基于多模态深度学习的中国地震仪器烈度预测模型

朱景宝; 李山有; 宋晋东

doi:10.3799/dqkx.2025.078

Volume 51 Issue 1

Jan. 2026

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Zhu Jingbao, Li Shanyou, Song Jindong, 2026. A Chinese Seismic Instrument Intensity Prediction Model Based on Multimodal Deep Learning. Earth Science, 51(1): 14-29. doi: 10.3799/dqkx.2025.078

Citation:

Zhu Jingbao, Li Shanyou, Song Jindong, 2026. A Chinese Seismic Instrument Intensity Prediction Model Based on Multimodal Deep Learning. Earth Science, 51(1): 14-29. doi: 10.3799/dqkx.2025.078

Zhu Jingbao, Li Shanyou, Song Jindong, 2026. A Chinese Seismic Instrument Intensity Prediction Model Based on Multimodal Deep Learning. Earth Science, 51(1): 14-29. doi: 10.3799/dqkx.2025.078

Citation:

Zhu Jingbao, Li Shanyou, Song Jindong, 2026. A Chinese Seismic Instrument Intensity Prediction Model Based on Multimodal Deep Learning. Earth Science, 51(1): 14-29. doi: 10.3799/dqkx.2025.078

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A Chinese Seismic Instrument Intensity Prediction Model Based on Multimodal Deep Learning

doi: 10.3799/dqkx.2025.078

Zhu Jingbao^{1, 2
,
,},
Li Shanyou^{1, 2},
Song Jindong^{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: 2025-03-07
Publish Date: 2026-01-25

Abstract

Abstract

The Chinese seismic instrument intensity prediction is crucial for earthquake early warning (EEW) and hazard mitigation in China, but traditional methods suffer from issues such as insufficient accuracy and insufficient fusion of multi-source data. This study aims to construct a multimodal deep learning model, explore its feasibility for predicting seismic instrument intensity in China, and improve the accuracy and robustness of instrument intensity prediction for EEW. A Multimodal Chinese Instrument Intensity prediction Network (MCIINet) is proposed, which is trained and tested by the seismic events recorded by China Earthquake Networks Center. Experiments have shown that on the test dataset, compared to the baseline model at 3 s after P-wave triggering, MCIINet reduced MAE and RMSE of instrument intensity prediction by 9.03% and 8.67%, respectively, and improved R² and accuracy by 9.10% and 2.51%, respectively. MCIINet has effectively improved the accuracy of intensity prediction through multimodal deep feature fusion, verifying the feasibility of multimodal deep learning for seismic instrument intensity prediction in China, and providing technical support for instrument intensity prediction in EEW.
- seismic instrument intensity,
- earthquake early warning,
- multimodal,
- deep learning,
- P wave,
- seismology

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References(83)

References

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