Ground-motion simulations using two-dimensional convolution condition adversarial neural network(2D-cGAN)

Abstract

In this paper, an integrated framework based on conditional adversarial neural network (cGAN) is established to simulate ground motions for earthquake scenarios with different magnitude, distance, and local site conditions. A ground motion dataset is created for training the network, within which most data are observed accelerograms collected from PEER and gaps for certain magnitude-site conditions are filled with accelerograms simulated using stochastic finite-fault modeling. During the learning of cGAN, the dataset is classified first with multi-labels according to local site conditions, and the multi-labels are set to be the conditional information Y for the network. Effects of using one-dimensional convolution (Conv1D) and two-dimensional convolution (Conv2D) in cGAN are tested in terms of the time-domain and frequency-domain characteristics of the generated ground motion. The test proves the strengths of Conv2D in capturing high-dimensional temporal and spectral characteristics from one-dimensional time series of ground motion. Ground motions generated using 2D-cGAN network are compared to the measures predicted by widely-used ground motion prediction equations, through which the reliability of 2D-cGAN network on ground motion simulation is verified.