Published July 27, 2023
| Version 1.0
Dataset
Open
Self-supervised learning of seismological data reveals new eruptive sequences at the Mayotte submarine volcano - Supplementary Materials
Creators
- 1. Institut Terre et Environnement de Strasbourg (ITES), CNRS UMR 7063 - Université de Strasbourg, 5 rue Descartes, F-67084 Strasbourg, France
- 2. Institut de Physique du Globe de Paris, CNRS - Université Paris Cité, 1 rue Jussieu, F-75005 Paris, France
- 3. Institut de Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS), UR 7499 - University of Haute-Alsace F-68100 Mulhouse, France
- 4. NORSAR, Gunnar Randers Vei 15, 2007 Kjeller, Norway
- 5. IFREMER, Centre de Bretagne – Unité Géosciences Marines, 1625 Route de Sainte Anne, 29280 Plouzané, France
Description
The following files are shared:
- The scripts used to train the model and generate the figures of the article
- The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix)
- The clusters organization with their associated images
Notes
- Additional information on affiliations:
- Hibert, Clément: Ecole et Observatoire des Sciences de la Terre (EOST), CNRS UAR 830 – Université de Strasbourg, 5 rue Descartes, F-67084 Strasbourg, France
- Retailleau, Lise: Observatoire volcanologique du Piton de la Fournaise, Institut de Physique du Globe de Paris - 14 RN3 - Km 27, F-97418 La Plaine des Cafres, La Réunion, France
- Malet, Jean-Philippe : Ecole et Observatoire des Sciences de la Terre (EOST), CNRS UAR 830 – Université de Strasbourg, 5 rue Descartes, F-67084 Strasbourg, France
- Additional information on grant : REVOSIMA
Files
ClusterGroups.zip
Files
(30.8 GB)
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md5:5cbd7c3655d88a64f5c7f45e71f49a72
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md5:7c19107424ffe0c4e0d3ae93621172d3
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md5:cae559db60c8fee054159dc37c5f64f1
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md5:e20e2b0da354064d096bb30d0b05d1ab
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md5:9a41a4bce8758511536d623daedce630
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md5:026b86c54dfe848d5f2299f6ddcb4937
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81.6 MB | Preview Download |
Additional details
Funding
- Agence Nationale de la Recherche
- HighLand - Landslides and Climate Change in highly Sensible Environments: Seismology, Earth Observation and Artificial Intelligence ANR-20-CE01-0006