Development of Synthetic Ground-Motion Records through Generative Adversarial Neural Operators

Realistic strong-motion accelerograms of earthquakes are required for various earthquake engineering tasks, including modeling structural and site response to large and near-source events. In this work, we introduce a data-driven framework for three-component ground motion synthesis intended for engineering applications. Leveraging the increase of ground-motion data from seismic networks and recent advancements in machine learning, we train a generative adversarial neural operator (GANO) to produce realistic three-component acceleration time histories conditioned on moment magnitude (M), rupture distance (Rrup), time-average shear-wave velocity at the top 30 m (Vs30) based on a California dataset compiled from PEER NGA-West2 database, and a public DesignSafe California database. The results show that the framework can efficiently recover the magnitude, distance, and Vs30 scaling of Fourier amplitude and pseudo-spectral accelerations. Through a comprehensive residual analysis using empirical data, we have verified that our model accurately captures both the mean values and aleatory variability of the evaluated ground-motion parameters.