Machine-Learning-based Relocation Analysis: Revealing the Spatiotemporal Changes in the 2019 Cotabato and Davao del Sur Earthquakes

From October to December 2019, the provinces of Cotabato and Davao del Sur in the Philippines experienced an earthquake sequence that involved five M~6 (Mw 6.4, 6.6, 5.9, 6.5, and 6.7) inland earthquakes. A deep-neural network-based phase picker, PhaseNet, was used to obtain the seismic phases of earthquake waveforms of stations within 200 km from the area of the events for 80 days from October 16 to December 31, 2019. The acquired seismic phases were initially associated and located using the Rapid Earthquake Association and Location (REAL). Subsequently, the initial hypocenter locations were adjusted through relocation utilizing VELEST, with further refinement achieved through a relative relocation technique hypoDD. By employing these methodologies, we successfully created an earthquake catalog that contains ~5,000 earthquakes for the corresponding period. The number of determined earthquakes through this method surpassed the ~3,000 event count reported in the original catalog by DOST-PHIVOLCS which depended solely on manually selected seismic phases. The spatial distribution of the relocated hypocenters reveals two seismic alignments: one trending in the SW-NE direction, parallel to the existing mapped active faults, and the other in the NW-SE direction. These lineaments intersect near the location of the Mw6.4 event, suggesting the presence of a conjugate fault or cross fault. The created earthquake catalog illuminates the spatial and temporal evolution of seismicity following each significant event, offering insights into the detailed patterns that characterize the clustering of aftershocks.