Articles | Volume 23, issue 2
Research article
03 Mar 2023
Research article |  | 03 Mar 2023

Multi-station automatic classification of seismic signatures from the Lascar volcano database

Pablo Salazar, Franz Yupanqui, Claudio Meneses, Susana Layana, and Gonzalo Yáñez

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Cited articles

Álvarez, I., García, L., Cortés, G., Benítez, C., and De la Torre, Á.: Discriminative feature selection for automatic classification of volcano-seismic signals, IEEE Geosci. Remote S., 9, 151–155, 2012. a
Araujo, A. F. R. and Rego, R. L. M. E.: Self-organizing maps with a time-varying structure, ACM Comput. Surv., 46, 7,, 2013. a
Bebbington, M.: Identifying volcanic regimes using Hidden Markov Models, Geophys. J. Int., 171, 921–942,, 2007. a
Beyreuther, M. and Wassermann, J.: Continuous earthquake detection and classification using discrete Hidden Markov Models, Geophys. J. Int., 175, 1055–1066, 2008. a, b
Beyreuther, M. and Wassermann, J.: Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers, Nonlin. Processes Geophys., 18, 81–89,, 2011. a
Short summary
The acquisition of more generalizable models, using machine learning techniques, creates a good opportunity to develop a multi-volcano probabilistic model for volcanoes worldwide. This will improve the understanding and evaluation of the hazards and risks associated with the activity of volcanoes.
Final-revised paper