Neural Phase Picker Trained on the Japan Meteorological Agency Unified Earthquake Catalog

Abstract

In Japan, one of the most seismically active nations, seismic data from various institutions are shared in real-time and made accessible via the web, which facilitates research by numerous scholars. The Japan Meteorological Agency (JMA), in collaboration with the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), processes these data to compile a “unified earthquake catalog.” This catalog is crucial for developing disaster prevention strategies and significantly enhances societal safety. To facilitate the efficient development of high-quality seismic catalogs from this data, we retrained a deep-learning phase picker, known as a neural phase picker, which has gained prominence in recent years. This retraining was based on manual arrival time measurements provided by the JMA. We utilized the PhaseNet architecture for our model and trained it using 6.1 million three-component seismic waveforms collected between 2014 and 2021. When the original PhaseNet model, which was trained with data from California, was applied to routine Japanese data, its performance was suboptimal, especially with ocean-bottom seismometer records. However, retraining the model with the JMA-unified catalogs and corresponding waveforms significantly enhanced its performance in reading arrival times of natural and low-frequency earthquakes. The retrained model reduced its dependency on the types of stations that were monitored when applying the original PhaseNet and displayed improved performance for waveforms even from stations not included in the training dataset. The performance of the model varied with earthquake magnitude, which highlights the reliance on extensive data on small events in the training set. When integrated into the automatic processing used in the routine operation of the JMA, the model identified a larger number of events, especially smaller ones with undetermined magnitudes, compared to the events recorded by the conventional procedure. Furthermore, leveraging approximately ten times more training data than the California study, we developed and trained PhaseNetWC, which is a model with greater expressiveness than the original PhaseNet. This new model surpassed the performance of its predecessor. The publication and dissemination of these models are anticipated to enhance the analysis of routine observational datasets in Japan.