PhD opportunities

Physics informed statistical modeling of earthquakes

Thesis proposal

Area of expertiseGéosciences et géoingénierie
Doctoral SchoolGéosciences, Ressources Naturelles et Environnement
SupervisorDUBLANCHET Pierre
Research unitCentre de Géosciences
KeywordsEarthquake physics, ETAS, Point processes, rate-and-state friction, asperity model, Bayesian inference
AbstractSeismic activity (the occurrence and magnitude of earthquakes) is generally modeled either with stochastic or deterministic (based on physics) approaches. Although physics-based models allow realistic earthquake nucleation and interaction, their computational cost and the number of unknown parameters prevent any practical use for earthquake hazard assessment. Stochastic models usually rely on a limited number of parameters, which allows tractable parameter inference and uncertainty quantification. Although congenial, stochastic models miss relevant features of earthquake triggering, resulting in unrealistic predictions.
This project aims at bridging the gap between statistical and mechanical approaches, by proposing new point process models for earthquake modeling, driven by physics-based models explaining the nucleation and interaction of earthquakes on a planar fault (asperity model). A refined formulation of the widely used ETAS model will be developed: the parameters of this model will be directly inherited from the asperity model (friction heterogeneity, stress state, loading rate), and their inference will be tackled using efficient Bayesian algorithms. The models developed will be tested against natural earthquake sequences, observed in several active regions worldwide (California, Italy, Corinth rift…), and also on earthquake swarms induced by the exploitation of georessources.
ProfileA solid background in applied probabilities is required. Interest for physical modeling and computational methods. Skills in solid mechanics, would be appreciated
FundingConcours pour un contrat doctoral