Earthquake Event Classification Using Multitasking Deep Learning

Bonhwa Ku, Jeungki Min, Jae Kwang Ahn, Jimin Lee, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


This letter proposes an attention-based convolutional neural network architecture for multitasking learning to accurately classify not only the presence of an earthquake but also the event type of the earthquake. In particular, to improve the performance in earthquake-type classification, we develop an attention-based feature aggregation framework embedded in multitask learning architecture. Representative experimental results show that the proposed method provides an effective structure for an earthquake detection and event classification with an earthquake database of the Korean peninsula and the Circum-Pacific belt.

Original languageEnglish
Article number9107451
Pages (from-to)1149-1153
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number7
Publication statusPublished - 2021 Jul


  • Attention module
  • Convolutional neural network (CNN)
  • Earthquake event classification
  • Feature aggregation
  • Multitasking deep learning

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Earthquake Event Classification Using Multitasking Deep Learning'. Together they form a unique fingerprint.

Cite this