Unsupervised exploration of seismic activity at Mount Fuji, Japan

Mount Fuji volcano, located 100~km away from Tokyo, directly threatens over 30~million people. It last erupted in 1707, and has remained dormant since then. Seismicity --and particularly Low-Frequency Earthquakes (LFE)-- is to now the primary indicator of processes occurring beneath the volcano and is usually linked to fluid movement. Yet, these signals are usually manually picked and classified as such, without the ability to formally define them for automatic detection systems. 

Our goal is to develop an automatic method to detect and classify LFEs, among other seismic events at Mount Fuji using the continuous seismic records from 2008 at 11 stations. First, we use the CovSeisNet software to detect events by analyzing the wavefield coherence, derived from the network covariance matrix width. Over one year of continuous data, the wavefield coherence shows distinct patterns that correspond to various event types, including LFEs and tectonic earthquakes. To enable interpretation, we apply a manifold learning algorithm (UMAP) to reduce the dimension of the coherence patterns into two dimensions to ease the interpretation. We name this low-dimensional representation a "coherence atlas" where each point represents a time window of seismic data, grouped by similarity. This automatic approach enables not only the detection but also the classification of seismic events, as compared with the Japan Meteorological Agency catalog. Moreover, the atlas helps identify previously unrecorded events and facilitates the definition of new event classes. By autonomously mapping and classifying seismic activity beneath Mount Fuji, this method offers unprecedented insights into its activity and allows us to detect new events that had been hidden in the manually prepared catalog.