Descripción del proyecto
Una tecnología de reconocimiento de terremotos mejorada
Durante años, los esfuerzos por predecir los terremotos se han visto frenados por la falta de una tecnología y un método científico fiables. Curiosamente, unos avances recientes mostraron que los terremotos creados en laboratorio pueden predecirse utilizando el aprendizaje automático. Los temblores van precedidos por una cascada de microfallos que radian energía elástica de una forma que predice el fallo catastrófico. Por lo tanto, el aprendizaje automático puede predecir el tiempo de fallo y, en algunos casos, la magnitud de los terremotos creados en laboratorio. El proyecto TECTONIC, financiado con fondos europeos, conectará estos resultados con observaciones de campo y el aprendizaje automático para buscar los precursores de los terremotos y construir modelos de predicción de fallas tectónicas. El equipo multidisciplinar del proyecto tiene como objetivo formar a la nueva generación de investigadores de los terremotos y fomentar un nuevo nivel de colaboración en su amplia comunidad.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Régimen de financiación
ERC-ADG - Advanced GrantInstitución de acogida
00185 Roma
Italia