Abstract

Volcanic earthquakes provide essential information for evaluating volcanic activity. As volcanic earthquakes are often characterized by swarm-like features, conventional methods using manual picking require much time in constructing seismic catalogs. In this study, using a machine learning framework and a trained model from a volcanic earthquake catalog, we obtained a detailed picture of volcanic earthquakes during the past 12 years at Kirishima volcano, southwestern Japan. We could detect earthquakes about 7.5 times larger than those in a conventional seismic catalog and obtain a high-resolution hypocenter distribution through waveform correlation analysis. Hypocenter clusters were estimated below the craters where magmatic or phreatic eruptions occurred in recent years. Increases in seismic activities, b-values, and low-frequency earthquakes were detected before the eruptions. The process can be carried out in real time, and monitoring volcanic earthquakes through machine learning contributes to understanding the changes in volcanic activity and improving eruption predictions.