@misc{10481/66966,
year = {2021},
url = {http://hdl.handle.net/10481/66966},
abstract = {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.},
organization = {European Union'sHorizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 

749249},
publisher = {FRONTIERS MEDIA SA},
keywords = {Volcano monitoring},
keywords = {Eruption forecasting},
keywords = {Machine learning},
keywords = {Data mining},
keywords = {VULCAN.ears},
keywords = {Volcano-seismic recognition},
keywords = {Volcano-independent VSR},
keywords = {Seismic recognition},
title = {Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project},
doi = {10.3389/feart.2020.616676},
author = {Cortés, Guillermo and Mendoza, María Ángeles},
}