  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
