MagInfoNet: Magnitude Estimation Using Seismic Information Augmentation and Graph Transformer
文献类型:期刊论文
作者 | Chen, Ziwei1; Wang, Zhiguo1; Wu, Shaojiang2; Wang, Yibo2; Gao, Jinghuai1 |
刊名 | EARTH AND SPACE SCIENCE
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出版日期 | 2022-12-01 |
卷号 | 9期号:12页码:15 |
关键词 | magnitude estimation seismic signal arrival time travel time residual neural network graph neural network |
DOI | 10.1029/2022EA002580 |
英文摘要 | 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. |
WOS关键词 | NEURAL-NETWORKS ; TIME-SERIES ; EARTHQUAKE ; FRAMEWORK |
资助项目 | National Natural Science Foundation of China[41974137] |
WOS研究方向 | Astronomy & Astrophysics ; Geology |
语种 | 英语 |
WOS记录号 | WOS:000924585900001 |
出版者 | AMER GEOPHYSICAL UNION |
资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China |
源URL | [http://ir.iggcas.ac.cn/handle/132A11/106794] ![]() |
专题 | 地质与地球物理研究所_中国科学院油气资源研究重点实验室 |
通讯作者 | Wang, Zhiguo |
作者单位 | 1.Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Peoples R China 2.Chinese Acad Sci, Key Lab Petr Resource Res, Inst Geol & Geophys, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Ziwei,Wang, Zhiguo,Wu, Shaojiang,et al. MagInfoNet: Magnitude Estimation Using Seismic Information Augmentation and Graph Transformer[J]. EARTH AND SPACE SCIENCE,2022,9(12):15. |
APA | Chen, Ziwei,Wang, Zhiguo,Wu, Shaojiang,Wang, Yibo,&Gao, Jinghuai.(2022).MagInfoNet: Magnitude Estimation Using Seismic Information Augmentation and Graph Transformer.EARTH AND SPACE SCIENCE,9(12),15. |
MLA | Chen, Ziwei,et al."MagInfoNet: Magnitude Estimation Using Seismic Information Augmentation and Graph Transformer".EARTH AND SPACE SCIENCE 9.12(2022):15. |
入库方式: OAI收割
来源:地质与地球物理研究所
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