Rock discontinuities are essential for the mechanical behavior and stability of rock mass. Previous approaches for characterizing discontinuities either rely on limited handcrafted features (point normals, point curvatures, point densities, and so on) or fail to classify discontinuities, making them unsuitable for complex and large-scale scenes. To cope with these problems, an end-to-end point-based deep learning method that can automatically learn rich and high-dimensional features and classify discontinuities was developed in this study. Firstly, a roadcut and part of a natural slope were selected to train the developed network and assess its performance. Subsequently, the trained network was used to classify the remaining part of the slope. Finally, the “Density-Based Scan Algorithm with Noise” (DBSCAN) and principal component analysis (PCA) algorithms were employed to extract individual discontinuities and calculate their orientations. The two cases achieved a global accuracy (GA) of 97.25% and 94.56%, respectively, and a mean intersection over union (MIoU) of 93.77% and 88.66%, respectively, indicating the excellent performance of the network. Meanwhile, the average error in dip angle and dip direction was 0.67° and 3.33°, respectively, proving the characterization ability of the developed method was satisfactory. Furthermore, the presented method exhibits strong robustness and the potential to characterize large-scale rock discontinuities with noise. This method facilitates the application of deep learning in geosciences and provides geologists and geological engineers with a new idea for rock discontinuity characterization.

中文翻译：

---

使用基于点的深度学习方法从大规模点云中表征岩石不连续性


岩石不连续性对于岩体的力学行为和稳定性至关重要。以前描述不连续性的方法要么依赖于有限的手工特征（点法线、点曲率、点密度等），要么无法对不连续性进行分类，使得它们不适合复杂和大规模的场景。为了解决这些问题，本研究开发了一种基于点的端到端深度学习方法，可以自动学习丰富的高维特征并对不连续性进行分类。首先，选择路堑和部分自然坡度来训练所开发的网络并评估其性能。随后，训练后的网络用于对斜坡的剩余部分进行分类。最后，采用“基于密度的噪声扫描算法”（DBSCAN）和主成分分析（PCA）算法来提取各个不连续性并计算它们的方向。两种情况的全局准确率（GA）分别达到97.25%和94.56%，平均交并集（MIoU）分别达到93.77%和88.66%，表明网络具有优异的性能。同时，倾角和倾角方向的平均误差分别为0.67°和3.33°，证明该方法的表征能力令人满意。此外，所提出的方法表现出很强的鲁棒性，并且具有用噪声来表征大规模岩石不连续性的潜力。该方法促进了深度学习在地球科学中的应用，为地质学家和地质工程师提供了岩石面连续性表征的新思路。