Descrizione del progetto
Migliorata la tecnologia di riconoscimento delle scosse sismiche
Gli sforzi per prevedere i terremoti sono stati ostacolati per anni dalla mancanza di scienza e tecnologia affidabili. È interessante notare che i recenti progressi hanno dimostrato che i terremoti in laboratorio possono essere previsti con l’apprendimento automatico. Le scosse sono precedute da una cascata di eventi di micro-crolli che irradiano energia elastica in un modo che preannuncia un crollo catastrofico. L’apprendimento automatico può quindi prevedere il tempo del crollo e, in alcuni casi, l’entità dei terremoti in laboratorio. Il progetto TECTONIC, finanziato dall’UE, collegherà questi risultati con le osservazioni sul campo e l’apprendimento automatico alla ricerca di precursori dei terremoti e costruirà modelli predittivi per la fagliazione tettonica. Il gruppo multidisciplinare del progetto mira a formare la prossima generazione di ricercatori in scienze sismiche e a promuovere un nuovo livello di ampia collaborazione comunitaria.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-ADG - Advanced GrantIstituzione ospitante
00185 Roma
Italia