Abstract
Ground motion parameters are crucial characteristics in earthquake warning and earthquake engineering practice. However, the existing methods are time-consuming and labor-intensive. In this study, a multi-task approach (GMP-MT) based on a hard parameter sharing strategy and single station data is proposed to improve the overall estimation accuracy by jointly optimizing the estimation of peak ground acceleration (PGA) and peak ground velocity (PGV). In addition, this study reshapes the mean squared error by adjusting the weight of the loss according to the data distribution to solve the data imbalance. The developed network structure extracts not only the seismic features from various dimensions but also the spatial–temporal correlations from large-dimensional seismic data. The designed model is trained and tested based on the global three-component seismic waveform data recorded in the STanford EArthquake Dataset. Experimental results show that the correlation coefficients of PGA and PGV are above 90%, and the average errors are less than 0.19. The model has good stability, specifically insensitive to epicenter distance, hypocentral depth, and signal-to-noise ratio. Furthermore, the superiority of the model in terms of learning and fitting is demonstrated by comparison with several state-of-the-art models in the existing literature.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Waveform data, metadata, or data products for STEAD can be downloaded from https://github.com/smousavi05/STEAD. The Chinese dataset is from CENC. We only have the right to use data from CENC and cannot share it with others. If the reader is interested in these data, he/she can contact CENC (zouly@seis.ac.cn) for permission.
Code availability
The GMP-MT algorithm was developed based on Windows 10, pycharm, Python 3.6, and PyTorch 1.10. The algorithm size is 22 MB. The application may be downloaded from https://github.com/Fan-Chun-Meng/GMP-MT. For enquiries contact chinafcmeng@163.com.
References
Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput Struct 89(23–24):2176–2194. https://doi.org/10.1016/j.compstruc.2011.08.019
Alavi AH, Gandomi AH, Modaresnezhad M, Mousavi M (2011) New ground-motion prediction equations using multi expression programing. J Earthq Eng 15(4):511–536. https://doi.org/10.1080/13632469.2010.526752
Derakhshani A, Foruzan AH (2019) Predicting the principal strong ground motion parameters: a deep learning approach. Appl Soft Comput 80:192–201. https://doi.org/10.1016/j.asoc.2019.03.029
Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion prediction equations. Eng Appl Artif Intell 24(4):717–732. https://doi.org/10.1016/j.engappai.2011.01.005
Güllü H, Erçelebi E (2007) A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 93(3–4):65–81. https://doi.org/10.1016/j.enggeo.2007.05.004
Hacıefendioğlu K, Başağa HB, Demir G (2021) Automatic detection of earthquake-induced ground failure effects through Faster R-CNN deep learning-based object detection using satellite images. Nat Hazards 105:383–403. https://doi.org/10.1007/s11069-020-04315-y
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Francis B, David B (eds) Proceedings of the 32nd international conference on machine learning. Proceedings of machine learning research, PMLR, vol 37, pp 448–456
Jozinović D, Lomax A, Štajduhar I, Michelini A (2020) Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network. Geophys J Int 222(2):1379–1389. https://doi.org/10.1093/gji/ggaa233
Kafaei Mohammadnejad A, Mousavi SM, Torabi M, Mousavi M, Alavi AH (2012) Robust attenuation relations for peak time-domain parameters of strong ground motions. Environ Earth Sci 67:53–70. https://doi.org/10.1007/s12665-011-1479-9
Kriegerowski M, Petersen GM, Vasyura-Bathke H, Ohrnberger M (2019) A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms. Seismol Res Lett 90(2A):510–516. https://doi.org/10.1785/0220180320
Kuang W, Yuan C, Zhang J (2021) Network-based earthquake magnitude determination via deep learning. Seismol Res Lett 92(4):2245–2254. https://doi.org/10.1785/0220200317
Lin JT, Melgar D, Thomas A, Searcy J (2021) Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning. J Geophys Res Solid Earth 126(10):e2021JB022703. https://doi.org/10.1029/2021JB022703
Liu X, Ren T, Chen H, Chen Y (2021) Classification of tectonic and non-tectonic seismicity based on convolutional neural network. Geophys J Int 224(1):191–198. https://doi.org/10.1093/gji/ggaa444
