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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Cited articles

Allen, R., Gasparini, P., Kamigaichi, O., and Böse, M.: The Status of Earthquake Early Warning around the World: An Introductory Overview, Seismol. Res. Lett. 80, 682–693,, 2009. 
Allen, R. and Kanamori, H.: The Potential for Earthquake Early Warning in Southern California, Science, 300, 786–789,, 2003. 
Allen, R. M. and Melgar, D.: Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs, Annu. Rev. Earth Planet Sc., 47, 361–388,, 2019. 
Aly, M.: Survey on multiclass classification methods, Neural Netw., 19, 1–9, 2005. 
Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, SIGKDD Explorations Newsletter, 6, 20–29,, 2004. 
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 %.