Analysis of normalization effect for earthquake events classification

Shou Zhang, Bonhwa Ku, Hansoek Ko

Research output: Contribution to journalArticlepeer-review


This paper presents an effective structure by applying various normalization to Convolutional Neural Networks (CNN) for seismic event classification. Normalization techniques can not only improve the learning speed of neural networks , but also show robustness to noise. In this paper, we analyze the effect of input data normalization and hidden layer normalization on the deep learning model for seismic event classification. In addition an effective model is derived through various experiments according to the structure of the applied hidden layer. As a result of various experiments, the model that applied input data normalization and weight normalization to the first hidden layer showed the most stable performance improvement.

Original languageEnglish
Pages (from-to)130-138
Number of pages9
JournalJournal of the Acoustical Society of Korea
Issue number2
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Acoustical Society of Korea. All rights reserved.


  • Convolutional neural network
  • Hidden layer
  • Normalization
  • Seismic event classification

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation
  • Acoustics and Ultrasonics
  • Applied Mathematics
  • Speech and Hearing


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