{"id":19957,"date":"2025-10-28T14:14:08","date_gmt":"2025-10-28T13:14:08","guid":{"rendered":"https:\/\/navier-lab.fr\/?post_type=event&#038;p=19957"},"modified":"2025-10-28T14:14:45","modified_gmt":"2025-10-28T13:14:45","slug":"seminaire-geotech-julien-lalanne","status":"publish","type":"event","link":"https:\/\/navier-lab.fr\/en\/agenda\/seminaire-geotech-julien-lalanne\/","title":{"rendered":"Geotech Young Seminar Series: Julien Lalanne (Navier)"},"content":{"rendered":"<section class=\"kc-elm kc-css-721067 kc_row\"><div class=\"kc-row-container  kc-container\"><div class=\"kc-wrap-columns\"><div class=\"kc-elm kc-css-52671 kc_col-sm-12 kc_column kc_col-sm-12\"><div class=\"kc-col-container\">\n<div class=\"kc-elm kc-css-618186 kc-title-wrap \">\n\n\t<h1 class=\"kc_title\">Generative Modeling of Spatial Processes for 3D Geophysical Data Interpolation in Offshore Windfarm Site Characterization<\/h1>\n<\/div>\n<div class=\"kc-elm kc-css-606093 kc_text_block\"><\/p>\n<h2><strong>Abstract:<\/strong><\/h2>\n<p style=\"text-align: justify;\">In offshore windfarm development, geophysical and geotechnical surveys are essential for assessing subsurface conditions. However, these datasets are typically sparse and irregularly distributed, posing challenges for accurate 3D predictions. Recent advances in generative modeling offer promising solutions by learning complex spatial distributions directly from data.<\/p>\n<p style=\"text-align: justify;\">We present a framework based on flow matching to learn spatial random fields from partial measurements and demonstrate its effectiveness on seismic data. This approach enables realistic interpolation while providing quantifiable uncertainty estimates. Our results demonstrate the method\u2019s effectiveness in capturing spatial variability and improving predictive accuracy.<\/p>\n<p>\n<\/div><div class=\"kc-elm kc-css-857064 kc_text_block\"><\/p>\n<h2><strong>Short bio:<\/strong><\/h2>\n<p style=\"text-align: justify;\">I am a 1st year PhD student at Laboratoire Navier &#8211; \u00c9cole Nationale des Ponts et Chauss\u00e9es and TotalEnergies, working at the intersection of generative modeling and geosciences. My research focuses on stochastic interpolation techniques for subsurface data, with applications in soil characterization for offshore wind farms. Prior to my PhD, I worked as a research engineer at TotalEnergies, where I developed machine learning methods for seismic and well log data analysis and defined my PhD project. I hold a Master in Applied Mathematics from Universit\u00e9 Grenoble Alpes and an engineering degree from Grenoble INP \u2013 Ensimag, with a focus in machine learning, statistical modeling, and optimization. My academic journey also includes a research internship at the National Institute of Informatics in Tokyo, where I explored multimodal context analysis for live video comment generation.<\/p>\n<p>\n<\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"<p>Generative Modeling of Spatial Processes for 3D Geophysical Data Interpolation in Offshore Windfarm Site Characterization<\/p>\n","protected":false},"author":158,"featured_media":0,"template":"","class_list":["post-19957","event","type-event","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/navier-lab.fr\/en\/wp-json\/wp\/v2\/event\/19957","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/navier-lab.fr\/en\/wp-json\/wp\/v2\/event"}],"about":[{"href":"https:\/\/navier-lab.fr\/en\/wp-json\/wp\/v2\/types\/event"}],"author":[{"embeddable":true,"href":"https:\/\/navier-lab.fr\/en\/wp-json\/wp\/v2\/users\/158"}],"wp:attachment":[{"href":"https:\/\/navier-lab.fr\/en\/wp-json\/wp\/v2\/media?parent=19957"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}