Efficient non-ergodic ground-motion prediction for large datasets

An efficient numerical method for non-ergodic ground-motion inference and prediction is proposed that alleviates the large computational and memory requirements associated with the traditional approach based on Gaussian Processes described in Landwehr et al. (Bull Seismol Soc Am, https://doi.org/10.1785/0120160118, 2016). The method uses the latest developments in Gaussian Processes and Machine Learning from Wilson and Nickisch (in: International Conference on Machine Learning, pp 1775-1784, 2015) (SKI) and Gardner et al. (in: International Conference on Artificial Intelligence and Statistics, pp. 1407-1416, 2018) (SKIP) and uses sparse approximations combined with efficient matrix decompositions to accurately approximate the large covariance matrices involved in the calculations. This efficient method can be used for both inference of hyperparameters of the non-ergodic ground-motion models and for forward predictions of non-ergodic median ground-motion. The application to predictions are presented. For large datasets of 100,000 to 1,000,000 ground motion values, the proposed method increases the computation speed by factors of 100 to 1000, reducing run times from days to minutes. In addition, the memory requirements are reduced from hundreds of GB to a few GB only, which makes the development of non-ergodic ground-motion models practical using traditional desktop computers.