{"id":3357,"date":"2024-11-20T10:11:03","date_gmt":"2024-11-20T10:11:03","guid":{"rendered":"http:\/\/hebergement.universite-paris-saclay.fr\/brigaud\/?p=3357"},"modified":"2026-01-18T21:17:53","modified_gmt":"2026-01-18T21:17:53","slug":"vers-une-analyse-de-roche-plus-efficace-lapport-de-lia-pour-des-applications-en-petrographie","status":"publish","type":"post","link":"https:\/\/hebergement.universite-paris-saclay.fr\/brigaud\/vers-une-analyse-de-roche-plus-efficace-lapport-de-lia-pour-des-applications-en-petrographie\/","title":{"rendered":"Vers une analyse de roche plus efficace : l’apport de l’IA pour des applications en p\u00e9trographie"},"content":{"rendered":"\n

Retrouvez nos derniers r\u00e9sultats sur l’automatisation de la reconnaissance p\u00e9trographique des roches:<\/p>\n\n\n\n

Azzam, F., Blaise, T., & Brigaud, B. (2024). Automated petrographic image analysis by supervised and unsupervised machine learning methods. Sedimentologika, 2(2). https:\/\/doi.org\/10.57035\/journals\/sdk.2024.e22.1594<\/a><\/p>\n\n\n\n

R\u00e9sum\u00e9: <\/p>\n\n\n\n

Les techniques d’apprentissage automatique ont \u00e9t\u00e9 explor\u00e9es dans le but d\u2019automatiser et rendre plus rapide les \u00e9tudes p\u00e9trographiques appliqu\u00e9es \u00e0 la s\u00e9dimentologie et diagen\u00e8se. Le travail s\u2019est concentr\u00e9 sur l\u2019utilisation d\u2019algoritmes de segmentation des grains afin d’en extraire leurs caract\u00e9ristiques. Deux nouveaux outils logiciels ont pu \u00eatre mis au point (1) GrainSight<\/strong>, qui utilise un mod\u00e8le d’apprentissage profond supervis\u00e9 (FastSAM) pour la d\u00e9tection automatique des grains et leur caract\u00e9risation morphologique et (2) PetroSeg<\/strong>, qui applique une approche de segmentation non supervis\u00e9e pour explorer les propri\u00e9t\u00e9s des roches et calculer par exemple leur porosit\u00e9.<\/p>\n\n\n\n

L’application GrainSight<\/strong> permet de caract\u00e9riser la morphologie des constituants de la roche comme la taille des grains (p\u00e9rim\u00e8tre, longueure, sph\u00e9ricit\u00e9) afin de rendre possible la d\u00e9termination automatique de la granulom\u00e9trie \u00e0 partir d\u2019une photographie de lame mince. Cette technique am\u00e9liore consid\u00e9rablement l’efficacit\u00e9 et la pr\u00e9cision d\u2019une \u00e9tude quantitative des caract\u00e9ristiques de la roche par rapport aux m\u00e9thodes manuelles. Le mod\u00e8le FastSAM permet une d\u00e9tection rapide et pr\u00e9cise des grains ainsi qu’une extraction des caract\u00e9ristiques morphologiques, offrant ainsi des informations pr\u00e9cieuses sur les environnements de d\u00e9p\u00f4t, les syst\u00e8mes de transport des s\u00e9diments et la qualit\u00e9 des r\u00e9servoirs.<\/p>\n\n\n\n

L\u2019application PetroSeg<\/strong> propose une approche exploratoire pour la quantification de la porosit\u00e9, l’identification des associations min\u00e9rales et la caract\u00e9risation des domaines texturaux.<\/p>\n\n\n\n

Ces deux m\u00e9thodes pr\u00e9sentent des avantages uniques et d\u00e9montrent le potentiel de l’apprentissage automatique dans l’analyse p\u00e9trographique. L’utilisation de ces outils pourrait consid\u00e9rablement am\u00e9liorer l’efficacit\u00e9, l’objectivit\u00e9 et le traitement des donn\u00e9es, ouvrant ainsi de nouvelles opportunit\u00e9s pour l’enseignement, la recherche et les applications dans divers domaines de la g\u00e9ologie.<\/p>\n\n\n\n

Dans un esprit de partage et d\u2019ouverture, les codes des deux applications, GrainSight et PetroSeg, sont accessibles en open source et disponibles sur recherche.data.gouv.fr.<\/p>\n\n\n\n

Grainsight : <\/strong>https:\/\/www.data.gouv.fr\/fr\/datasets\/grainsight\/<\/strong><\/a><\/strong><\/p>\n\n\n\n

Petroseg\u00a0: <\/strong>https:\/\/www.data.gouv.fr\/fr\/datasets\/petroseg\/<\/strong><\/a><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Retrouvez nos derniers r\u00e9sultats sur l’automatisation de la reconnaissance p\u00e9trographique des roches: Azzam, F., Blaise, T., & Brigaud, B. (2024). Automated petrographic image analysis by supervised and unsupervised machine learning methods. Sedimentologika, 2(2). https:\/\/doi.org\/10.57035\/journals\/sdk.2024.e22.1594 R\u00e9sum\u00e9: Les techniques d’apprentissage automatique ont \u00e9t\u00e9 explor\u00e9es dans le but d\u2019automatiser et rendre plus rapide les \u00e9tudes p\u00e9trographiques appliqu\u00e9es \u00e0 […]<\/p>\n","protected":false},"author":1,"featured_media":3358,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[75],"tags":[119,7],"class_list":["post-3357","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article","tag-petrographie","tag-sedimentologie","post-preview"],"yoast_head":"\nVers une analyse de roche plus efficace : l'apport de l'IA pour des applications en p\u00e9trographie - benjamin brigaud<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/hebergement.universite-paris-saclay.fr\/brigaud\/vers-une-analyse-de-roche-plus-efficace-lapport-de-lia-pour-des-applications-en-petrographie\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Vers une analyse de roche plus efficace : l'apport de l'IA pour des applications en p\u00e9trographie - benjamin brigaud\" \/>\n<meta property=\"og:description\" content=\"Retrouvez nos derniers r\u00e9sultats sur l’automatisation de la reconnaissance p\u00e9trographique des roches: Azzam, F., Blaise, T., & Brigaud, B. 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