The identification of rock mass discontinuities is critical for rock mass characterization. While high-resolution digital outcrop models (DOMs) are widely used, current digital methods struggle to generalize across diverse geological settings. Large-scale models (LSMs), with vast parameter spaces and extensive training datasets, excel in solving complex visual problems. This study explores the potential of using one such LSM, Segment anything model (SAM), to identify facet-type discontinuities across several outcrops via interactive prompting. The findings demonstrate that SAM effectively segments two-dimensional (2D) discontinuities, with its generalization capability validated on a dataset of 2426 identified discontinuities across 170 outcrops. The model achieves 0.78 mean IoU and 0.86 average precision using 11-point prompts. To extend to three dimensions (3D), a framework integrating SAM with Structure-from-Motion (SfM) was proposed. By utilizing the inherent but often overlooked relationship between image pixels and point clouds in SfM, the identification process was simplified and generalized across photogrammetric devices. Benchmark studies showed that the framework achieved 0.91 average precision, identifying 87 discontinuities in Dataset-3D. The results confirm its high precision and efficiency, making it a valuable tool for data annotation. The proposed method offers a practical solution for geological investigations.
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