Vers une nouvelle génération de modèles de glissements co-sismiques : analyse stochastique et approche multi-données

Abstract : The explosion in the amount and variety of available geodetic, tsunami, and seismological observations offers an outstanding opportunity to develop new seismic source models. But these data are sensitive to different sources of uncertainty and provide heterogeneous information, which makes the solution of the inverse problem non-unique.In this thesis, we use a Bayesian sampling method to propose new slip models, which benefit from an objective weighting of the various datasets by combining observational and modelling errors. These models are less affected by data overfit and allow a realistic assessment of posterior uncertainties. We apply this method to the study of slip processes occurring in three different tectonic contexts: the Landers earthquake (1992, Mw=7.3), the Ecuador-Colombia subduction zone which hosted the Pedernales earthquake (2016, Mw=7.8), and the intraslab Tehuantepec earthquake (2017, Mw=8.2). Through these analyses, we demonstrate how the study of the seismic cycle can benefit from rigorous uncertainty estimates and Bayesian sampling.
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Baptiste Gombert. Vers une nouvelle génération de modèles de glissements co-sismiques : analyse stochastique et approche multi-données. Géophysique [physics.geo-ph]. Université de Strasbourg, 2018. Français. ⟨NNT : 2018STRAH016⟩. ⟨tel-02155222⟩

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