UAV coal-rock recognition and 3D coal seam model dynamic correction technology in open-pit mine

High-precision 3D coal seam models are crucial for refined mining design and precise production in open-pit coal mining. However, due to limitations like sparse initial data and challenges in obtaining geological realism data, these models are often static with insufficient spatial resolution. We propose a UAV coal-rock recognition and 3D coal seam model dynamic correction technology, which generates a UAV color point cloud and applies an improved region-growing algorithm and Alpha-shape algorithm for coal-rock identification and boundary points extraction. During the ongoing mining process in open-pit mines, the latest geological realistic data is dynamically integrated, and the spatial interpolation correction technique is used to calculate the correction values of the spatial interpolation points. The model is then dynamically corrected through a TIN update and growth reconstruction algorithm, continuously improving the accuracy of the coal seam 3D model. Application results show that the coal-rock recognition and boundary points extraction are highly effective, with accuracies of 97.44% and 91.85%, respectively. The standard deviation of the 3D coal seam model before and after correction is 0.32 m. Field measurements reveal that the average elevation error of the corrected model is 0.34 m, representing a 78.76% reduction in error compared to the initial model.