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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
PDF(5529 KB)
PDF(5529 KB)
基于机器学习和迁移学习的现地地震动峰值预测
Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning
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在现地地震预警中，为了提高中国仪器地震烈度计算中地震动峰值（PGA和PGV）预测的准确性，提出了基于机器学习和迁移学习的现地地震动峰值预测方法.基于日本K-NET台网记录的强震动数据，使用神经网络建立了预训练的现地地震动峰值预测模型；基于中国的强震动数据和预训练的现地地震动峰值预测模型，通过迁移学习建立了用于中国的现地地震动峰值预测模型.对于日本和中国测试数据集以及泸定6.8级地震，在P波到达后3 s，和传统的现地地震动峰值预测方法相比，本研究提出的方法对于PGA预测和PGV预测有更小的平均绝对误差和标准差.结果表明，本研究提出的方法可以在一定程度上提高现地地震预警地震动峰值预测的可靠性，对于现地地震预警系统具有重要意义.
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.
地震 / 机器学习 / 迁移学习 / P波 / 地震动峰值 / 人工智能.
earthquakes / machine learning / transfer learning / P wave / peak ground motion / artificial intelligence
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