Deep Learning Peak Ground Acceleration Prediction Using Single-Station Waveforms
Omar M. Saad, Islam Helmy, Mona S. Mohammed, Alexandros Savvaidis, Avigyan Chatterjee, Yangkang Chen
Abstract
Predicting the peak ground acceleration from the first few seconds after the P-wave arrival time is crucial in estimating the ground motion intensity of the earthquake. The early estimation of peak ground acceleration supports the earthquake early warning system to generate the warning. Here, we propose to use the vision transformer to predict the peak ground acceleration using 4-sec three-channel single-station seismograms, i.e., 1s prior to the P-wave arrival and 3s subsequent to the arrival. The vision transformer can significantly extract remarkable information from the data resulting in superior prediction performance. The core layer of the vision transformer is the multi-head attention network which highlights the significant features of the input data. We train and evaluate the proposed algorithm using the Italian earthquake waveform data, where the proposed algorithm shows a promising result. The proposed vision transformer network utilizes an augmentation strategy to improve the learning ability of the model. Our proposed method is compared to the benchmark deep learning methods and empirical ground-motion models and outperforms all of them. The proposed algorithm can also predict the peak ground acceleration accurately using only 2-sec data after the P-wave arrival time. The proposed vision transformer architecture can also be integrated into a peak ground acceleration classification framework. Finally, the proposed algorithm is tested using real-time data and shows accurate results, indicating its applicability in real-time monitoring.