An efficient method for extracting and clustering rock mass discontinuities from 3D point clouds
An efficient method for extracting and clustering rock mass discontinuities from 3D point clouds
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DOI：
10.1007/s11440-023-01803-w
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发表时间：
2023-01
期刊：
影响因子：
5.7
通讯作者：
Xiaoyuan Yi;W. Feng;Dingchang Wang;Ronghao Yang;Yunpeng Hu;Yongjian Zhou
中科院分区：
文献类型：
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作者：
Xiaoyuan Yi;W. Feng;Dingchang Wang;Ronghao Yang;Yunpeng Hu;Yongjian Zhou
岩体不连续性是岩体结构的基本要素，它调节其力学和水文地质特性，具有重大的工程意义。在这项研究中，我们提供了一种基于岩石露头的 3D 点云数据提取不连续性并执行有效聚类分析的方法。首先，利用K-d树方法组织点云数据，以便快速计算法向量和曲率。然后使用多规则区域生长算法提取不连续性，并计算不连续性的倾角方向和倾角。然后，采用改进的最远点采样算法和肘部法对K-means算法进行优化，最终自动确定岩体的主要间断面集合和平均方向。该方法通过两个真实案例进行测试，并与国际研究人员的方法进行比较，发现本文提出的方法具有良好的精度，与倾角方向和倾角的平均偏差小于5°。与多个点云数据集的对比测试表明，该方法可以高效地从海量岩石露头点云数据中提取不连续性并进行聚类分析。所提出的方法为地质学家和地质工程师提供了一种快速有效地了解岩石露头不连续性的新工具。
A rock mass discontinuity is a fundamental element of a rock mass structure that regulates its mechanical and hydrogeological properties and has significant engineering implications. In this research, we offer a method for extracting discontinuities and performing efficient clustering analysis based on 3D point cloud data for rock outcrops. First, the K-d tree approach is utilized to organize the point cloud data so that the normal vector and curvature can be calculated quickly. Discontinuities are then extracted using a multirule region growing algorithm, and the dip directions and dip angles of the discontinuities are calculated. Then, the improved farthest point sampling algorithm and the elbow method are used to optimize the K-means algorithm and finally automatically determine the main discontinuity set and average direction for the rock mass. The approach is tested on two real cases and compared to the methods of international researchers, and it is discovered that the method proposed in this work shows good accuracy, with an average deviation of less than 5° from the dip direction and dip angle. Comparative tests with many point cloud data sets show that this new method can be used to extract discontinuities from massive-scale rock outcrop point cloud data and perform cluster analysis with high efficiency. The proposed method gives geologists and geological engineers a new tool for quickly and efficiently understanding rock outcrop discontinuities.