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In this study we leverage machine learning techniques using 6\u00a0years of continuous seismic data from the Piton de la Fournaise volcano (La R\u00e9union 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\u2013October 2015 eruption as well as the closing of the eruptive vent during the September\u2013November 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.<\/jats:p>","DOI":"10.1029\/2019gl085523","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T21:14:19Z","timestamp":1579641259000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano"],"prefix":"10.1029","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0787-6713","authenticated-orcid":false,"given":"C. 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