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

Dongsik Yoon; Yuanming Li; Bonhwa Ku; Hanseok Ko

doi:10.1109/LGRS.2023.3234299

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

Dongsik Yoon, Yuanming Li, Bonhwa Ku, Hanseok Ko

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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 language	English
Article number	3000305
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	20
DOIs	https://doi.org/10.1109/LGRS.2023.3234299
Publication status	Published - 2023

Bibliographical note

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

Publisher Copyright:
© 2012 IEEE.

Keywords

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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10.1109/LGRS.2023.3234299

Cite this

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title = "Estimation of Magnitude and Epicentral Distance From Seismic Waves Using Deeper CRNN",

abstract = "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.",

keywords = "Deep convolutional recurrent neural network (CRNN), epicentral distance estimation, magnitude estimation, multitasking deep learning",

author = "Dongsik Yoon and Yuanming Li and Bonhwa Ku and Hanseok Ko",

note = "Funding Information: This work was supported by the Brain Korea 21 FOUR Project in 2022. Publisher Copyright: {\textcopyright} 2012 IEEE.",

year = "2023",

doi = "10.1109/LGRS.2023.3234299",

language = "English",

volume = "20",

journal = "IEEE Geoscience and Remote Sensing Letters",

issn = "1545-598X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Estimation of Magnitude and Epicentral Distance From Seismic Waves Using Deeper CRNN

AU - Yoon, Dongsik

AU - Li, Yuanming

AU - Ku, Bonhwa

AU - Ko, Hanseok

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Deep convolutional recurrent neural network (CRNN)

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KW - magnitude estimation

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SN - 1545-598X

VL - 20

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

M1 - 3000305

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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