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. R?sum?,

, Une classification standard des sources sismiques endog?nes est ainsi propos?e; l'objectif de cette norme est de pouvoir comparer l'activit? sismique de plusieurs glissements de terrain et d'identifier les m?canismes g?n?rant ces signaux sismiques ainsi que leur corr?lation avec les for?ages externes. Plusieurs propri?t?s de signal (dur?e, contenu spectral et forme de spectrogramme) sont prises en compte pour d?crire les diff?rentes classes de signaux et permettre une comparaison g?n?rique. Les observations montrent que des signaux similaires enregistr?s sur diff?rents sites pr?sentent les m?mes propri?t?s et les sources sismiques possibles sont discut?es compte tenu du type de d?formation observ? sur les pentes ?tudi?es. Ces propri?t?s de signal sont utilis?es comme caract?ristiques pour classer les sources endog?nes ? l'aide d'algorithmes d'apprentissage automatique. En effet, la classification manuelle des enregistrements sismologiques longs prend du temps et peut ?tre tr?s subjective. Une m?thode de classification automatique bas?e sur le calcul de 71 attributs sismiques et l'utilisation d'un classificateur supervis? est ainsi propos?e. Aucun attribut n'a ?t? s?lectionn? ? priori afin de cr?er une m?thode de classification g?n?rique multi-classes applicable ? de nombreux contextes de glissements de terrain. La m?thode peut ?tre appliqu?e directement sur les r?sultats d'un d?tecteur simple. L'algorithme automatique r?cup?re pr?s de 90\% de sensibilit? par rapport ? un catalogue interpr?t? manuellement consid?r? comme r?f?rence. Une nouvelle m?thode (APOLoc) pour automatiser la localisation des sources sismiques est ensuite propos?e. La localisation de la source est un probl?me difficile sur les glissements de terrain ? cause des limitations impos?es soit par la d?tection des erreurs sur lÕarriv?e des ondes sismiques, Ce projet de recherche vise ? accro"tre les connaissances sur les m?canismes contr?lant la d?formation des glissements de terrain argileux gr?ce ? la combinaison de la surveillance sismique passive et de la surveillance g?od?sique. Des ?tudes r?centes ont d?montr? que la surveillance sismique peut fournir des informations int?ressantes sur la m?canique des glissements de terrain et, dans certains cas, fournir des pr?curseurs utiles pour la pr?vision des d?faillances

, Kurtosis et une premi?re estimation de la localisation est calcul?e en analysant l'amplitude du signal; la s?lection et la localisation sont ensuite am?lior?es de mani?re it?rative jusqu'? ce que la corr?lation des premi?res arriv?es soit maximis?e. Enfin, des catalogues avanc?s de sources sismiques sont construits pour des p?riodes d'observation ? long terme et ? court terme sur des glissements de terrain s?lectionn?s, L'approche et les m?thodes sont d?velopp?es et test?es sur les bases de donn?es sismologiques et g?od?siques acquises lors du glissement de Super-Sauze

. Mots-cl?s, Glissement de terrain, micro-sismicit?/?coute sismique, tomographie sismique, machine learning