[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ background_color=”#072c72″ border_color_all=”#3255c9″ text_orientation=”right” background_layout=”dark” custom_padding=”20px|15px|15px|” z_index_tablet=”500″]<\/p>\n
2022<\/em><\/p>\n PhD\/ DIAI Projects<\/em><\/p>\n @<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”3.22.1″ text_orientation=”right” z_index_tablet=”500″]<\/p>\n Matthieu Nougaret <\/p>\n Charles Le Losq<\/strong> <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n Project Summary<\/strong><\/p>\n Monitoring volcanic edifices is central for mitigating volcanic risks and hazards. This involves monitoring and analyzing multivariate data, which often have complex characteristics. But observatories need to communicate the state of the volcano in clear, understandable terms for both the public and decision-makers. The challenge of analyzing large amounts of data could be improved through the use of machine learning. Machine learning may be a key method for conducting multivariate analyses of time series at volcanic observatories. Its application could provide new insights, enhance understanding, and help better anticipate We investigate whether and how signals from seismicity, ground deformation, and CO2 degassing can be combined to detect and forecast volcanic eruptions at Piton de la Fournaise. We analyze signals from the past twenty-four years using supervised and unsupervised deep learning techniques. Our results demonstrate that using various machine learning algorithms shows significant potential for detecting eruptive precursors and could enable the detection of eruptions several days in advance<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ admin_label=”Row”][et_pb_column type=”4_4″ _builder_version=”3.22.1″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”63″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″ locked=”off”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 2022 PhD\/ DIAI Projects @Matthieu NougaretPhD student at Institut de Physique du Globe de Paris.\u00a0Doctoral school 560.diiP, IdEx Universit\u00e9 Paris Cit\u00e9, ANR-18-IDEX-0001. Charles Le LosqUniversit\u00e9 de Paris, Institut de physique du globe de Paris, CNRSLise RetailleauInstitut de Physique du Globe de ParisAline PeltierUniversit\u00e9 de Paris, Institut de Physique du Globe de Paris (IPGP), UMR… Lire la suite Leveraging multivariate geophysical and geochemical time series for monitoring volcanic systems: can we use machine learning?<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[55,63,1],"tags":[],"class_list":["post-1998","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-55","category-diai","category-diip"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1998","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=1998"}],"version-history":[{"count":13,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1998\/revisions"}],"predecessor-version":[{"id":2435,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1998\/revisions\/2435"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2263"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1998"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1998"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1998"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/strong>PhD student at Institut de Physique du Globe de Paris.\u00a0Doctoral school 560.
diiP, IdEx Universit\u00e9 Paris Cit\u00e9, ANR-18-IDEX-0001.<\/p>\n
Universit\u00e9 de Paris, Institut de physique du globe de Paris, CNRS
Lise Retailleau<\/strong>
Institut de Physique du Globe de Paris
Aline Peltier<\/strong>
Universit\u00e9 de Paris, Institut de Physique du Globe de Paris (IPGP), UMR 7154<\/p>\n
eruptions.<\/p>\n\u00a0<\/h6>\n
Other projects
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