Volcano-seismic Unsupervised Labelling and ClAssificatioN Embedded in A Real-time Scenario

Información del proyecto

VULCAN.ears

Identificador del acuerdo de subvención: 749249

DOI

10.3030/749249

Proyecto cerrado

Fecha de la firma de la CE 12 Abril 2017

Fecha de inicio 1 Noviembre 2017

Fecha de finalización 31 Octubre 2019

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 180 277,20

Aportación de la UE

€ 180 277,20

Coordinado por

UNIVERSITA DEGLI STUDI DI UDINE
Italy

Objetivo

Volcanic activity has a big impact on the economy and society. Nowadays, volcano monitoring (VM) is mainly based on the analysis of the seismicity, specifically on some type of precursory events (or classes) which appear before an eruption. The variability of the volcano-seismic classes and the increase of the seismicity in a volcano crisis difficult the manual supervised classification carried out by expert technicians to detect an event and assign it to its proper class. Most of the VM observatories demand an automatic Volcano Seismic Recognition (VSR) to quickly detect and analyse the precursory seismicity and to correctly assess the population risk, avoiding human casualties. Nevertheless, only a few VM facilities have their 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 seismicity analysis. It will be based on state-of-the-art VSR technologies: a) class description by statistical means (structured Hidden Markov Models) and b) Parallel System Architecture (PSA-VSR) composed of specialised recognition channels, each designed 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 searching the most efficient way to describe each class.
2. To maximise the system applicability: the system will be integrated into several VM scenarios and eruption forecasting tools to obtain useful feedback information.

The interaction between machine learning and volcanology will be the key to build this innovative, long-awaited, standard solution in the VM area: a collaborative framework software able to recognise events from any volcano in real-time.

Ámbito científico (EuroSciVoc)

• ciencias naturales › informática y ciencias de la información › software
• ciencias naturales › informática y ciencias de la información › base de datos
• ciencias naturales › ciencias de la tierra y ciencias ambientales conexas › geología › vulcanología
• ciencias sociales › sociología › gobernanza › gestión de crisis
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › aprendizaje automático

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2016 - Individual Fellowships
  

Régimen de financiación

MSCA-IF-EF-ST - Standard EF

Convocatoria de propuestas

H2020-MSCA-IF-2016

Coordinador

UNIVERSITA DEGLI STUDI DI UDINE

• Aportación neta de la UEn: € 180 277,20
• Dirección: VIA PALLADIO 8 ; 33100 Udine ; Italia
• Región: Nord-Est › Friuli-Venezia Giulia › Udine
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 180 277,20