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<dc:title xml:lang="fr">Vers une nouvelle génération de modèles de glissements co-sismiques : analyse stochastique et approche multi-données</dc:title>
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<dc:subject xml:lang="fr">Séismes</dc:subject>
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<dc:subject xml:lang="fr">Modèle de glissement</dc:subject>
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<dc:subject xml:lang="en">Slip model</dc:subject>
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<dcterms:abstract xml:lang="fr">L’explosion du nombre et de la variété des données géodésiques, sismologiques et tsunami disponibles est une opportunité exceptionnelle pour produire de nouveaux modèles de la source sismique. Mais ces données n’apportent pas toutes la même information et sont soumises à différentes sources d’incertitudes, rendant la solution au problème inverse non-unique. Dans cette thèse, nous utilisons une méthode d’échantillonnage bayésien pour produire de nouveaux modèles de glissement moins assujettis au sur-ajustement des données et permettant une estimation réaliste de l’incertitude associée aux paramètres estimés. Nous l’appliquons à l’étude du glissement dans trois contextes tectoniques différents : le séisme de Landers (1992, Mw=7.3), la zone de subduction équato-colombienne où s’est produit le séisme de Pedernales (2016, Mw=7.8), et le séisme intra-plaque de Tehuantepec (2017, Mw=8.2). À travers ce travail, nous démontrons l’importance de la considération rigoureuse des incertitudes et les atouts de l’approche bayésienne pour l’étude des différentes phases du cycle sismique.</dcterms:abstract>
<dcterms:abstract xml:lang="en">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.</dcterms:abstract>
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