Summary
While most earthquakes occur near plate boundaries, where the relative motion of tectonic plates is accommodated by slip on active faults, rare and unexpected but destructive earthquakes also occur in Stable Continental Regions. In SCRs, although we do not know why, earthquakes occur everywhere, they cluster, are of all magnitudes and are modulated by surface loads. However, no tectonic, secular strain can be detected and we do not know the origin of the elastic stress fueling these seismic events. Current hypothesis suggests that SCRs are stable reservoirs of elastic stress in which earthquakes can tap to break the crust. However, although tremendous work has been conducted to seismically characterize these regions, existing catalogs are not dense enough to explore potential physical mechanisms.
I propose to test the following hypothesis: these rare albeit potentially destructive earthquakes are the result of the static fatigue of continents under stress left by previous phases of deformation in the geological history of a region. I will leverage the latest developments in artificial intelligence to grow the densest and largest global catalog of earthquakes in SCRs from seismological and InSAR data (WP1). I will develop a tool to predict realistic time series of surface loads affecting the crust (WP2). I will implement static fatigue in the form of brittle creep in a numerical model (WP3) to test whether continents are effectively failing today under paleo-stress left by fossil plate boundaries perturbed by todays' modulations of crustal stress, comparing model outcomes with data collected in WP1.
This interdisciplinary project combining seismology, geodesy, machine learning and numerical modeling will allow to (1) grow a physical understanding of the seismogenic behavior of SCRs, (2) tune estimates of seismogenic potential (and eventually hazard) for any given SCR (3) test whether a changing climate will affect the seismogenic potential of SCRs in the future.
I propose to test the following hypothesis: these rare albeit potentially destructive earthquakes are the result of the static fatigue of continents under stress left by previous phases of deformation in the geological history of a region. I will leverage the latest developments in artificial intelligence to grow the densest and largest global catalog of earthquakes in SCRs from seismological and InSAR data (WP1). I will develop a tool to predict realistic time series of surface loads affecting the crust (WP2). I will implement static fatigue in the form of brittle creep in a numerical model (WP3) to test whether continents are effectively failing today under paleo-stress left by fossil plate boundaries perturbed by todays' modulations of crustal stress, comparing model outcomes with data collected in WP1.
This interdisciplinary project combining seismology, geodesy, machine learning and numerical modeling will allow to (1) grow a physical understanding of the seismogenic behavior of SCRs, (2) tune estimates of seismogenic potential (and eventually hazard) for any given SCR (3) test whether a changing climate will affect the seismogenic potential of SCRs in the future.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101125232 |
| Start date: | 01-09-2024 |
| End date: | 31-08-2029 |
| Total budget - Public funding: | 1 999 434,00 Euro - 1 999 434,00 Euro |
Cordis data
Original description
While most earthquakes occur near plate boundaries, where the relative motion of tectonic plates is accommodated by slip on active faults, rare and unexpected but destructive earthquakes also occur in Stable Continental Regions. In SCRs, although we do not know why, earthquakes occur everywhere, they cluster, are of all magnitudes and are modulated by surface loads. However, no tectonic, secular strain can be detected and we do not know the origin of the elastic stress fueling these seismic events. Current hypothesis suggests that SCRs are stable reservoirs of elastic stress in which earthquakes can tap to break the crust. However, although tremendous work has been conducted to seismically characterize these regions, existing catalogs are not dense enough to explore potential physical mechanisms.I propose to test the following hypothesis: these rare albeit potentially destructive earthquakes are the result of the static fatigue of continents under stress left by previous phases of deformation in the geological history of a region. I will leverage the latest developments in artificial intelligence to grow the densest and largest global catalog of earthquakes in SCRs from seismological and InSAR data (WP1). I will develop a tool to predict realistic time series of surface loads affecting the crust (WP2). I will implement static fatigue in the form of brittle creep in a numerical model (WP3) to test whether continents are effectively failing today under paleo-stress left by fossil plate boundaries perturbed by todays' modulations of crustal stress, comparing model outcomes with data collected in WP1.
This interdisciplinary project combining seismology, geodesy, machine learning and numerical modeling will allow to (1) grow a physical understanding of the seismogenic behavior of SCRs, (2) tune estimates of seismogenic potential (and eventually hazard) for any given SCR (3) test whether a changing climate will affect the seismogenic potential of SCRs in the future.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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