Attention-Based Convolutional Neural Network for Earthquake Event Classification

Bonhwa Ku, Gwantae Kim, Jae Kwang Ahn, Jimin Lee, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This letter presents a deep convolutional neural network (CNN) with attention module that improves the performance of the classification of various earthquake events. Addressing all possible earthquake events, including not only microearthquakes and artificial-earthquakes but also large-earthquakes, requires both suitable feature expression and a classifier that can effectively discriminate seismic waveforms under adverse conditions. To robustly classify earthquake events, a deep CNN with an attention module was proposed in raw seismic waveforms. Representative experimental results show that the proposed method provides an effective structure for earthquake events classification and, with the Korean peninsula earthquake database from 2016 to 2018, outperforms previous state-of-the-art methods.

Original languageEnglish
Pages (from-to)2057-2061
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number12
Publication statusPublished - 2021 Dec 1


  • Attention module
  • convolutional neural network (CNN)
  • deep learning
  • earthquake classification
  • raw seismic waveform

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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