EQGraphNet:Advancing single-station earthquake magnitude estimation via deep graph networks with residual connections  

作　　者：Zhiguo Wang Ziwei Chen Huai Zhang 

机构地区：[1]School of Mathematics and Statistics,Xi’an Jiaotong University,Xi’an,710049,Shaanxi,PR China [2]School of Information and Communications Engineering,Xi’an Jiaotong University,Xi’an,710049,Shaanxi,PR China [3]College of Earth and Planetary Sciences,University of Chinese Academy of Sciences.,Beijing,100049,Beijing,PR China

出　　处：《Artificial Intelligence in Geosciences》2024年第1期257-268,共12页地学人工智能（英文）

基　　金：supported by the National Natural Science Foundation of China under Grant 41974137.

摘　　要：Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules,where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To evaluate the robustness,we add natural background noise to the model input and find that EQGraphNet achieves the best results,particularly for signals with lower signal-to-noise ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,by demonstrating the effectiveness of deeper architectures,this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

关 键 词：Magnitude estimation Graph neural network Symmetric graph Single-station seismic signals 

分 类 号：P31[天文地球—固体地球物理学]