PDF(5529 KB)
Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning
Zhu Jingbao, Liu Heyi, Luan Shicheng, Liang Kunzheng, Song Jindong, Li Shanyou
PDF(5529 KB)
PDF(5529 KB)
Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning
,
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
|
Allen, R. M., Melgar, D., 2019. Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs. Annual Review of Earth and Planetary Sciences, 47: 361-388. https://doi.org/10.1146/annurev-earth-053018-060457
|
|
Brondi, P., Picozzi, M., Emolo, A., et al., 2015. Predicting the Macroseismic Intensity from Early Radiated P Wave Energy for On-Site Earthquake Early Warning in Italy. Journal of Geophysical Research: Solid Earth, 120(10): 7174-7189. https://doi.org/10.1002/2015jb012367
|
|
Chen, M., Wang, H., 2022. Explainable Machine Learning Model for Prediction of Ground Motion Parameters with Uncertainty Quantification. Chinese Journal of Geophysics, 65(9): 3386-3404 (in Chinese with English abstract).
|
|
Chung, J., Gulcehre, C., Cho, K., et al., 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXivv: 1412.3555. https://arxiv.org/abs/1412.3555v1
|
|
Fu, Z.Y., Li, D.Q., Wang, S., et al., 2023. Landslide Susceptibility Assessment Based on Multitemporal Landslide Inventories and TrAdaBoost Transfer Learning. Earth Science, 48(5): 1935-1947 (in Chinese with English abstract).
|
|
Hsu, T. Y., Huang, C. W., 2021. Onsite Early Prediction of PGA Using CNN with Multi-Scale and Multi-Domain P-Waves as Input. Frontiers in Earth Science, 9: 626908. https://doi.org/10.3389/feart.2021.626908
|
|
Hsu, T. Y., Pratomo, A., 2022. Early Peak Ground Acceleration Prediction for On-Site Earthquake Early Warning Using LSTM Neural Network. Frontiers in Earth Science, 10: 911947. https://doi.org/10.3389/feart.2022.911947
|
|
Hu, J.J., Ding, Y.T., Zhang, H., et al., 2023. A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network. Earth Science, 48(5): 1853-1864 (in Chinese with English abstract).
|
|
Kamigaichi, O., Saito, M., Doi, K., et al., 2009. Earthquake Early Warning in Japan: Warning the General Public and Future Prospects. Seismological Research Letters, 80(5): 717-726. https://doi.org/10.1785/gssrl.80.5.717
|
|
Kanamori, H., 2005. Real-Time Seismology and Earthquake Damage Mitigation. Annual Review of Earth and Planetary Sciences, 33: 195-214. https://doi.org/10.1146/annurev.earth.33.092203.122626
|
|
Kanamori, H., 2015. Earthquake Hazard Mitigation and Real-Time Warnings of Tsunamis and Earthquakes. Pure and Applied Geophysics, 172(9): 2335-2341. https://doi.org/10.1007/s00024-014-0964-y
|
|
Kong, Q. K., Wang, R. J., Walter, W. R., et al., 2022. Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination. Geophysical Research Letters, 49(13): e2022GL098645. https://doi.org/10.1029/2022gl098645
|
|
LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep Learning. Nature, 521(7553): 436-444. https://doi.org/10.1038/nature14539
|
|
Li, Z. F., 2022. A Generic Model of Global Earthquake Rupture Characteristics Revealed by Machine Learning. Geophysical Research Letters, 49(8): e2021GL096464. https://doi.org/ 10.1029/2021GL096464
|
|
Liu, C., Li, X.J., Jing, B.B., et al., 2019. The Distance Segmentation Characters of PGV-Pd Relationship Parameters for Earthquake Early Warning. Chinese Journal of Geophysics, 62(4): 1413-1426 (in Chinese with English abstract).
|
|
Lü, S., Fang, L. H., Ren, H. Y., et al., 2024. Overview of the Earthquake Monitoring Status in the United States. China Earthquake Engineering Journal, 46(2): 431-448 (in Chinese with English abstract).
|
|
Ma, Q., 2008. Study and Application on Earthquake Early Warning (Dissertation). Institute of Engineering Mechanics, China Earthquake Administration, Harbin (in Chinese with English abstract).
|
|
Ma, Q., Jin, X., Li, S.Y., et al., 2013. Automatic P-Arrival Detection for Earthquake Early Warning. Chinese Journal of Geophysics, 56(7): 2313-2321 (in Chinese with English abstract).
|
|
Mousavi, S. M., Beroza, G. C., 2020. A Machine-Learning Approach for Earthquake Magnitude Estimation. Geophysical Research Letters, 47(1): e2019GL085976.
