MC-DBRG: 3D Point Cloud Instance Segmentation for Automatic Seismic Fault Interpretation via Multi-Scale Contour Constraints Delaunay-Based Region Growing

Abstract

Automatic fault interpretation is essential for seismic geological modeling; however, fault intersections, noise interference, and topological complexity frequently lead to over-segmentation or under-segmentation. To address these challenges, we propose multi-scale contour constraints Delaunay-based region growing (MC-DBRG), an instance segmentation method for three-dimensional (3D) fault point clouds. This approach effectively extracts individual fault instances from fault probability volumes. The pipeline begins with voxel downsampling and multi-scale feature extraction applied to the fault point cloud. A Gaussian Mixture Model then performs binary classification to delineate explicit and closed fault contour walls. These walls function as physical barriers during the subsequent Delaunay triangulation-based region growing, which initializes from non-contour smooth points. Implementing these constraints guides the growth process and prevents topological adhesion among intersecting faults. To correct over-segmentation caused by data discontinuities in the preliminary results, we introduce a global manifold secondary geometric merging strategy. A macroscopic planar trend constraint is subsequently applied to accurately reassign and backfill contour points and noise. Experimental results indicate that MC-DBRG resolves boundary ambiguity and spatial adhesion within complex intersecting faults. The generated fault models preserve high topological integrity and precise instance labels, establishing a reliable foundation for automated seismic geological modeling.