Deep Learning Models for Seismic Continuous Data Analysis Developed in STAR-E Project

The STAR-E Project that aims to develop state-of-the-art information science techniques, including artificial intelligence, applicable in seismology is going on in Japan, supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). We introduce our various activities in the SYNTHA-Seis, which is one of the research teams in the STAR-E Project. We have been developing deep learning models to detect earthquake signals in seismic continuous waveforms, such as for phase-picking (Tokuda and Nagao, 2023; Katoh et al., 2025; Gerardo Mendo et al., submitted) and for P-wave polarity determination with UQ using the Monte Carlo dropout method (Katoh et al., 2025). We have also been developing methods to extract waveform features of low-frequency tremors (LFTs), such as a template matching technique to extract LFTs waveforms (Gerardo Mendo et al., 2025), a deep learning model to detect LFTs in historical paper records obtained by mechanical seismograms more than fifty years ago (Kaneko et al., 2023), and a deep learning technique to acquire a stochastic differential equation expression of LFTs (Kusui et al., 2025). We also discuss the future direction of AI seismology in Japan.