MagInfoNet: Magnitude Estimation Using Seismic Information Augmentation and Graph Transformer

Abstract

Abstract In this study, we propose a reliable data‐driven tool, MagInfoNet, to enhance the accuracy of magnitude estimation. Its architecture was assembled using the Pre‐Inform and Mag‐Pred modules to replace and update the key functions of traditional seismic analysis workflows. The Pre‐Inform module with the residual network was used for data pretreatment by combining the intrinsic characteristics of seismic signals with the potential features of the arrival and travel times. Meanwhile, using a graph transformer with an improved cyclic graph, the Mag‐Pred module was used to calculate magnitudes by the preprocessed information and the autocorrelation of seismic time series. Training and testing data were randomly selected from the Stanford Earthquake Data Set. The results show that the estimation accuracy, generalization, and robustness of the proposed MagInfoNet are better than those of three machine learning models. Besides, MagInfoNet can perform better for those samples with larger epicentral distances, enhancing the monitoring capacity of existing system for earthquake events in remote areas. Finally, we discuss the interpretability of the explainable MagInfoNet to verify the role of advanced neural network modules.

Keywords

• magnitude estimation
• seismic signal
• arrival time
• travel time
• residual neural network
• graph neural network