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Seismic severity estimation using convolutional neural network for earthquake early warning.

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Názov:	Seismic severity estimation using convolutional neural network for earthquake early warning.
Autori:	Ren, Tao, Liu, Xinliang, Chen, Hongfeng, Dimirovski, Georgi M, Meng, Fanchun, Wang, Pengyu, Zhong, Zhida, Ma, Yanlu
Zdroj:	Geophysical Journal International; Aug2023, Vol. 234 Issue 2, p1355-1362, 8p
Predmety:	CONVOLUTIONAL neural networks, EARTHQUAKE swarms, MAGNITUDE estimation, EARTHQUAKE magnitude, SEISMOGRAMS, EARTHQUAKES
Geografický termín:	TANGSHAN (Hebei Sheng, China)
Abstrakt:	SUMMARY: In this study, magnitude estimation in earthquake early warning (EEW) systems is seen as a classification problem: the single-channel waveform, starting from the P-wave onset and lasting 4 s, is given in the input, and earthquake severity (medium and large earthquakes: local magnitude (ML) ≥ 5; small earthquakes: ML < 5) is the classification result. The convolutional neural network (CNN) is proposed to estimate the severity of the earthquake, which is composed of several blocks that can extract the latent representation of the input from different receptive fields automatically. We train and test the proposed CNN model using two data sets. One is recorded by the China Earthquake Networks Center (CENC), and the other is the Stanford Earthquake Dataset (STEAD). Accordingly, the proposed CNN model achieves a test accuracy of 97.90 per cent. The proposed CNN model is applied to estimate two real-world earthquake swarms in China (the Changning earthquake and the Tangshan earthquake swarms) and the INSTANCE data set, and demonstrated the promising performance of generalization. In addition, the proposed CNN model has been connected to the CENC for further testing using real-world real-time seismic data. [ABSTRACT FROM AUTHOR]
	Copyright of Geophysical Journal International is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza:	Complementary Index

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