Project description DEENESFRITPL Improved quake recognition technology Efforts to forecast earthquakes have been hampered for years by the lack of reliable science and technology. Intriguingly, recent advances showed that lab-engineered earthquakes can be predicted using machine learning (ML). The tremors are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. ML can thus predict the failure time, and in some cases, the magnitude of lab earthquakes. The EU-funded TECTONIC project will connect these results with field observations and ML to search for earthquake precursors and build predictive models for tectonic faulting. The project's multidisciplinary team aims to train the next generation of researchers in earthquake science and foster a new level of broad community collaboration. Show the project objective Hide the project objective Objective 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. Fields of science humanitieshistory and archaeologyhistorynatural sciencesearth and related environmental scienceshydrologyhydrogeologynatural sciencesearth and related environmental sciencesgeologyseismologynatural sciencesearth and related environmental sciencesphysical geographynatural disastersnatural sciencescomputer and information sciencesartificial intelligencemachine learning Keywords Earthquakes Machine Learning Prediction Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2018-ADG - ERC Advanced Grant Call for proposal ERC-2018-ADG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Host institution UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA Net EU contribution € 2 603 500,00 Address Piazzale Aldo Moro 5 00185 Roma Italy See on map Region Centro (IT) Lazio Roma Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 603 500,00 Beneficiaries (2) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA Italy Net EU contribution € 2 603 500,00 Address Piazzale Aldo Moro 5 00185 Roma See on map Region Centro (IT) Lazio Roma Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 603 500,00 ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA Italy Net EU contribution € 856 250,00 Address Via di Vigna Murata 605 00143 Roma See on map Region Centro (IT) Lazio Roma Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 856 250,00
