Cumulative absolute velocity prediction for earthquake early warning with deep learning

Abstract

AbstractA rapid and accurate estimate of earthquake damage is a key component in a successful earthquake early warning (EEW) system. The cumulative absolute velocity (CAV) is an important and widely used parameter to measure ground motion intensity, but it cannot be correctly estimated via the traditional approach with the limited information available in typical EEW systems. Therefore, current EEW systems cannot effectively use CAV to predict earthquake damage. Herein, a CAV prediction model (DLcav) based on convolutional neural networks was proposed for EEW systems. DLcav is an end‐to‐end solution to continuously predict CAV using arriving seismic waves of increasing length and supplemented with additional auxiliary information. The effectiveness of DLcav to predict CAV was tested based on Japanese ground motion records, and the generalization ability of DLcav was assessed using the ground motion records from Chile. The results demonstrate that DLcav can rapidly predict CAV with good accuracy, which will help better estimate earthquake damage in EEW systems.