Malfante, MarielleDalla Mura, MauroMetaxian, Jean-PhillipeMars, Jerome I.Macedo Sánchez, Orlando EfraínInza Callupe, Lamberto Adolfo2018-08-072018-08-072018-03-09Malfante, M., Dalla, M., Metaxian, J., Mars, J., Macedo, O., & Inza, L. (2018). Machine learning for volcano-seismic signals: challenges and perspectives.==$IEEE Signal Processing Magazine, 35$==(2), 20-30. https://doi.org/10.1109/MSP.2017.2779166index-oti2018http://hdl.handle.net/20.500.12816/2301Environmental 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.application/pdfenginfo:eu-repo/semantics/closedAccessVolcanoesSignal processing algorithmsSeismic measurementsinfo:eu-repo/semantics/articlehttp://purl.org/pe-repo/ocde/ford#2.02.00http://purl.org/pe-repo/ocde/ford#1.05.07IEEE Signal Processing Magazinehttps://doi.org/10.1109/MSP.2017.2779166