Browsing by Author "Dalla Mura, Mauro"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Restricted 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 AdolfoThe 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.Item Open Access 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, MarielleThis 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%.Item Restricted Machine learning for volcano-seismic signals: challenges and perspectives(Institute of Electrical and Electronics Engineers Inc. (IEEE), 2018-03-09) Malfante, Marielle; Dalla Mura, Mauro; Metaxian, Jean-Phillipe; Mars, Jerome I.; Macedo Sánchez, Orlando Efraín; Inza Callupe, Lamberto AdolfoEnvironmental 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 on a larger scale.