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

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 techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion 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 machine learning 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 eruptions.

Key Points

• Statistical features derived from time‐windowed, filter‐banked seismic data can be an effective way to characterize eruptive behavior of volcanoes

• Supervised learning allows us to determine the eruptive state of the volcano given a single time window of raw seismic data from a single station

• Spectral clustering can reveal different phases of eruptions and differences between various eruptions