基于长短期记忆神经网络的实时地震烈度预测模型

胡进军; 丁祎天; 张辉; 靳超越; 汤超

doi:10.3799/dqkx.2022.338

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1853-1864

Hu Jinjun, Ding Yitian, Zhang Hui, Jin Chaoyue, Tang Chao, 2023. A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network. Earth Science, 48(5): 1853-1864. doi: 10.3799/dqkx.2022.338

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Hu Jinjun, Ding Yitian, Zhang Hui, Jin Chaoyue, Tang Chao, 2023. A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network. Earth Science, 48(5): 1853-1864. doi: 10.3799/dqkx.2022.338

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A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network

doi: 10.3799/dqkx.2022.338

Hu Jinjun^{1, 2
,
,
,},
Ding Yitian^{1, 2},
Zhang Hui^{1, 2},
Jin Chaoyue^{1, 2},
Tang Chao^{1, 2}

1.
Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2.
Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

Received Date: 2022-08-19
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

Real-time intensity prediction can estimate the maximum possible impact of an earthquake based on P-wave before the arrival of destructive seismic waves. Earthquake early warning targets can take measures to reduce the potential damage. Peak P-wave displacement amplitude is a parameter that effectively estimates the peak ground motion, however, it is difficult to fully characterize the information in ground motion by a single or multiple parameters. Meanwhile, the calculation of the parameter requires the determination of the time window size, and continuous prediction cannot be achieved. To solve the above problems, a prediction model based on long short-term memory network is proposed in this paper. The model is constructed based on K-NET data from 2010‒2021, and the M_JMA 7.3 earthquake event in March 2022 is selected as a case to validate the model. The results show that the intensity can be predicted at each time step of the record after the P-wave arrival, and the accuracy in the test set is 96.47% at 3 seconds after P-wave arrival. The LSTM model proposed in this paper improves the accuracy and continuity of intensity prediction and can provide a scientific basis for earthquake early warning and emergency response.
- seismic intensity,
- real time,
- neural network,
- deep learning,
- earthquake early warning,
- engineering geology

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References(42)

References

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A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network

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