A Machine Learning-based Method for Identifying Segmented Fault Surfaces Through Hypocenter Clustering

We present a method for automatically identifying segmented fault surfaces through the clustering of earthquake hypocenters without prior information. Our approach integrates density-based clustering algorithms (DBSCAN and OPTICS) with principal component analysis (PCA). Using the spatial distribution of earthquake hypocenters, DBSCAN detects primary clusters, which represent areas with the highest density of connected seismic events. Within each primary cluster, OPTICS identifies nested higher-order clusters, providing information on their quantity and size. PCA analysis is then applied to the primary and higher-order clusters to assess eigenvalues, enabling the differentiation of seismicity associated with planar features and distributed seismicity that remains uncategorized. The identified planes are subsequently characterized in terms of their location and orientation in space, as well as their length and height. By applying PCA analysis before and after OPTICS, a planar feature derived from a primary cluster can be interpreted as a fault surface, while planes derived from high-order clusters can be interpreted as fault segments within the fault surface. The consistency between the orientation of illuminated fault surfaces and fault segments, and that of the nodal planes of earthquake focal mechanisms calculated along the same faults, supports this interpretation. We show applications of the method to earthquake hypocenter distributions from various seismically active areas (Italy, Taiwan, California) associated with faults exhibiting diverse kinematics.