Rapid prediction of ground shaking intensity with Graph Neural Networks

Rapid accurate prediction of strong ground shaking can be crucial for earthquake early warning. Recently, machine learning (ML), with its advances in Deep Learning (DL), has shown great potential in analysing seismic waveforms. More specifically, when using the data acquired by a seismic network, the incorporation of additional information consisting of the network station positioning into the DL model has been found beneficial to improve the accuracy of the ground motion predictions (Jozinović et al., 2022). Such spatial information can be exploited thoroughly by adopting graph structures, along with the seismic waveforms. Recent advances in adapting DL to graphs have shown promising potential in various graph- related tasks. However, these methods have not been completely adapted for seismological tasks. In this work, we advance an architecture capable of processing a set of seismic time series acquired by a network of stations using the benefits of Graph Neural Networks (GNNs) (see Fig. 1). The objective of the study is the rapid determination of the ground motion (PGA, PGV, and SA 0.3s, 1s and 3s) at farther stations that have not been yet reached by the strong ground shaking by availing of the first signals recorded at the stations close to the epicentre. The work builds upon the GNN approach proposed in Bloemheuvel et al. (2022) and incorporates transfer learning, see Jozinović et al. (2022). We apply the methodology to two datasets having very different source-receiver geometries sited in central Italy (CI, Jozinović et al., 2020, Jozinović et al., 2022) and in north-western central Italy (CW), respectively (Fig. 2). The two datasets have already been the object of similar studies using convolutional neural networks which serve as baselines for comparison. We find that the GNNs are highly suited for the analysis of seismic data from a set of stations and show improvement when compared to the previous work (Bloemheuvel et al., 2022 and Jozinović et al., 2022). We exemplify the early warning capabilities of the proposed approach.

Interreg North-West Europe program (Interreg NWE), project Di-Plast - Digital Circular Economy for the Plastics Industry (NWE729), and partially funded by the INGV project Pianeta Dinamico 2021 Tema 8 SOME (grant no. CUP D53J1900017001) funded by the Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018” and by the European Union’s Horizon 2020 research and innovation program (grant no. 821115), Real-time Earthquake Risk Reduction for a Resilient Europe (RISE).

Bloemheuvel, Stefan, Jurgen van den Hoogen, Dario Jozinović, Alberto Michelini, and Martin Atzmueller. “Multivariate Time Series Regression with Graph Neural Networks.” ArXiv:2201.00818 [Cs], January 3, 2022. http://arxiv.org/abs/2201.00818. Bozinovski, S., 2020. Reminder of the first paper on transfer learning in neural networks, 1976, Informatica, 44(3), 291–302 Jozinović, Dario, Anthony Lomax, Ivan Štajduhar, and Alberto Michelini. “Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network.” Geophysical Journal International 222, no. 2 (August 1, 2020): 1379–89. https://doi.org/10.1093/gji/ggaa233. ———. “Transfer Learning: Improving Neural Network Based Prediction of Earthquake Ground Shaking for an Area with Insufficient Training Data.” Geophysical Journal International 229, no. 1 (April 1, 2022): 704–18. https://doi.org/10.1093/gji/ggab488.
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