URAL: Uncertainty-driven Region-based Active Learning for data-efficient fault interpretation

Deep learning-based fault interpretation has recently emerged as a dominant approach, offering significant advancements in automation and accuracy. However, its effectiveness is often constrained by the reliance on large amounts of expert-labeled data. Moreover, the sparse and elongated nature of faults makes them difficult to identify accurately, especially when only limited annotations are available. To overcome these challenges, we propose a novel Uncertainty-driven Region-based Active Learning (URAL) workflow. URAL employs an uncertainty-based sampling strategy to select informative samples and uses coordinate-based K-means clustering to identify sample-dense regions as annotation areas, directing expert attention to fault-rich regions. Additionally, we introduce an automatic cropping strategy along fault extension directions to increase the fault-to-background ratio in training patches, mitigating the severe imbalance between faults and non-faults. Applied to Thebe, a real large-scale, well-labeled seismic dataset, URAL achieves performance comparable to fully supervised learning using only 3.7% of the labeled data. Moreover, when compared to classical AL methods, URAL achieves similar accuracy with only 21% of the labeled pixels, further demonstrating its ability to minimize labeling effort while maintaining high performance. The proposed method offers a scalable solution for large-scale seismic fault interpretation.