Characterisation of structural discontinuity sets in exposed rock faces of underground mine cavities is essential for assessing rock-mass stability, excavation safety, and operational efficiency. UAV and other mobile laser-scanning techniques provide efficient means of collecting point clouds from rock faces. However, the development of a robust and efficient approach for automatic characterisation of discontinuity sets in real-world scenarios, like fully enclosed rock faces in cavities, remains an open research problem. In this study, a new approach is proposed for automatic discontinuity set characterisation that uses a single-shot filtering strategy, an innovative cyclic orientation transformation scheme and a hierarchical clustering technique. The single-shot filtering step isolates planar regions while robustly suppressing noise and high-curvature artefacts in one pass using a signal-processing technique. To address the limitations of Cartesian clustering on polar orientation data, a cyclic orientation transformation scheme is developed, enabling accurate representation of dip angle and dip direction in Cartesian space. The transformed orientations are then characterised into sets using a hierarchical clustering technique, which handles varying density distributions and identifies clusters without requiring user-defined set numbers. The accuracy of the method is validated on real-world mine stope and against ground truth obtained using manually handpicked discontinuity planes identified with the Virtual Compass tool, as well as widely used automated structure mapping techniques. The proposed approach outperforms the other techniques by exhibiting the lowest mean absolute error in estimating discontinuity set orientations in real-world stope data with errors of 1.95° and 2.20° in nominal dip angle and dip direction, respectively, and dispersion errors lying below 3°.

翻译：地下矿山空区暴露岩体结构不连续面组的表征对于评估岩体稳定性、开挖安全性和作业效率至关重要。无人机及其他移动激光扫描技术为岩面点云采集提供了高效手段。然而，针对实际场景（如空区内完全封闭的岩面）开发鲁棒高效的不连续面组自动表征方法，仍是一个待解决的研究难题。本研究提出一种新的不连续面组自动表征方法，该方法采用单次滤波策略、创新的循环方位变换方案以及层次聚类技术。单次滤波步骤通过信号处理技术一次性隔离平面区域，同时鲁棒地抑制噪声和高曲率伪影。为克服笛卡尔聚类对极坐标方位数据的局限性，本研究开发了循环方位变换方案，实现了倾角和倾向在笛卡尔空间中的精确表征。随后使用层次聚类技术将变换后的方位表征为不连续面组，该方法能够处理非均匀密度分布，且无需用户预先定义组数即可识别聚类。本方法的准确性在真实矿山采场数据上得到验证，并与采用Virtual Compass工具人工拾取的不连续面基准数据及广泛使用的自动结构映射技术进行对比。所提方法在真实采场数据的不连续面组方位估计中表现出最低的平均绝对误差：名义倾角与倾向误差分别为1.95°和2.20°，离散误差均低于3°，性能优于其他对比技术。