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TECTONIC SIGNED

The physics of Earthquake faulting: learning from laboratory earthquake prediCTiON to Improve forecasts of the spectrum of tectoniC failure modes: TECTONIC

Total Cost €

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EC-Contrib. €

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Partnership

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Project "TECTONIC" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA 

Organization address
address: Piazzale Aldo Moro 5
city: ROMA
postcode: 185
website: www.uniroma1.it

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 3˙459˙750 €
 EC max contribution 3˙459˙750 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-ADG
 Funding Scheme ERC-ADG
 Starting year 2020
 Duration (year-month-day) from 2020-01-01   to  2024-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA IT (ROMA) coordinator 2˙603˙500.00
2    ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA IT (ROMA) participant 856˙250.00

Map

 Project 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.

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

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