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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Volcanology
Volume 12 - 2024 | doi: 10.3389/feart.2024.1440967
This article is part of the Research Topic Applications of Machine Learning in Volcanology View all 9 articles

Grid-search method for STA/LTA parameters tuning: an application to Stromboli Explosion Quakes

Provisionally accepted
Andrea Di Benedetto Andrea Di Benedetto 1,2*Anna Figlioli Anna Figlioli 3,4Antonino D'Alessandro Antonino D'Alessandro 1Giosue' Lo Bosco Giosue' Lo Bosco 2
  • 1 National Institute of Geophysics and Volcanology, National Earthquake Observatory, Rome, Lazio, Italy
  • 2 Department of Mathematics and Computer Science, University of Palermo, Palermo, Sicily, Italy
  • 3 Department of Earth and Sea Sciences, University of Palermo, Palermo, Sicily, Italy
  • 4 National Institute of Geophysics and Volcanology (INGV), Milan, Milan, Lombardy, Italy

The final, formatted version of the article will be published soon.

    The collection of a significant catalog of seismo-volcanic data involves the selection of relevant parts of raw signals, that can be automatized by using the Short-term over Long-term Average (STA/LTA) method. Since it is parametric, the common approach to the choice is the adoption of literature-suggested parameters. To overcome these limitations, in this paper, we propose a methodology for the automatic selection of STA/LTA parameters able to optimize the extraction of local events from a seismo-volcanic raw signal. The parameters are found by a grid search over an index named Quality-Numerosity Index (QNI) that measures the accordance in the automatic cuts and the consequent quantity of triggered seismo-volcanic events with the ones suggested by a human expert. The method was applied in the volcano domain, for the specific application of Explosion Quakes (EQs) signals extraction in Stromboli Volcano. Experiments have been conducted selecting a subset of the dataset as training where to search for the best parameters, which were subsequently adopted in a test set. The results demonstrate that the selected parameters significantly improve the quality of the extraction when compared to those extracted by adopting the parameters indicated in the literature.

    Keywords: STA/LTA method, machine learning, Parametrization Tuning, Grid search, Seismo-volcanic signals, Explosion quakes, Stromboli volcano

    Received: 30 May 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Di Benedetto, Figlioli, D'Alessandro and Lo Bosco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Andrea Di Benedetto, National Institute of Geophysics and Volcanology, National Earthquake Observatory, Rome, 00143, Lazio, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.