Identifying rock mass discontinuities and their plane orientation are crucial factors when determining rock mass characteristics. Rock mass discontinuity mapping is fundamentally dependent on joint trace surveying since traces of them are most often the only visible features in rock outcrops. Traditional methods for joint trace surveying using tape and a geological compass are challenging, time-consuming, and hazardous. Fortunately, non-contact measuring techniques offer the advantage of generating accurate objective records of rock masses and enabling the measurement of discontinuities from digital surface models and 3D point clouds of outcrops without requiring direct access to the rock mass and associated constraints. Herein, an innovative approach for identifying discontinuity planes in rock formations using 3D trace data is presented. We introduce the concept of curved and straight traces, with a curvature index indicating the trace's accuracy by representing its discontinuity plane. In addition, we identified co-planar traces by analyzing intersecting straight traces, thereby further contributing to discontinuity plane determination. The methodology's effectiveness was established through validation using a predefined 3D trace network of discontinuity planes with known orientations on a 3D digital rock outcrop model. The methodology was then applied to trace data collected from an actual rock outcrop. The algorithm successfully matched >66% of traces with their corresponding discontinuity planes. After clustering, 80% of the identified planes aligned with principal joint sets within the rock mass. These identified planes accurately aligned with their expected jointing pattern, thereby validating the robustness of the method. Despite challenges presented by discontinuous and complex trace segments obtained from semi-automatic trace detection techniques, the algorithm effectively processed such traces, thereby enabling a comprehensive understanding of the rock mass's jointing system. This enables swift identification of the main joint orientations. We leveraged stereonet analysis to identify principal joint sets using kernel-based and fuzzy C-means clustering techniques. Our approach represents an important advancement in the characterization of rock mass structural properties.

中文翻译：

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使用 3D 轨迹网络分析进行关节方向识别的半自动方法

识别岩体不连续性及其平面方向是确定岩体特征时的关键因素。岩体不连续性测绘从根本上依赖于联合痕迹测量，因为它们的痕迹通常是岩石露头中唯一可见的特征。使用卷尺和地质罗盘进行联合追踪测量的传统方法具有挑战性、耗时且危险。幸运的是，非接触式测量技术具有生成岩体的准确客观记录的优势，并且能够根据数字表面模型和露头的 3D 点云测量不连续性，而无需直接访问岩体和相关约束。本文提出了一种使用 3D 轨迹数据识别岩层中不连续面的创新方法。我们引入了弯曲和直线迹线的概念，曲率指数通过表示迹线的不连续平面来指示迹线的精度。此外，我们通过分析相交的直线迹线来识别共面迹线，从而进一步有助于不连续平面的确定。该方法的有效性是通过在 3D 数字岩石露头模型上使用具有已知方向的不连续平面的预定义 3D 追踪网络进行验证来确定的。然后将该方法应用于追踪从实际岩石露头收集的数据。该算法成功地将超过 66% 的迹线与其相应的不连续平面匹配。聚类后​​，80% 的已识别平面与岩体内的主要节理组对齐。这些识别的平面与其预期的接合模式精确对齐，从而验证了该方法的稳健性。尽管半自动痕迹检测技术获得的不连续且复杂的痕迹段带来了挑战，但该算法有效地处理了此类痕迹，从而能够全面了解岩体的节理系统。这使得能够快速识别主要关节方向。我们利用基于核的模糊 C 均值聚类技术，利用立体网分析来识别主要联合集。我们的方法代表了岩体结构特性表征方面的重要进步。