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

Yoon, Dongsik; Li, Yuanming; Ku, Bonhwa; Ko, Hanseok

doi:10.1109/LGRS.2023.3234299

상세 보기

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

Yoon, Dongsik;
Li, Yuanming;
Ku, Bonhwa;
Ko, Hanseok

Citations

WEB OF SCIENCE

0

Citations

SCOPUS

9

초록

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.

키워드

Feature extraction; Earthquakes; Estimation; Convolution; Recurrent neural networks; Data mining; Training; Deep convolutional recurrent neural network (CRNN); epicentral distance estimation; magnitude estimation; multitasking deep learning; NEURAL-NETWORK; QUANTIFICATION

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

저자: Yoon, Dongsik; Li, Yuanming; Ku, Bonhwa; Ko, Hanseok

DOI: 10.1109/LGRS.2023.3234299

발행일: 2023-01-01

유형: Article

저널명: IEEE Geoscience and Remote Sensing Letters

권: 20