Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

TL;DR

This study applies machine learning to six years of seismic data from Piton de la Fournaise to identify seismic patterns linked to eruptive behaviors, improving understanding of volcanic activity.

Contribution

It introduces a machine learning workflow that detects specific seismic signatures associated with different eruptive phases, adaptable to other volcanoes.

Findings

Identified seismic signals associated with large lava effusions.

Detected signals indicating vent closure during eruptions.

Demonstrated ML's potential for real-time volcanic monitoring.

Abstract

Volcanic tremor is key to our understanding of active magmatic systems but, due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning (ML) techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La R\'eunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August-October 2015 eruption, as well as the closing of the eruptive vent during the September-November 2018 eruption. The ML workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptiions