Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives
| العنوان: | Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives |
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| المؤلفون: | Jean-Philippe Métaxian, Orlando Macedo, Jerome Mars, Adolfo Inza, Marielle Malfante, Mauro Dalla Mura |
| 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]), Géophysique des volcans & géothermie, 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])-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]), Instituto Geofisico del Peru (IPG), Instituto Geofísico del Perú (IGP), Universidad Nacional de San Agustín (UNSA), DGA - MRIS, ANR10 LABX56, ANR-10-LABX-0056,OSUG@2020,Innovative strategies for observing and modelling natural systems(2010) |
| المصدر: | IEEE Signal Processing Magazine IEEE Signal Processing Magazine, 2018, 35 (2), pp.20-30. ⟨10.1109/MSP.2017.2779166⟩ IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2018, 35 (2), pp.20-30. ⟨10.1109/MSP.2017.2779166⟩ |
| بيانات النشر: | HAL CCSD, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Volcanic hazards, Geospatial analysis, Support Vector Machine, 010504 meteorology & atmospheric sciences, Computer science, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Big data, 010502 geochemistry & geophysics, computer.software_genre, 01 natural sciences, Machine Learning, Natural hazard, Environmental monitoring, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Data Mining, Electrical and Electronic Engineering, 0105 earth and related environmental sciences, Random Forest, business.industry, Applied Mathematics, Scale (chemistry), Automatic Statistical Classification, Feature Space, Signal Representation, Data science, Volcano-seismic signals, 13. Climate action, Index Terms, Signal Processing, Task analysis, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, LEAPS |
| الوصف: | International audience; Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long term monitoring, manual inspection is no longer a viable option. As in many Big Data situations, classic Machine Learning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring at a larger scale. This paper focuses on integrated and operational tools dedicated to the automatic analysis of volcano-seismic signals. Namely we review (i) tools for the optimal representation of volcano-seismic signals (feature space) and the available methods for volcano-seismic events (ii) detection and (iii) classification. We then propose an architecture for the automatic classification of volcano-seismic events. Our prediction system is tested on 6 years of recordings containing 109434 volcano-seismic events acquired from Ubinas volcano (the most active volcano in PerúPer´Perú). Our new proposed model is build using supervised machine learning algorithms (Support Vector Machine) and reaches 92.2% of correct classification over six classes. This prediction model is then used to fully analyze the 6 years of recorded signals. |
| اللغة: | English |
| تدمد: | 1053-5888 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::433bd92e59fe469a19ef7279b1116ecd https://hal.science/hal-01742506/document |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....433bd92e59fe469a19ef7279b1116ecd |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10535888 |
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