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

Viewed

Total article views: 4,077 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,821 970 286 4,077 119 147
  • HTML: 2,821
  • PDF: 970
  • XML: 286
  • Total: 4,077
  • BibTeX: 119
  • EndNote: 147
Views and downloads (calculated since 17 Jun 2024)
Cumulative views and downloads (calculated since 17 Jun 2024)

Viewed (geographical distribution)

Total article views: 4,077 (including HTML, PDF, and XML) Thereof 3,915 with geography defined and 162 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 08 Jun 2026
Download
Short summary
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint