Estimation of Magnitude and Epicentral Distance From Seismic Waves Using Deeper CRNN

Dongsik Yoon, Yuanming Li, Bonhwa Ku, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Estimating earthquake parameters is an essential process for an earthquake analysis system. In particular, the magnitude and epicentral distance of an earthquake are the most basic parameters in earthquake analysis. To estimate these, the existing approaches require long waveform data from multiple stations. In this letter, we propose a novel estimation method based on multitasking deep learning and a convolutional recurrent neural network (CRNN) using only a single station. We also use the stream maximum of the input waveform to accurately estimate the earthquake magnitude. Based on the evaluation using the Stanford Earthquake dataset (STEAD) and the Kiban Kyoshin Network (KiK-net) dataset, we verify the high performance of the proposed method.

Original languageEnglish
Article number3000305
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - 2023

Bibliographical note

Funding Information:
This work was supported by the Brain Korea 21 FOUR Project in 2022.

Publisher Copyright:
© 2012 IEEE.


  • Deep convolutional recurrent neural network (CRNN)
  • epicentral distance estimation
  • magnitude estimation
  • multitasking deep learning

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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