A moment magnitude 5.4 earthquake struck the Pohang city located in the southeastern Korean Peninsula on 15 November 2017, possibly triggered by an enhanced geothermal system. Despite the moderate magnitude of the earthquake, extensive geotechnical and structural damage occurred. This study summarizes the widespread geotechnical damage resulted from the Pohang earthquake, observed during our reconnaissance trip. The affected area is mainly covered by Tertiary/Quaternary Alluvium underlain by mudstones with a thickness of approximately 500 m. Because of the soft grounds and shallow focal depth, this area experienced ground settlements up to 39 cm, building settlements that made apartments tilted by 1.6°. We also present observations on ground cracks, lateral spreadings, landslides, retaining wall deformations, and liquefactions. Documenting the damage is significant because (1) those are the first earthquake‐induced damage observed in Korea, and (2) those are caused by the shallow‐depth earthquake possibly induced by an enhanced geothermal system.

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 (⁠ $ML$⁠) greater than 3.0, including the Gyeongju and Pohang earthquakes with $ML$ 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 (⁠ $R2$⁠), 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 (⁠ $ML$⁠, epicentral distance [ $Repi$], average shear‐wave velocity of the upper 30 m [ $VS30$], focal depth, and slope angle) used in this study, $ML$ and $Repi$ are the most influential variables and show strong correlations with PSAs. We apply the GB‐based model to three recent earthquakes larger than $ML$ 3.0, and the model accurately predicts the PSAs at various stations. We also generate maps of estimated PSA (⁠ $PSAest$⁠) values for the four periods (T = 0.01, 0.1, 1, and 3 s) for the scenario earthquake with an $ML$ 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.