Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation

arXiv:2309.03447v3 Announce Type: replace-cross
Abstract: We present a data-driven framework for ground-motion synthesis that generates three-component acceleration time histories conditioned on moment magnitude, rupture distance , time-average shear-wave velocity at the top $30m$ ($V_{S30}$), 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 previous work. We next train …

abstract adversarial arxiv broadband cs.lg data data-driven development framework generative operators physics.geo-ph shear style synthesis type validation via