Masked graph neural network for rapid ground motion prediction in Italy

Authors

DOI:

https://doi.org/10.26443/seismica.v4i2.1655

Keywords:

Machine learning, Seismology, Earthquake early warning, ground motions

Abstract

This study presents an updated version of TISER-GCN, a graph neural network (GCN) designed to predict maximum intensity measurements (IMs) from 10-second seismic waveforms starting at the earthquake origin time, without prior knowledge of location, distance, and magnitude. The improved model was applied to nearly 600 seismic stations from the INSTANCE benchmark dataset, significantly expanding the original TISER-GCN setup, which was limited to 39 stations in a smaller area of central Italy. Input data consist of three-component waveforms selected to ensure high quality and minimize saturation. Results show that masking stations where the P-wave arrives within the first 10 seconds , combined with the integration of additional information, reduces the mean squared error (MSE) by up to 6% for peak ground acceleration (PGA) and 5.5% for peak ground velocity (PGV), compared to the unmasked baseline. Moreover, the proposed approach yields near-zero median residuals across all IMs, mitigating the systematic underestimation observed when using a ground motion model specifically developed for Italy. These findings indicate that the model provides accurate predictions of ground motions, comparable to those obtained with the original TISER-GCN, which, however, requires a fixed seismic network geometry.

References

Atik, L. A., Abrahamson, N., Bommer, J. J., Scherbaum, F., Cotton, F., & Kuehn, N. (2010). The variability of ground-motion prediction models and its components. Seismological Research Letters, 81(5), 794–801. https://doi.org/10.1785/gssrl.81.5.794

Bindi, D., Pacor, F., Luzi, L., Puglia, R., Massa, M., Ameri, G., & Paolucci, R. (2011). Ground motion prediction equations derived from the Italian strong motion database. Bulletin of Earthquake Engineering, 9, 1899–1920. https://doi.org/10.1007/s10518-011-9313-z

Bloemheuvel, S., van den Hoogen, J., Jozinović, D., Michelini, A., & Atzmueller, M. (2023). Graph neural networks for multivariate time series regression with application to seismic data. International Journal of Data Science and Analytics, 16(3), 317–332. https://doi.org/10.1007/s41060-022-00349-6

Böse, M., Heaton, T., & Hauksson, E. (2012). Rapid estimation of earthquake source and ground-motion parameters for earthquake early warning using data from a single three-component broadband or strong-motion sensor. Bulletin of the Seismological Society of America, 102(2), 738–750. https://doi.org/10.1785/0120110152

Danecek, P., Pintore, S., Mazza, S., Mandiello, A., Fares, M., Carluccio, I., Della Bina, E., Franceschi, D., Moretti, M., Lauciani, V., & others. (2021). The Italian node of the European integrated data archive. Seismological Research Letters, 92(3), 1726–1737. https://doi.org/10.1785/0220200409

Derras, B., Bard, P. Y., & Cotton, F. (2014). Towards fully data driven ground-motion prediction models for Europe. Bulletin of Earthquake Engineering, 12(1), 495–516. https://doi.org/10.1007/s10518-013-9481-0

Fornasari, S. F., Pazzi, V., & Costa, G. (2022). A machine-learning approach for the reconstruction of ground-shaking fields in real time. Bulletin of the Seismological Society of America, 112(5), 2642–2652. https://doi.org/10.1785/0120220034

Gandomi, A. H., Alavi, A. H., Mousavi, M., & Tabatabaei, S. M. (2011). A hybrid computational approach to derive new ground-motion prediction equations. Engineering Applications of Artificial Intelligence, 24(4), 717–732. https://doi.org/10.1016/j.engappai.2011.01.005

Hsu, T.-Y., Huang, S.-K., Chang, Y.-W., Kuo, C.-H., Lin, C.-M., Chang, T.-M., Wen, K.-L., & Loh, C.-H. (2013). Rapid on-site peak ground acceleration estimation based on support vector regression and P-wave features in Taiwan. Soil Dynamics and Earthquake Engineering, 49, 210–217. https://doi.org/10.1016/j.soildyn.2013.03.001

