Browsing by Author "Mars, Jérôme I."

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    Automatic classification of volcano seismic signatures
    (American Geophysical Union, 2018-12) Malfante, Marielle; Dalla Mura, Mauro; Mars, Jérôme I.; Métaxian, Jean-Philippe; Macedo Sánchez, Orlando Efraín; Inza Callupe, Lamberto Adolfo
    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 dataset of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals pre‐processing ; (ii) represention of the signals in the feature space; (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, 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.
    Palabras clave:Volcano seismic signalAutomatic classificationMachine learning
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    Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD
    (Institute of Electrical and Electronics Engineers, 2020-03-27) Espinoza Lara, Pablo Eduardo; Rolim Fernandes, Carlos Alexandre; Inza Callupe, Lamberto Adolfo; Mars, Jérôme I.; Métaxian, Jean-Philippe; Dalla Mura, Mauro; Malfante, Marielle
    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 preprocessing 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 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 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%.
    Palabras clave:Artificial intelligenceEmpirical mode decompositionDeconvolution
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    Compréhension des processus magmatiques et localisation de source sismo-volcanique avec des antennes sismiques multicomposantes
    (Université de Grenoble, 2013-05-30) Inza Callupe, Lamberto Adolfo; Mars, Jérôme I.; Métaxian, Jean-Philippe; Bean, Christopher
    Cette thèse, à la fois méthodologique et appliquée, a été financée par le programme de bourse IRD (l’Institut de Recherche pour le Développement). Elle concerne la localisation des sources liées à l’activité sismique volcanique. Cette tâche est cruciale pour mieux comprendre le mécanisme d’éruption volcanique. L’apparition émergente des capteurs sismiques à trois composantes (3C) permet d’utiliser des approches moderne de localisation, en particulier pour des séismes longue période liés à l’activité magmatique proche de la surface du volcan. Cette thèse se situe donc dans le contexte de la localisation de sources sismo-volcaniques enregistrées par de réseaux de capteurs. Une attention particulière est portée sur la surveillance de l’activité magmatique qui peut être détecté par des mesures sismiques autour du volcan. Les recherches scientifiques dans ce domaine et l’évaluation des risques éruptifs visent toujours le même objectif: á savoir comprendre les processus physiques de l’éruption. Les réseaux de capteurs sismiques déployés en antenne sismique sont devenus des outils puissants pour surveiller l’activité volcanique. Dans cette thèse, nous étudions le problème de la localisation de sources, basée sur les données enregistrées par des réseaux de capteurs composés de sismomètres à trois composantes (3C). Nous nous concentrerons sur le stratovolcan Ubinas, l’un des plus actifs au Pérou (environ 5400 m d’altitude et possedant une grande caldeira de 1.4 km). Les éruptions de ce volcan historiques et récentes ont présenté un style majoritairement vulcanian caractérisé par des explosions de courte durée et violentes...
    Palabras clave:VulcanismoErupciones volcánicasSismología