Data-Knowledge Driven Hybrid Deep Learning for Earthquake Early Warning

Abstract

Earthquake early warning (EEW) is of great significance in mitigating seismic disasters. Traditional EEW algorithms, which are knowledge-driven approaches, rely on seismologists' analysis. The limited intensity measures were extracted by seismologists from P-wave signals. And there is considerable uncertainty for predicting epicentral distance, magnitude, peak ground acceleration (PGA), and peak ground velocity (PGV). Currently, data-driven deep learning methods with the strong learning abilities do not consider knowledge information from seismologists in EEW; thus, there is unexplored potential in enhancing the performance of deep learning models for EEW. Here, we construct the Data-knowledge driven Hybrid deep Learning network (DHLnet) for EEW using the waveform input, knowledge embedding, convolutional neural network and graph convolutional network, aiming to integrate knowledge information from knowledge-driven methods and the strong learning ability of data-driven deep learning methods, that is, improving the performance of EEW. For the same test data set, compared with knowledge-driven methods and data-driven deep learning models, we demonstrate that DHLnet enhances the timeliness and robustness in predicting the epicentral distance, magnitude, PGA, and PGV during 10 s time window following the arrival of P-wave. Furthermore, to validate the generalization and robustness of the DHLnet in EEW, we applied the trained DHLnet to an independent data set, within first few seconds after an earthquake occurs, DHLnet can provide robust magnitude estimation, epicentral distance estimation and high alarm accuracy. The potential of the proposed network is to enhance the performance of EEW systems and provides new insights into the exploration of deep learning methods for EEW domain.