Lomax A, Michelini A, Jozinović D (2019) An investigation of rapid earthquake characterization using single-station waveforms and a convolutional neural network. Seismol Res Lett 90(2A):517–529. https://doi.org/10.1785/0220180311
Luco N, Cornell CA (2007) Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthq Spectra 23(2):357–392. https://doi.org/10.1193/1.2723158
Ma J, Zhao Z, Yi X, Chen J, Hong L, Chi Ed H (2018) Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1930–1939. https://doi.org/10.1145/3219819.3220007
Mangalathu S, Burton HV (2019) Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions. Int J Disaster Risk Reduct 36:101111. https://doi.org/10.1016/j.ijdrr.2019.101111
Mousavi SM, Sheng Y, Zhu W, Beroza GC (2019) STanford EArthquake Dataset (STEAD): a global data set of seismic signals for AI. IEEE Access 7:179464–179476. https://doi.org/10.1109/ACCESS.2019.2947848
Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun 11(1):3952. https://doi.org/10.1038/s41467-020-17591-w
Münchmeyer J, Bindi D, Leser U, Tilmann F (2021) The transformer earthquake alerting model: a new versatile approach to earthquake early warning. Geophys J Int 225(1):646–656. https://doi.org/10.1093/gji/ggaa609
Otake R, Kurima J, Goto H, Sawada S (2020) Deep learning model for spatial interpolation of real-time seismic intensity. Seismol Soc Am 91(6):3433–3443. https://doi.org/10.1785/0220200006
Saad OM, Hafez AG, Soliman MS (2020) Deep learning approach for earthquake parameters classification in earthquake early warning system. IEEE Geosci Remote Sens Lett 18(7):1293–1297. https://doi.org/10.1109/LGRS.2020.2998580
Shen H, Shen Y (2021) Array-based convolutional neural networks for automatic detection and 4D localization of earthquakes in Hawai ‘i. Seismol Res Lett 92(5):2961–2971. https://doi.org/10.1785/0220200419
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30
Wang B, Zhang N, Lu W, Wang J (2019) Deep-learning-based seismic data interpolation: a preliminary result. Geophysics 84(1):V11–V20. https://doi.org/10.1190/geo2017-0495.1
Wibowo A, Pratama C, Sahara DP, Heliani L, Rasyid S, Akbar Z, Muttaqy F, Sudrajat A (2021) Earthquake early warning system using ncheck and hard-shared orthogonal multitarget regression on deep learning. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2021.3066346
Xiao Z, Wang J, Liu C, Li J, Zhao L, Yao Z (2021) Siamese earthquake transformer: a pair-input deep-learning model for earthquake detection and phase picking on a seismic array. J Geophys Res Solid Earth 126(5):e2020JB021444. https://doi.org/10.1029/2020JB021444
Zhang G, Wang Z, Chen Y (2018) Deep learning for seismic lithology prediction. Geophys J Int 215(2):1368–1387. https://doi.org/10.1093/gji/ggy344
Zhang X, Zhang M, Tian X (2021) Real-time earthquake early warning with deep learning: application to the 2016 M 60 Central Apennines, Italy earthquake. Geophys Res Lett 48(5):2020GL089394. https://doi.org/10.1029/2020GL089394
Zhu W, Beroza GC (2019) PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophys J Int 216(1):261–273. https://doi.org/10.1093/gji/ggy423
Zhu W, Mousavi SM, Beroza GC (2020) Seismic signal augmentation to improve generalization of deep neural networks. Adv Geophys 61:151–177. https://doi.org/10.1016/bs.agph.2020.07.003
Acknowledgements
This work is partially supported by National Natural Science Foundation of China (62276058, 61902057, and 41774063), Fundamental Research Funds for the Central Universities (N2217003), and Joint Fund of Science and Technology Department of Liaoning Province and State Key Laboratory of Robotics, China (2020-KF-12-11).
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
Fanchun Meng helped in formal analysis, methodology, software, validation, visualization, and writing—original draft. Tao Ren helped in conceptualization, funding acquisition, resources, supervision, writing—review, and editing. Enming Guo helped in methodology. Hongfeng Chen helped in data curation and resources. Xinliang Liu helped in methodology and supervision. Haodong Zhang contributed to writing—review and editing. Jiang Li helped in methodology and editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare no conflict of interest related to this work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Meng, F., Ren, T., Guo, E. et al. Estimation of ground motion parameters via multi-task deep neural networks. Nat Hazards 120, 6737–6754 (2024). https://doi.org/10.1007/s11069-024-06464-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-024-06464-w