SOURCE-LINKED INTELLIGENCE
HOw Predictable are Earthquakes
e earthquake cycles on faults with controlled heterogeneities and (iii) the statistical predictability of earthquakes, studied by forecasting the spatial distribution of experimental seismicity using machine learning. At the core of this project lies the development of a new dedicated experimental setup to generate multiple earthquake cycles along a fault with prescribed complex geometry and rheology. With this new capability, we will conduct a threefold experimental program to: (i) compute the complete energy budget of laboratory earthquakes, (ii) study the sensitivity of rupture nucleation, propagation and arrest to heterogeneities, and (iii) study the effect of heterogeneities on the relation between fault seismic coupling and seismicity. Our work will provide insights for earthquake hazard mitigation, constrain the physics underlying ubiquitously observed seismological statistical laws (Omori, Gutenberg-Richter) and test seismic slip inversion and dynamic rupture modelling techniques in unprecedented data sets on rock fracture dynamics in experiments that mimic field condition
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 2498856
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T00:21:03.701Z. This is not the publication date.