Automatic Classification of Volcano Seismic Signatures
| العنوان: | Automatic Classification of Volcano Seismic Signatures |
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| المؤلفون: | Jean-Philippe Métaxian, Adolfo Inza, Jerome Mars, Mauro Dalla Mura, Marielle Malfante, Orlando Macedo |
| المساهمون: | GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Faculdad de Geologia, Geofisica y Minas, Universidad Nacional de San Agustin de Arequipa, Universidad Nacional de San Agustín (UNSA), Insituto Geofisico del Peru (IPG), OSUG2020-ANR10 LABX56, ANR-15-IDEX-02, DGA-MRIS, Instituto Geofísico del Perú (IGP) |
| المصدر: | Journal of Geophysical Research Journal of Geophysical Research, American Geophysical Union, 2018, 123 (12), pp.10,645-10,658. ⟨10.1029/2018jb015470⟩ Journal of Geophysical Research, 2018, 123 (12), pp.10,645-10,658. ⟨10.1029/2018jb015470⟩ |
| بيانات النشر: | American Geophysical Union (AGU), 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 010504 meteorology & atmospheric sciences, Feature vector, automatic classification, 010502 geochemistry & geophysics, 01 natural sciences, Geochemistry and Petrology, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Earth and Planetary Sciences (miscellaneous), Extensive data, Preprocessor, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, volcano monitoring, Ubinas Volcano, volcano seismic signal, Random forest, Support vector machine, Tectonics, machine learning, Geophysics, Volcano, volcanic hazards, 13. Climate action, Space and Planetary Science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Classifier (UML), Seismology, Geology |
| الوصف: | International audience; The prediction of volcanic eruptions and the evaluation of associated risks remain a timely and unresolved issue. This paper presents a method to automatically classify seismic events linked to volcanic activity. As increased seismic activity is an indicator of volcanic unrest, automatic classification of volcano seismic events is of major interest for volcano monitoring. The proposed architecture is based on supervised classification, whereby a prediction model is built from an extensive data set of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals preprocessing, (ii) representation of the signals in the feature space, and (iii) use of an automatic classifier to train the model. Our main contribution lies in the feature space where the seismic observations are represented by 102 features gathered from both acoustic and seismic fields. Ideally, observations are separable in the feature space, depending on their class. The architecture is tested on 109,609 seismic events that were recorded between June 2006 and September 2011 at Ubinas Volcano, Peru. Six main classes of signals are considered: long-period events, volcanic tremors, volcano tectonic events, explosions, hybrid events, and tornillos. Our model reaches 93.5% ± 0.50% accuracy, thereby validating the presented architecture and the features used. Furthermore, we illustrate the limited influence of the learning algorithm used (i.e., random forest and support vector machines) by showing that the results remain accurate regardless of the algorithm selected for the training stage. The model is then used to analyze 6 years of data. |
| تدمد: | 2169-9313 0148-0227 2156-2202 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1713d8a9adce0df2e2c2082fce963469 https://doi.org/10.1029/2018jb015470 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....1713d8a9adce0df2e2c2082fce963469 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21699313 01480227 21562202 |
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