatic extraction method for the minimum burden based on combined region growing and DBSCAN. Using UAV-acquired three-dimensional point cloud data from open-pit mines, the elbow method is first applied to determine the clustering number of point cloud zenith distances, enabling initial extraction of the slope surface under roughness constraints. Subsequently, DBSCAN parameters are adaptively determined using the K-nearest neighbor average distance method, and density optimization is performed on the region-growing results to remove noise points such as rock protrusions and blasting residues, thereby refining the reconstruction of the free surface. Based on the reconstructed surface, the minimum burden is calculated using three-dimensional borehole modeling combined with the shortest Euclidean distance algorithm. Field experiments were conducted at the 5015 platform of the Huatailong open-pit mine in Tibet, with additional validation at the Qianshan limestone mine in Liaoyang and the Qidashan iron mine in Anshan. Results show that the proposed method effectively identifies slope free surfaces and accurately extracts the minimum burden. In the Huatailong case, the average absolute error was 0.077 m and the average relative error was 2.68%. The method provides a reliable basis for blasting fragmentation control and blast-hole pattern design in open-pit mines.