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Comparative Analysis of Earthquake Detection Methods Using Deep Learning: Reproducibility and Uncertainty Assessment in EQTransformer

Comparative Analysis of Earthquake Detection Methods Using Deep Learning: Reproducibility and Uncertainty Assessment in EQTransformer

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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Authors

Sebastián Francisco Gamboa-Chacón, Esteban Chaves Sibaja, Esteban Meneses Rojas

Abstract

This study evaluates the performance and reliability of earthquake detection using the EQTransformer, a novel AI program that is widely used in seismological observatories and research for enhancing earthquake catalogs. We test the EQTransformer capabilities and uncertainties using seismic data from the Volcanological and Seismological Observatory of Costa Rica and compare two detection options: the simplified method (MseedPredictor) and the complex method (Predictor), the latter incorporating Monte Carlo Dropout, to assess their reproducibility and uncertainty in identifying seismic events. Our analysis focuses on 24 hour-duration data that ...  more

DOI

https://doi.org/10.31223/X54T4F

Subjects

Physical Sciences and Mathematics

Keywords

AI Earthquake detection, Deep learning, EQTransformer, reproducibility, Determinism

Dates

Published: 2024-09-18 23:07

Last Updated: 2024-09-19 03:07

License

CC BY Attribution 4.0 International