Marielle Malfante

Atomic Energy and Alternative Energies Commission | CEA · Centre d'Etudes de Grenoble

This work proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionali...

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 fr...

After decades of work in all directions, some successes and unfortunately many resounding failures, earthquake prediction has been abandoned in favour of the development of seismic forecasting. The discovery of the nucleation phase of earthquakes coupled today with methods of ambient seismic noise correlation and machine learning has opened a new e...

This manuscript summarizes a three years work addressing the use of machine learning for the automatic analysis of natural signals. The main goal of this PhD is to produce efficient and operative frameworks for the analysis of environmental signals, in order to gather knowledge and better understand the considered environment. Particularly, we focu...

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...

The work presented in this paper focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. Specifically, it focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. To this end, various indicators can be used to monitor marine areas such as bo...

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 mo...

Dans le contexte de surveillance volcanique, la question de l'analyse automatique des données sismiques enregistrées en observatoire reste sans réponse. La quantité de données à analyser est telle qu'une analyse manuelle n'est plus possible. On considère ici l'utilisation de méthodes d'apprentissage statistique, et en particulier d'apprentissage su...

The prediction of volcanic eruptions and the evaluation of their associated risks is still a timely and open issue. For this purpose, several types of signals are recorded in the proximity of volcanoes and then analysed by experts. Typically, seismic signals that are considered as precursor or indicator of an active volcanic phase are detected and...