Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 10
Articles | Volume 25, issue 10
https://doi.org/10.5194/nhess-25-3827-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3827-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 10
Research article
 | 
08 Oct 2025
Research article |  | 08 Oct 2025

Could seismo-volcanic catalogs be improved or created using weakly supervised approaches with pre-trained systems?

Manuel Titos, Carmen Benítez, Luca D'Auria, Milad Kowsari, and Jesús Miguel Ibáñez

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Cited articles

Alasonati, P., Wassermann, J., and Ohrnberger, M.: Signal classification by wavelet-based hidden Markov models: application to seismic signals of volcanic origin, Geophysical Journal International, 165, 452–466, https://doi.org/10.1111/j.1365-246X.2006.02878.x, 2006. a, b
Arango-Galván, C., Martin-Del Pozzo, A. L., Flores-Márquez, E. L., González-Morán, T., Vidal-Amaro, M., and Ruiz-Aguilar, D.: Unraveling the complex structure of Popocatépetl volcano (Central Mexico): new evidence for collapse features and active faulting inferred from geophysical data, Journal of Volcanology and Geothermal Research, 407, 107091, https://doi.org/10.1016/j.jvolgeores.2020.107091, 2020. a, b
Barra, F.: Geology of Mexico: Celebrating the Centenary of the Geological Society of Mexico. edited by: Alaniz-Alvarez, S. A. and Nieto-Samaniego, A. F., Geological Society of America, Special Paper 422, 465 pp., Boulder, Colorado, 2007, ISBN 13-978-0-8137-2422-5, Economic Geology, 103, 653–654, https://ui.adsabs.harvard.edu/abs/2008EcGeo.103..653B/abstract (last access: 29 September 2025), 2008. a
Benítez, M. C., Ramírez, J., Segura, J. C., Ibanez, J. M., Almendros, J., García-Yeguas, A., and Cortes, G.: Continuous HMM-based seismic-event classification at Deception Island, Antarctica, IEEE Transactions on Geoscience and Remote Sensing, 45, 138–146, https://doi.org/10.1109/TGRS.2006.882264, 2007. a, b, c
Bhatti, S. M., Khan, M. S., Wuth, J., Huenupan, F., Curilem, M., Franco, L., and Yoma, N. B.: Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models, Journal of Volcanology and Geothermal Research, 324, 134–143, https://doi.org/10.1016/j.jvolgeores.2016.01.002, 2016. a, b
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Developing seismo-volcanic monitoring tools is crucial for volcanic observatories. Our study reviews current methods using transfer learning techniques and finds that while these systems identify nearly 90 % of seismic events, they miss other important volcanic data due to the catalog-learning bias. We propose a weakly supervised technique to reduce bias and uncover new volcanic information. This method can improve existing databases and efficiently create new ones using machine learning.
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