Masked graph neural network for rapid ground motion prediction in Italy

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.