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Wei D L，Wang Y W，Wang Z F，Liao J A，Zhao D K. 2022. A fast estimation method of earthquake magnitude based on convolutional neural networks. Acta Seismologica Sinica，44（2）：316−326. DOI: 10.11939/jass.20210198
Citation: Wei D L，Wang Y W，Wang Z F，Liao J A，Zhao D K. 2022. A fast estimation method of earthquake magnitude based on convolutional neural networks. Acta Seismologica Sinica，44（2）：316−326. DOI: 10.11939/jass.20210198
shu

A fast estimation method of earthquake magnitude based on convolutional neural networks

  • 1.

    College of Architecture and Civil Engineering，Henan University，Henan Kaifeng 475004，China

  • 2.

    College of Architecture and Civil Engineering，Guilin University of Technology，Guangxi Guilin 541004，China

  • 3.

    CEAKJ ADPRHexa Inc，Guangdong Shaoguan 512026，China

  • 4.

    Institute of Engineering Mechanics，China Earthquake Administration，Harbin 150080，China

More Information
  • Received Date: November 04, 2021
  • Revised Date: January 03, 2022
  • Accepted Date: March 08, 2022
  • Available Online: April 21, 2022
  • Published Date: April 23, 2022
    Graphical Abstract
    • Abstract
  • Earthquake early warning （EEW） is an effective approach to reduce human casualty and economic loss resulted from destructive earthquakes. Quick and accurate magnitude estimation after an earthquake is an important part of EEW, and the traditional approaches of magnitude estimation based on empirical parameters have their limits in accuracy and timeliness. This paper proposed a multi-fully connected convolutional neural network to quickly estimate the magnitude based on the information from a single station. The frequency-domain information from 3 065 earthquakes recorded by Japan’s KiK-net and K-NET networks between 1997 and 2019 （arriving waves corresponding to the selected data ranging from 3 s to 9 s）, together with the corresponding information on hypocentral distance, focal depth, and site conditions （vS30） are used to train and validate the proposed model. The validation results demonstrate that the magnitude estimate accuracy is 89.92% even using as little as 3 s of arriving waves and the accuracy improves as longer duration of arriving waves is used. When 9 s of arriving waves are used, the accuracy increases to 96.08%. Comparison with the traditional Pd method suggests that the proposed approach in this study has smaller mean and standard deviation of the absolute estimation error, thus the proposed method has better accuracy and timeliness and would greatly enhance the disaster mitigation effects of the EEW systems.
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    • References (34)
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