Yuan Jie, Liu Guangwei, Chai Senlin, Guo Weiqiang, Huang Yunlong
College of Mining, Liaoning Technical University, Fuxin, 123000, China.
Sci Rep. 2025 Jul 1;15(1):22029. doi: 10.1038/s41598-025-05945-7.
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.
高精度三维煤层模型对于露天煤矿的精细化开采设计和精确生产至关重要。然而，由于初始数据稀疏以及获取地质真实数据存在挑战等限制，这些模型往往是静态的，空间分辨率不足。我们提出了一种无人机煤岩识别与三维煤层模型动态校正技术，该技术生成无人机彩色点云，并应用改进的区域生长算法和Alpha形状算法进行煤岩识别和边界点提取。在露天煤矿的持续开采过程中，动态整合最新的地质真实数据，并使用空间插值校正技术计算空间插值点的校正值。然后通过TIN更新和生长重建算法对模型进行动态校正，不断提高煤层三维模型的精度。应用结果表明，煤岩识别和边界点提取效果显著，准确率分别为97.44%和91.85%。校正前后三维煤层模型的标准差为0.32米。现场测量表明，校正后模型的平均高程误差为0.34米，与初始模型相比误差降低了78.76%。