|
|
Pan, S. J., Yang, Q., 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10): 1345-1359. https://doi.org/10.1109/TKDE.2009.191
|
|
Peng, C. Y., Ma, Q., Jiang, P., et al., 2020. Performance of a Hybrid Demonstration Earthquake Early Warning System in the Sichuan-Yunnan Border Region. Seismological Research Letters, 91(2A): 835-846. https://doi.org/10.1785/0220190101
|
|
Serdar Kuyuk, H., Allen, R. M., Brown, H., et al., 2014. Designing a Network-Based Earthquake Early Warning Algorithm for California: ElarmS-2. Bulletin of the Seismological Society of America, 104(1): 162-173. https://doi.org/10.1785/0120130146
|
|
Song, J., Zhu, J., Li, S., 2023. MEANet: Magnitude Estimation via Physics-Based Features Time Series, an Attention Mechanism, and Neural Networks. Geophysics, 88(1): V33-V43. https://doi.org/10.1190/geo2022-0196.1
|
|
Song, J.D., Jiao, C.C., Li, S.Y., et al., 2018. Prediction Method of First-Level Earthquake Warning for High Speed Railway Based on Two-Parameter Threshold of Seismic P-Wave. China Railway Science, 39(1): 138-144 (in Chinese with English abstract).
|
|
Song, J.D., Yu, C., Li, S.Y., 2021. Continuous Prediction of Onsite PGV for Earthquake Early Warning Based on Least Squares Support Vector Machine. Chinese Journal of Geophysics, 64(2): 555-568 (in Chinese with English abstract).
|
|
State Administration for Market Regulation, Standardization Administration of China, 2020. GB/T‐17742‐2020, The Chinese Seismic Intensity Scale. China Quality and Standards Publishing & Media Co., Ltd., Beijing (in Chinese).
|
|
Wald, D. J., Quitoriano, V., Heaton, T. H., et al., 1999. Relationships between Peak Ground Acceleration, Peak Ground Velocity, and Modified Mercalli Intensity in California. Earthquake Spectra, 15(3): 557-564. https://doi.org/10.1193/1.1586058
|
|
Wang, C. Y., Huang, T. C., Wu, Y. M., 2022. Using LSTM Neural Networks for Onsite Earthquake Early Warning. Seismological Research Letters, 93(2A): 814-826. https://doi.org/10.1785/0220210197
|
|
Wang, D., Sun, K., 2022. How the Big Data Seismology and AI Refine Rapid Determination of Source Parameters of Large Earth-Quakes?. Earth Science, 47(10): 3915-3917 (in Chinese with English abstract).
|
|
Wang, J., Yang, Y., Mao, J. H., et al., 2016. CNN-RNN: A Unified Framework for Multi-Label Image Classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2285-2294. https://doi.org/10.1109/CVPR.2016.251
|
|
Wu, L.Y., Tong, J.B., Wang, Z.F., et al., 2023. Classification of Damaged Grade on Rural Houses after Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning. Earth Science, 48(5): 1742-1754 (in Chinese with English abstract).
|
|
Wu, Y. M., Kanamori, H., 2005. Rapid Assessment of Damage Potential of Earthquakes in Taiwan from the Beginning of P Waves. Bulletin of the Seismological Society of America, 95(3): 1181-1185. https://doi.org/10.1785/0120040193
|
|
Yu, C., Song, J. D., Li, S. Y., 2021. Prediction of Ground Motion for On-Site Earthquake Early Warning Based on SVM. Journal of Vibration and Shock, 40(3): 63-72, 80 (in Chinese with English abstract).
|
|
Zhang, H., Jin, X., Wei, Y. X., et al., 2016. An Earthquake Early Warning System in Fujian, China. Bulletin of the Seismological Society of America, 106(2): 755-765. https://doi.org/10.1785/0120150143
|
|
Zhou, L. Q., Zhao, C. P., Zhang, M., et al., 2021. Machine-Learning-Based Earthquake Locations Reveal the Seismogenesis of the 2020 Mw 5.0 Qiaojia, Yunnan Earthquake. Geophysical Journal International, 228(3): 1637-1647. https://doi.org/10.1093/gji/ggab420
|
|
Zhu, J., Li, Z. F., Fang, L. H., 2023a. USTC-Pickers: A Unified Set of Seismic Phase Pickers Transfer Learned for China. Earthquake Science, 36(2): 95-112. https://doi.org/10.1016/j.eqs.2023.03.001
|
|
Zhu, J. B., Li, S. Y., Wei, Y. X., et al., 2023b. On-Site Instrumental Seismic Intensity Prediction for China via Recurrent Neural Network and Transfer Learning. Journal of Asian Earth Sciences, 248: 105610. https://doi.org/10.1016/j.jseaes.2023.105610
|
|
Zhu, J. B., Song, J. D., Li, S. Y., 2024. Influence of Low-Cost Sensors on Earthquake Early Warning Magnitude Estimation Using Convolutional Neural Network Model. Journal of Harbin Institute of Technology, 56(6): 81-90 (in Chinese with English abstract).
|
|
Zhu, J. B., Li, S. Y., Song, J. D., 2022. Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction. IEEE Transactions on Geoscience and Remote Sensing, 60: 5925712. https://doi.org/10.1109/TGRS.2022.3230829
|
|
Zollo, A., Amoroso, O., Lancieri, M., et al., 2010. A Threshold-Based Earthquake Early Warning Using Dense Accelerometer Networks. Geophysical Journal International, 183(2): 963-974. https://doi.org/10.1111/j.1365-246X.2010.04765.x
|
/
| 〈 |
|
〉 |
AI Summary
