QuakeMLab Phase I: Deep Learning-Based Automated Seismic Phase Picking Using PhaseNet

Phase picking in seismology is the first step of signal processing and locating a seismic event. At the beginning of seismological research, it is straightforward to manually pick P- and S-wave arrival times because the number of seismic stations is relatively small. Recently, instrumentation has improved, and the gap is lower than before. This widespread instrumentation creates problems for users who still have to pick manually, especially in national networks such as those in Türkiye. We used pre-trained deep learning models, trained on different seismic datasets, to estimate P- and S-wave arrival times for earthquakes in the Marmara Region, NW Türkiye. We compared these times with manual readings collected from the Ministry of Interior, the Disaster and Emergency Management Presidency, and the Earthquake Department (AFAD). The results indicate that PhaseNet produces arrival-time estimates that are largely consistent with expert manual readings, demonstrating its potential to substantially reduce analyst workload in large-scale seismic monitoring systems. The mean absolute error ranges from 6 to 14 seconds, and the number of total picks varies between 75,000 and 140,000 for both the P-wave and S-wave. This project has been supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under grant 124E294, and the results have been shared on the website (https://quakemlab.sakarya.edu.tr)