H2020 - VULCAN.ears

Volcanic activity has a big impact on the economy and society. Nowadays, volcano monitoring (VM) is mainly based on theanalysis of the seismicity, specifically on some type of precursory events (or classes) which appear before an eruption. Thevariability of the volcano-seismic classes and the increase of the seismicity in a volcano crisis difficult the manual supervisedclassification carried out by expert technicians to detect an event and assign it to its proper class. Most of the VMobservatories demand an automatic Volcano Seismic Recognition (VSR) to quickly detect and analyse the precursoryseismicity and to correctly assess the population risk, avoiding human casualties. Nevertheless, only a few VM facilities havetheir own VSR prototypes designed to monitor their volcanoes.The aim of this proposal is to build an automatic VSR system focused on recognising events in unsupervised scenarios,robust enough to be integrated into the VM centre of any volcano, allowing online risk assessment by real-time seismicityanalysis. It will be based on state-of-the-art VSR technologies: a) class description by statistical means (structured HiddenMarkov Models) and b) Parallel System Architecture (PSA-VSR) composed of specialised recognition channels, eachdesigned to detect and classify events of a given type. To accomplish this goal, two objectives have to be achieved:1. To build models robust enough, which requires gathering massive data from different types of volcanoes and searchingthe most efficient way to describe each class.2. To maximise the system applicability: the system will be integrated into several VM scenarios and eruption forecastingtools to obtain useful feedback information.The interaction between machine learning and volcanology will be the key to build this innovative, long-awaited, standardsolution in the VM area: a collaborative framework software able to recognise events from any volcano in real-time.