Opendata, web and dolomites


Monitoring real faults towards their critical state

Total Cost €


EC-Contrib. €






 MONIFAULTS project word cloud

Explore the words cloud of the MONIFAULTS project. It provides you a very rough idea of what is the project "MONIFAULTS" about.

sequence    understand    covariance    matrix    risk    evolve    time    earthquake    geometry    edge    tides    italy    methodology    ignorance    rupture    reveal    spatiotemporal    nucleation    geophysical    approaching    emerges    last    discover    nucleate    excellent    faults    laboratory    critical    describing    fault    machine    urgent    evolution    posed    economic    poorly    detection    factorization    anomalous    posit    governing    earth    instrumentation    data    lab    elastic    responsible    techniques    earthquakes    incoming    physical    observations    direct    monitor    signals    perturbations    preceding    rocks    near    cutting    introduce    models    forecasting    fatalities    dependent    continuous    rheology    forecast    deformation    298    remind    seismic    monitoring    observational    physics    permit    derive    unknown    learning    deep    insights    seismological    natural    parallel    unravel    stress    geodetic    limited    exhaustive    periods   

Project "MONIFAULTS" data sheet

The following table provides information about the project.


There are not information about this coordinator. Please contact Fabio for more information, thanks.

 Coordinator Country France [FR]
 Total cost 1˙393˙174 €
 EC max contribution 1˙393˙174 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-STG
 Funding Scheme ERC-STG
 Starting year 2019
 Duration (year-month-day) from 2019-01-01   to  2023-12-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITE GRENOBLE ALPES FR (GRENOBLE) coordinator 1˙393˙174.00


 Project objective

The last seismic sequence in Italy, responsible for 298 fatalities and important economic loss, remind us how urgent it is to improve our knowledge about earthquake physics to advance earthquake forecasting. While direct observations during laboratory earthquakes permit us to derive exhaustive physical models describing the behaviour of rocks and to forecast incoming lab-earthquakes, the complex physics governing the nucleation of earthquakes remain poorly understood in real Earth, and so does our ability to forecast earthquakes. I posit that this ‘ignorance’ emerges from our limited ability to unravel information about fault physics from geophysical data.The objective of this proposal is to introduce a new and integrated methodology to monitor the spatiotemporal evolution of elastic properties on real faults using seismological and geodetic data. We will apply machine learning and covariance matrix factorization for improved earthquake detection, and to discover ‘anomalous’ seismological signals, which will reveal unknown physical processes on faults. These novel observations will be integrated with time dependent measurements of rheology and deformation, obtained from cutting-edge techniques applied to continuous seismological and geodetic data. Our integrated monitoring approach will be applied to study how faults respond to known stress perturbations (as Earth tides). In parallel, we will analyse periods preceding significant earthquakes to assess how elastic properties and deformation evolve while a fault is approaching a critical (near rupture) state. Our natural laboratory will be Italy, given its excellent geodetic and seismological instrumentation, deep knowledge about faults geometry and the relevant risk posed by earthquakes. Our research will provide new insights about the complex physics of faults at critical state, necessary to understand how real earthquakes nucleate. This project will also have a major impact on observational earthquake forecast.

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The information about "MONIFAULTS" are provided by the European Opendata Portal: CORDIS opendata.

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