Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project
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FRONTIERS MEDIA SA
Volcano monitoringEruption forecastingMachine learningData miningVULCAN.earsVolcano-seismic recognitionVolcano-independent VSRSeismic recognition
Cortés G, Carniel R, Lesage P, Mendoza MÁ and Della Lucia I (2021) Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project. Front. Earth Sci. 8:616676. doi: 10.3389/feart.2020.616676
PatrocinadorEuropean Union'sHorizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 749249
Recognizing the mechanisms underlying seismic activity and tracking temporal and spatial patterns of earthquakes represent primary inputs to monitor active volcanoes and forecast eruptions. To quantify this seismicity, catalogs are established to summarize the history of the observed types and number of volcano-seismic events. In volcano observatories the detection and posterior classification or labeling of the events is manually performed by technicians, often suffering a lack of unified criteria and eventually resulting in poorly reliable labeled databases. State-of-the-art automatic Volcano-Seismic Recognition (VSR) systems allow real-time monitoring and consistent catalogs. VSR systems are generally designed to monitor one station of one volcano, decreasing their efficiency when used to recognize events from another station, in a different eruptive scenario or at different volcanoes. We propose a Volcano-Independent VSR (VI.VSR) solution for creating an exportable VSR system, whose aim is to generate labeled catalogs for observatories which do not have the resources for deploying their own systems. VI.VSR trains universal recognition models with data of several volcanoes to obtain portable and robust characteristics. We have designed the VULCAN.ears ecosystem to facilitate the VI.VSR application in observatories, including the pyVERSO tool to perform VSR tasks in an intuitive way, its graphical interface, geoStudio, and liveVSR for real-time monitoring. Case studies are presented at Deception, Colima, Popocatépetl and Arenal volcanoes testing VI.VSR models in challenging scenarios, obtaining encouraging recognition results in the 70–80% accuracy range. VI.VSR technology represents a major breakthrough to monitor volcanoes with minimal effort, providing reliable seismic catalogs to characterise real-time changes.