A deep learning approach to classify volcano activity using tremor data joint with infrasonic event counts and radar backscatter power; case study: mount Etna, Italy-Reference-Cited by-同舟云学术

A deep learning approach to classify volcano activity using tremor data joint with infrasonic event counts and radar backscatter power; case study: mount Etna, Italy

Published:2024-07-11 Issue: Volume: Page:
ISSN:1895-7455
Container-title:Acta Geophysica
language:en
Short-container-title:Acta Geophys.

Author:

Abazari Alireza,Hajian Alireza^ORCID,Kimiaefar Roohollah,Hodhodi Maryam,Gambino Salvatore

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11600-024-01412-5.pdf

Reference27 articles.

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2. Anantrasirichai N, Biggs J, Albino F, Hill P, Bull DR (2018) Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. J Geophys Res: Solid Earth 123:6592–6606. https://doi.org/10.1029/2018JB015911

3. Anantrasirichai N, Biggs J, Albino F, Bull DR (2019). A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. ArXiv, abs/1905.07286

4. Behncke B, Branca S, Corsaro RA, De Beni E, Miraglia L, Proietti C (2014) The 2011–2012 summit activity of Mount Etna: birth, growth and products of the new SE crater. J Volcanol Geotherm Res 270:10–21. https://doi.org/10.1016/j.jvolgeores.2013.11.012

5. Bonaccorso AA, Cannata RA, Corsaro G, Di Grazia S, Gambino F, Greco L, Miraglia A, Pistorio A (2011) Multidisciplinary investigation on a lava fountain preceding a flank eruption: the 10 491 May 2008 Etna case. Geochem Geophys Geosyst 12:Q07009. https://doi.org/10.1029/2010GC003480