Hu, J., Ding, Y., Zhang, H., Jin, C., & Wang, Z. (2023). A Real-Time Seismic Intensity Prediction Framework Based on Interpretable Ensemble Learning. Seismological Research Letters, 94(3), 1579–1602. https://doi.org/10.1785/0220220167

Iaccarino, A. G., Cristofaro, A., Picozzi, M., Spallarossa, D., & Scafidi, D. (2024). Real-time prediction of distance and PGA from P-wave features using Gradient Boosting Regressor for on-site earthquake early warning applications. Geophysical Journal International, 236(1), 675–687. https://doi.org/10.1093/gji/ggad443

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. Geophysical Journal International, 222(2), 1379–1389. https://doi.org/10.1093/gji/ggaa233

Jozinović, D., Lomax, A., Štajduhar, I., & Michelini, A. (2022). Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data. Geophysical Journal International, 229(1), 704–718. https://doi.org/10.1093/gji/ggab488

Margheriti, L., Nostro, C., Cocina, O., Castellano, M., Moretti, M., Lauciani, V., Quintiliani, M., Bono, A., Mele, F. M., Pintore, S., & others. (2021). Seismic surveillance and earthquake monitoring in Italy. Seismological Research Letters, 92(3), 1659–1671. https://doi.org/10.1785/0220200380

Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., & Lauciani, V. (2021). INSTANCE–the Italian seismic dataset for machine learning. Earth System Science Data, 13(12), 5509–5544. https://doi.org/10.5194/essd-13-5509-2021

Michelini, A., Faenza, L., Lanzano, G., Lauciani, V., Jozinović, D., Puglia, R., & Luzi, L. (2020). The new ShakeMap in Italy: Progress and advances in the last 10 yr. Seismological Research Letters, 91(1), 317–333. https://doi.org/10.1785/0220190130

Michelini, A., Margheriti, L., Cattaneo, M., Cecere, G., D’Anna, G., Delladio, A., Moretti, M., Pintore, S., Amato, A., Basili, A., & others. (2016). The Italian National Seismic Network and the earthquake and tsunami monitoring and surveillance systems. Advances in Geosciences, 43, 31–38. https://doi.org/10.5194/adgeo-43-31-2016

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). The transformer earthquake alerting model: A new versatile approach to earthquake early warning. Geophysical Journal International, 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. Seismological Research Letters, 91(6), 3433–3443. https://doi.org/10.1785/0220200006

Spallarossa, D., Kotha, S. R., Picozzi, M., Barani, S., & Bindi, D. (2019). On-site earthquake early warning: a partially non-ergodic perspective from the site effects point of view. Geophysical Journal International, 216(2), 919–934. https://doi.org/10.1093/gji/ggy470

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer learning. Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, 270–279.

Tang, Z., Qiao, Z., Hong, X., Wang, Y., Dharejo, F. A., Zhou, Y., & Du, Y. (2021). Data augmentation for graph convolutional network on semi-supervised classification. Web and Big Data: 5th International Joint Conference, APWeb-WAIM 2021, Guangzhou, China, August 23–25, 2021, Proceedings, Part II 5, 33–48.

Wang, A., Li, S., Lu, J., Zhang, H., Wang, B., & Xie, Z. (2023). Prediction of PGA in earthquake early warning using a long short-term memory neural network. Geophysical Journal International, 234(1), 12–24. https://doi.org/10.1093/gji/ggad067

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

Yang, L., Wu, F., Wang, Y., Gu, J., & Guo, Y. (2019). Masked Graph Convolutional Network. IJCAI, 4070–4077. https://doi.org/10.24963/ijcai.2019/565

Zhang, H., Melgar, D., Sahakian, V., Searcy, J., & Lin, J.-T. (2022). Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning. Geophysical Journal International, 231(3), 2186–2204. https://doi.org/10.1093/gji/ggac325

Zhu, W., & Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423

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Published

2025-09-16

How to Cite

Trappolini, D., Oliveti, I., Faenza, L., Jozinović, D., & Michelini, A. (2025). Masked graph neural network for rapid ground motion prediction in Italy. Seismica, 4(2). https://doi.org/10.26443/seismica.v4i2.1655

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