Ground-Shaking Intensity Prediction for Onsite Earthquake Early Warning Using Deep Learning

Abstract

Abstract Damage caused by an earthquake is highly correlated with the shaking intensity in an area. Therefore, it is beneficial to know the shaking intensity of a certain area in advance so that proper preparations and responses can be made. This article proposes a deep learning approach to predict the onsite intensity of ground shaking for a certain location at the very beginning of an earthquake, namely ground-shaking intensity prediction (GSIP). GSIP learns the features hidden in the acceleration waveform and frequency spectrum during a short initial window after the P-wave arrival. It then predicts the intensity level of the ground shaking based on the extracted features. Traditional methods determine parameters or thresholds by experience, but it is very difficult to select appropriate thresholds, and this may need careful calibration. In contrast, GSIP determines the appropriate parameters from the waveform to predict intensity levels without the need for calibration. The waveforms used for model training and validation were collected from 1991 to 2020 data from Taiwan, 2004 to 2020 data from Japan, and 2005 to 2020 data from Italy. The results show that GSIP can achieve more than 85% accuracy at predicting the intensity level, with a tolerance of one level of intensity, as well as high accuracy and recall. Recent events that occurred in Taiwan in 2021 and 2022 were used to evaluate GSIP, and the results confirm its ability to accurately predict intensity levels in different areas. In addition, GSIP is undergoing testing on the earthquake monitoring system in the Central Weather Bureau of Taiwan and has effectively provided real-time early warning for earthquakes.