Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD
| العنوان: | Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD |
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| المؤلفون: | Jerome Mars, Pablo Eduardo Espinoza Lara, Carlos Alexandre Rolim Fernandes, Marielle Malfante, Jean-Philippe Metaxian, Mauro Dalla Mura, Adolfo Inza |
| المساهمون: | Universidade Federal do Ceará = Federal University of Ceará (UFC), Instituto Geofísico del Perú (IGP), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Institut de Recherche pour le Développement (IRD), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) |
| المصدر: | IGP-Institucional Instituto Geofísico del Perú instacron:IGP IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 1322-1331 (2020) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2020, 13, pp.1322-1331. ⟨10.1109/JSTARS.2020.2982714⟩ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, pp.1322-1331. ⟨10.1109/JSTARS.2020.2982714⟩ |
| بيانات النشر: | Institute of Electrical and Electronics Engineers, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Atmospheric Science, Artificial intelligence, Computer science, Geophysics. Cosmic physics, Cepstral analysis, 02 engineering and technology, Deconvolution, 010502 geochemistry & geophysics, computer.software_genre, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Cepstrum, 0202 electrical engineering, electronic engineering, information engineering, Preprocessor, Empirical mode decomposition, time domain analysis, purl.org/pe-repo/ocde/ford#1.05.07 [http], Ocean engineering, Principal component analysis, Volcanoes, Curse of dimensionality, Communication channel, Feature vector, Time domain analysis, Explosions, deconvolution, Machine learning, spectral domain analysis, Hilbert–Huang transform, Seismic signal processing, purl.org/pe-repo/ocde/ford#1.05.00 [http], Databases, Computers in Earth Sciences, empirical mode decomposition, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, TC1501-1800, 0105 earth and related environmental sciences, Support vector machines, Sensors, business.industry, QC801-809, 020206 networking & telecommunications, cepstral analysis, Support vector machine, Spectral domain analysis, business, computer |
| الوصف: | International audience; This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral and cepstral domains, extracted from the EMD of the signals, as well as a set of pre-processing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis (PCA) method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine (SVM) providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1939-1404 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f24bd684b70fceca7704b5f0d94cd76a |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f24bd684b70fceca7704b5f0d94cd76a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19391404 |
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