  We develop a site-specific ground-motion model (GMM) for crustal earthquakes
in Japan that can directly model the probability distribution of ground motion
acceleration time histories based on generative adversarial networks (GANs).
The proposed model can generate ground motions conditioned on moment magnitude,
rupture distance, and detailed site conditions defined by the average
shear-wave velocity in the top 5 m, 10 m, and 20 m ($V_{\mathrm{S}5}$,
$V_{\mathrm{S}10}$, $V_{\mathrm{S}20}$) and the depth to shear-wave velocities
of 1.0 km/s and 1.4 km/s ($Z_{1.0}$, $Z_{1.4}$). We construct the neural
networks based on styleGAN2 and introduce a novel neural network architecture
to generate ground motions considering the effect of source, path, and such
detailed site conditions. 5% damped spectral acceleration of ground motions
generated by the proposed GMM is consistent with empirical GMMs in terms of
magnitude and distance scaling. The proposed GMM can also generate ground
motions accounting for the shear-wave velocity profiles of surface soil with
different magnitudes and distances, and represent characteristic that are not
explained solely by $V_{\mathrm{S}30}$.
