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

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)20-30
Number of pages11
JournalIEEE Signal Processing Magazine
Volume35
Issue number2
DOIs
StatePublished - Mar 2018

Bibliographical note

Funding Information:
Mauro Dalla Mura (mauro.dalla-mura@gipsa-lab.fr) received the laurea (B.E.) and laurea specialistica (M.E.) degrees in telecommunication engineering from the University of Trento, Italy, in 2005 and 2007, respectively. In 2011, he obtained a joint Ph.D. degree in information and communication technologies (telecommunications area) from the University of Trento, Italy, and in electrical and computer engineering from the University of Iceland. He is currently an assistant professor at the Grenoble Institute of Technology, France. He was the recipient of the IEEE Geoscience and Remote Sensing Society (GRSS) Second Prize in the Student Paper Competition of the 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2011 and corecipient of the Best Paper Award of International Journal of Image and Data Fusion for the year 2012–2013 and the Symposium Paper Award for the IEEE IGARSS 2014. He has been the president of the IEEE GRSS French Chapter since 2016. He has been on the editorial board of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing since 2016.

Publisher Copyright:
© 1991-2012 IEEE.

Fingerprint

Dive into the research topics of 'Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives'. Together they form a unique fingerprint.

Cite this