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Articles | Volume 13, issue 11
Articles | Volume 13, issue 11
https://doi.org/10.5194/se-13-1721-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-13-1721-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 11
Method article
 | 
10 Nov 2022
Method article |  | 10 Nov 2022

A study on the effect of input data length on a deep-learning-based magnitude classifier

Megha Chakraborty, Wei Li, Johannes Faber, Georg Rümpker, Horst Stoecker, and Nishtha Srivastava

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Latest update: 13 Dec 2024
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Short summary
Earthquake magnitude is a crucial parameter in defining its damage potential, and hence its speedy determination is essential to issue an early warning in regions close to the epicentre. This study summarises our findings in an attempt to apply deep-learning-based classifiers to earthquake waveforms, particularly with respect to finding an optimum length of input data. We conclude that the input length has no significant effect on the model accuracy, which varies between 90 %–94 %.
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