Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives

Marielle Malfante, Mauro Dalla Mura, Jean Philippe Metaxian, Jerome I. Mars, Orlando Efrain Macedo Sánchez, Adolfo Inza

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

43 Citas (Scopus)

Resumen

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 machinelearning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring on a larger scale.

Idioma originalInglés
Páginas (desde-hasta)20-30
Número de páginas11
PublicaciónIEEE Signal Processing Magazine
Volumen35
N.º2
DOI
EstadoPublicada - mar. 2018

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