Detecting Fault Structures from Earthquake Sequences via Unsupervised Learning

Abstract. This study develops a systematic framework to detect potential fault structures from earthquake sequences by integrating unsupervised learning and three-dimensional spatial analysis. Two major events in eastern Taiwan, EQ2018 and EQ2024, are analyzed using DBSCAN clustering, validated by the Silhouette Score, followed by Principal Component Analysis (PCA) to extract fault-plane geometries. The clustering results reveal both mapped and previously unrecognized fault orientations, with PCA-derived planes largely consistent with centroid moment tensor solutions of the largest-magnitude events. EQ2018 ruptures were confined to shallow crustal levels (<20 km), dominated by west-dipping planes, whereas EQ2024 exhibited greater depth variability, multiple dipping directions, and complex rupture geometries involving both onshore and offshore fault systems. Three-dimensional visualization further highlights the interplay between known active faults (e.g., Central Range, Milun, Lingding) and latent structures, underscoring the heterogeneous nature of rupture propagation in tectonically transitional zones. While PCA effectively captures dominant planar trends, limitations remain in representing curved or arc-shaped geometries. Overall, the proposed workflow demonstrates the utility of combining clustering and PCA to delineate subtle fault structures, offering a robust tool for advancing seismotectonic interpretation and improving seismic hazard assessment.