This research evaluates the influence of various discontinuity data acquisition methods on three-dimensional rockfall susceptibility assessments, employing a high-resolution 3D point cloud of the Špičunak rock slope in Croatia. Discontinuity mapping was conducted utilising manual and semi-automated methods. Manual mapping was done in open-source software CloudCompare, while semi-automated mapping was done using Discontinuity Set Extractor and two CloudCompare plug-ins, qFacet Fast Marching and qFacet Kd-Tree algorithm. Significant differences were observed between the methods in terms of the number of identified discontinuity planes, sets, mean orientation values, and weighted densities associated with specific sets. Based on the mapping results, three-dimensional rockfall susceptibility maps were generated. The 3D rockfall susceptibility assessment employed a Kinematic Hazard Index (KHI) for planar, wedge, and flexural toppling failure mechanisms, which determines the percentage of discontinuity planes that meet the geometrical conditions specific to each type of failure. The findings underscore that the mapping results greatly affect the 3D rockfall susceptibility assessment, impacting the identification of potential rockfall source areas. The study emphasizes the importance of integrating manual and semi-automated mapping for reliable 3D rockfall susceptibility assessment. Additionally, this study evaluated the effect of varying the number of discontinuity orientation input data on rockfall susceptibility by comparing nine scenarios, each involving a reduction in the number of discontinuity orientation data. The results indicate that accurate discontinuity mapping is more critical than the sheer number of mapped discontinuities for reliable rockfall susceptibility assessment. Therefore, prioritizing the precise determination of discontinuity set numbers and their associated weighted densities is essential for reliable susceptibility evaluations.

中文翻译：

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不连续性数据采集方法对使用高分辨率 3D 点云进行落石敏感性评估的影响


本研究采用克罗地亚 Špičunak 岩石边坡的高分辨率 3D 点云，评估了各种不连续性数据采集方法对三维落石敏感性评估的影响。利用手动和半自动方法进行不连续性映射。手动映射是在开源软件CloudCompare中完成的，而半自动映射是使用Discontinuity Set Extractor和两个CloudCompare插件qFacet Fast Marching和qFacet Kd-Tree算法完成的。在所识别的不连续平面、组、平均方向值以及与特定组相关的加权密度方面，观察到这些方法之间存在显着差异。根据测绘结果，生成三维落石敏感性图。 3D 落石敏感性评估采用了针对平面、楔形和弯曲倾倒失效机制的运动学危险指数 (KHI)，该指数确定了满足每种失效类型特定几何条件的不连续平面的百分比。研究结果强调，测绘结果极大地影响了 3D 落石敏感性评估，影响了潜在落石源区域的识别。该研究强调了集成手动和半自动测绘对于可靠的 3D 落石敏感性评估的重要性。此外，本研究通过比较九种情况（每种情况涉及不连续方向数据数量的减少），评估了改变不连续方向输入数据数量对落石敏感性的影响。 结果表明，对于可靠的落石敏感性评估，准确的不连续性测绘比测绘的不连续性的绝对数量更为重要。因此，优先考虑精确确定不连续集数量及其相关的加权密度对于可靠的磁敏度评估至关重要。