Fundamental study on probabilistic generative modeling of earthquake ground motion time histories using generative adversarial networks

This study proposes a probabilistic model for earthquake ground motion prediction, named ground motion generation model, which can generate ground motion time history data directly. The ground motion generation model is based on a data-driven technique called generative adversarial networks, allowing generation of ground motion time history data without making assumptions about physical or statistical models. A method to quantitatively and qualitatively evaluate the performance of constructed model is also proposed and the ground motion generation model is optimized for high performance from earthquake engineering and deep learning perspectives. Numerical experiments show that our proposed model is probabilistic, approximating the probabilistic distribution of the dataset of observed records and generating realistic ground motion time histories with various characteristics in the time and frequency domains.