@misc{10481/107429,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/107429},
abstract = {Real-time monitoring of volcano-seismic signals
is complex. Typically, automatic systems are built by learning from large seismic catalogs, where each instance has a label indicating its source mechanism. However, building complete catalogs is difficult owing to the high cost of data labeling. Current machine learning techniques have achieved
great success in constructing predictive monitoring tools;
nevertheless, catalog-based learning can introduce bias into
the system. Here, we show that while monitoring systems
trained on annotated data from seismic catalogs achieve performance of up to 90 % in event recognition, other information describing volcanic behavior is not considered but is
either discarded. We found that weakly supervised learning
approaches have the remarkable capability of simultaneously
identifying unannotated seismic traces in the catalog and correcting misannotated seismic traces. When a system trained
on a master dataset and catalog from Deception Island Volcano (Antarctica) is used as a pseudo-labeler in other volcanic contexts, such as Popocatépetl (Mexico) and Tajogaite
(Canary Islands) volcanoes, within the framework of weakly
supervised learning, it can uncover and update valuable information related to volcanic dynamics. Our results offer the
potential for developing more sophisticated semi-supervised
models to increase the reliability of monitoring tools. For example, the use of more sophisticated pseudo-labeling techniques involving data from several catalogs could be tested.
Ultimately, there is potential to develop universal monitoring
tools that are able to consider unforeseen temporal changes
in monitored signals at any volcano.},
organization = {MICIU/AEI/10.13039/501100011033, FEDER, EU – (PID2022-143083NB-I00, LEARNING)},
organization = {MCIN/AEI/10.13039/501100011033, European Union NextGenerationEU/PRTR – (PLEC2022-009271, DigiVolCan)},
organization = {SDAS (B-TIC-542- UGR20)},
publisher = {Copernicus Publications},
title = {Could seismo-volcanic catalogs be improved or created using weakly supervised approaches with pre-trained systems?},
doi = {10.5194/nhess-25-3827-2025},
author = {Titos Luzón, Manuel Marcelino and Benítez Ortúzar, María Del Carmen and D’Auria, Luca and Kowsari, Milad and Ibáñez Godoy, Jesús Miguel},
}