Site-Specific Ground Motion Generative Model for Crustal Earthquakes in Japan Based on Generative Adversarial Networks

TL;DR

This paper introduces a novel GAN-based site-specific ground motion model for crustal earthquakes in Japan, capable of generating realistic acceleration time histories conditioned on earthquake and site parameters.

Contribution

It presents a new neural network architecture based on styleGAN2 that models ground motions considering source, path, and detailed site conditions, improving upon empirical models.

Findings

Generated ground motions match empirical models in magnitude and distance scaling.

The model accounts for shear-wave velocity profiles beyond traditional $V_{S30}$.

It effectively captures site-specific effects on ground motion characteristics.

Abstract

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 $V_{\mathrm{S}30}$.