基于机器学习和迁移学习的现地地震动峰值预测

朱景宝; 刘赫奕; 栾世成; 梁坤正; 宋晋东; 李山有

doi:10.3799/dqkx.2024.071

Volume 50 Issue 5

May 2025

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Article Navigation > Earth Science > 2025 > 50(5): 1842-1860

Zhu Jingbao, Liu Heyi, Luan Shicheng, Liang Kunzheng, Song Jindong, Li Shanyou, 2025. Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning. Earth Science, 50(5): 1842-1860. doi: 10.3799/dqkx.2024.071

Citation:

Zhu Jingbao, Liu Heyi, Luan Shicheng, Liang Kunzheng, Song Jindong, Li Shanyou, 2025. Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning. Earth Science, 50(5): 1842-1860. doi: 10.3799/dqkx.2024.071

Citation:

Zhu Jingbao, Liu Heyi, Luan Shicheng, Liang Kunzheng, Song Jindong, Li Shanyou, 2025. Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning. Earth Science, 50(5): 1842-1860. doi: 10.3799/dqkx.2024.071

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Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning

doi: 10.3799/dqkx.2024.071

Zhu Jingbao^{1, 2
,
,},
Liu Heyi^{1, 2},
Luan Shicheng^{1, 2},
Liang Kunzheng^{1, 2},
Song Jindong^{1, 2},
Li Shanyou^{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: 2024-03-26
Available Online: 2025-06-06
Publish Date: 2025-05-25

Abstract

Abstract

To improve the accuracy of peak ground motion (peak ground acceleration (PGA) and peak ground velocity (PGV) prediction in Chinese instrument seismic intensity calculation for on-site earthquake early warning (EEW), a prediction method of on-site peak ground motion based on machine learning and transfer learning is proposed. A pretrained on-site peak ground motion prediction model was established using neural networks based on strong motion data recorded by the K-NET network in Japan. Based on strong motion data from China and the pretrained on-site peak ground motion prediction model, an on-site peak ground motion prediction model for China was established through transfer learning. For the Japanese and Chinese test dataset and Luding M6.8 eathquake, at 3 s after the arrival of P-wave, compared to traditional on-site peak ground motion prediction method, the method proposed in this study has smaller mean absolute error and standard deviation for PGA prediction and PGV prediction. The results indicate that the method proposed in this study can improve the reliability of predicting peak ground motion in on-site EEW to a certain extent, which is of great significance for the development of on-site EEW systems.
- earthquakes,
- machine learning,
- transfer learning,
- P wave,
- peak ground motion,
- artificial intelligence

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References(54)

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Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning

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