Implementation of a multistation approach for automated event classification at Piton de la Fournaise volcano

Abstract

We implemented the first operational automated seismic-event classification system for monitoring activity at the Piton de la Fournaise volcano observatory (OVPF, La Réunion Island). Our classifier is based on the Random Forest algorithm. It distinguishes between eight classes of seismic signals: summit and deep volcano tectonic events, local, regional, and teleseismic earthquakes, T phases, rockfalls, and sound waves. It adopts a multistation approach and automatically selects the best features for each station and combination of stations from a large set of waveform- and spectrum-based features. It reaches peak performance when it runs on a three-station combination: one station on the summit of Piton de la Fournaise, one in its caldera, and one on the volcano flank. We interfaced our classification system with the observatory management interface WebObs used at OVPF.