Machine-Learning-Based Surface Ground-Motion Prediction Models for South Korea with Low-to-Moderate Seismicity

Abstract

Ground-motion prediction models (GMPMs) have been developed to estimate seismic intensity considering earthquake magnitude, source-to-site distance, site condition, and so on. This study proposes GMPMs to predict 5% damped pseudospectral acceleration (PSA) for 27 periods ranging from 0.01 to 10 s in Korea, based on three machine-learning techniques (i.e., artificial neural network [ANN], random forest [RF], and gradient boosting [GB]). We use 1189 ground motions recorded at 50 surface stations during the 77 earthquakes with a local magnitude (M-L) greater than 3.0, including the Gyeongju and Pohang earthquakes with M-L of 5.8 and 5.4, respectively. We compare the performances of the three machine-learning-based models and the classical regression-based model in terms of the coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), standard deviation of residuals, and between-event and within-event residuals. The GB-based model shows the best performance. In addition, we analyze the working process of the GB-based model using variable importance and partial dependence (PD) plots. Among the five independent variables (M-L, epicentral distance [R-epi], average shear-wave velocity of the upper 30 m [V-s30], focal depth, and slope angle) used in this study, M-L and R-epi are the most influential variables and show strong correlations with PSAs. We apply the GB-based model to three recent earthquakes larger than M-L 3.0, and the model accurately predicts the PSAs at various stations. We also generate maps of estimated PSA (PSA(eSt)) ) values for the four periods (T = 0.01, 0.1, 1, and 3 s) for the scenario earthquake with an M-L of 5.0. We provide a method for training the GB-based model using the Python library, which can enhance the ground-motion prediction not only in Korea but also worldwide, and an executable version of the validated GB-based model.