We present a data‐driven framework for ground‐motion synthesis that generates three‐component acceleration time histories conditioned on moment magnitude (M), rupture distance (Rrup), time‐average shear‐wave velocity at the top 30 m (VS30), and style of faulting. We use a Generative Adversarial Neural Operator (GANO)—a resolution invariant architecture that guarantees model training independent of the data sampling frequency. We first present the conditional ground‐motion synthesis algorithm (cGM‐GANO) and discuss its advantages compared to the previous work. We next train cGM‐GANO on simulated ground motions generated by the Southern California Earthquake Center Broadband Platform (BBP) and on recorded the Kiban–Kyoshin network (KiK‐net) data, and show that the model can learn the overall magnitude, distance, and VS30 scaling of effective amplitude spectra (EAS) ordinates and pseudospectral accelerations (PSA). Results specifically show that cGM‐GANO produces consistent median scaling with the training data for the corresponding tectonic environments over a wide range of frequencies for scenarios with sufficient data coverage. For the BBP dataset, cGM‐GANO cannot learn the ground‐motion scaling of the stochastic frequency components (f > 1 Hz); for the KiK‐net dataset, the largest misfit is observed at short distances (Rrup<50  km) and for soft‐soil conditions (VS30<200  m/s) due to the scarcity of such data. Except for these conditions, the aleatory variability of EAS and PSA are captured reasonably well. Finally, cGM‐GANO produces similar median scaling to traditional ground‐motion models (GMMs) for frequencies greater than 1 Hz for both PSA and EAS but underestimates the aleatory variability of EAS. Discrepancies in the comparisons between the synthetic ground motions and GMMs are attributed to inconsistencies between the training dataset and the datasets used in GMM development. Our pilot study demonstrates GANO’s potential for efficient synthesis of broadband ground motions.

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