PhaseNeXt: neural phase picker trained on 20-year records to process the JMA-unified data set

Abstract To develop a higher-quality seismic event catalog from Japan’s routine seismic observations, which have been continuously recorded at approximately 2000 stations, we trained deep-learning-based phase pickers using 20 years of arrival-time data read by the Japan Meteorological Agency (JMA). To enhance performance, we developed a new model, PhaseNeXt, by incorporating techniques proven effective in the field of computer vision—particularly in semantic segmentation—into PhaseNet, one of the most widely used neural phase pickers. The resulting model adopts an architecture inspired by DeepLab v3+, connecting parameter-efficient ConvNeXt blocks through residual connections, which mitigate vanishing gradients and allow scalable adaptation to larger training data sets. Furthermore, by including automatically read or briefly reviewed arrival-time readings that had not undergone detailed manual inspection in the training process, we demonstrated improved performance for small earthquakes. Using these insights, we trained three deep neural network models with different parameter sizes on 25 million waveforms associated with events listed in the 2002–2023 JMA unified catalog. When integrated into the current JMA workflow, the best-performing model detected approximately 3.5 times more events than those listed in the JMA catalog while using nearly the same number of arrival-time readings.