Description du projet
Une technologie améliorée de reconnaissance de séisme
Les efforts de prévision des tremblements de terre ont été entravés pendant des années par le manque de science et de technologie fiables. Curieusement, les progrès récents ont montré que l’apprentissage automatique (AA) permettait de prédire les séismes générés en laboratoire. Les tremblements sont précédés par une cascade d’événements de micro-défaillance qui émettent de l’énergie élastique d’une manière indiquant un effondrement catastrophique. L’AA peut ainsi prédire le temps d’effondrement et, dans certains cas, l’ampleur des tremblements de terre en laboratoire. Le projet TECTONIC, financé par l’UE, associera ces résultats à des observations de terrain et à l’AA pour rechercher des événements précurseurs de tremblement de terre et créer des modèles prédictifs des failles tectoniques. L’équipe pluridisciplinaire du projet a pour objectif de former la prochaine génération de chercheurs à la science des tremblements de terre et de favoriser un nouveau niveau de collaboration au sein de la communauté.
Objectif
Earthquakes represent one of our greatest natural hazards. Even a modest improvement in the ability to forecast devastating events like the 2016 sequence that destroyed the villages of Amatrice and Norcia, Italy would save thousands of lives and billions of euros. Current efforts to forecast earthquakes are hampered by a lack of reliable lab or field observations. Moreover, even when changes in rock properties prior to failure (precursors) have been found, we have not known enough about the physics to rationally extrapolate lab results to tectonic faults and account for tectonic history, local plate motion, hydrogeology, or the local P/T/chemical environment. However, recent advances show: 1) clear and consistent precursors prior to earthquake-like failure in the lab and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the failure time and in some cases the magnitude of lab earthquakes. Here, I propose to connect these results with field observations and use ML to search for earthquake precursors and build predictive models for tectonic faulting.
This proposal will support acquisition and analysis of seismic and geodetic data and construction of new lab equipment to unravel earthquake physics, precursors and forecasts. I will use my background in earthquake source theory, ML, fault rheology, and geodesy to address the physics of earthquake precursors, the conditions under which they can be observed for tectonic faults and the extent to which ML can forecast the spectrum of fault slip modes. My multidisciplinary team will train the next generation of researchers in earthquake science and foster a new level of broad community collaboration.
Champ scientifique
- humanitieshistory and archaeologyhistory
- natural sciencesearth and related environmental scienceshydrologyhydrogeology
- natural sciencesearth and related environmental sciencesgeologyseismology
- natural sciencesearth and related environmental sciencesphysical geographynatural disasters
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Mots‑clés
Programme(s)
Thème(s)
Régime de financement
ERC-ADG - Advanced GrantInstitution d’accueil
00185 Roma
Italie