Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning

Gwantae Kim, Bonhwa Ku, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


This letter proposes a multifeature fusion model using deep convolution neural networks and transfer learning approach for earthquake event classification. There are several feature representations for seismic analysis, such as the time domain, the frequency domain, and the time-frequency domain. To successfully classify various earthquake events, we propose a novel model that combines these features hierarchically. In addition, we apply a transfer learning to mitigate overfitting problem of deep learning model while achieving high classification performance. To evaluate our approach, we conduct experiments with the Korean peninsula earthquake database from 2016 to 2018 and a large earthquake database on the Circum-Pacific belt in 2019. The experimental results show that the proposed method outperforms over the compared state-of-the-art methods.

Original languageEnglish
Article number9098918
Pages (from-to)974-978
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number6
Publication statusPublished - 2021 Jun


  • Convolution neural network (CNN)
  • deep learning
  • earthquake event classification
  • multifeature fusion
  • transfer learning

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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