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  2. 地质与地球物理研究所
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  4. 岩石圈演化国家重点实验室
Deep learning for quality control of receiver functions

文献类型：期刊论文

作者Gong, Chang2,3; Chen, Ling2,3; Xiao, Zhuowei1,2; Wang, Xu2,3
刊名FRONTIERS IN EARTH SCIENCE
出版日期2022-08-10
卷号10页码:12
关键词receiver function deep learning quality control model comparsion data processing
DOI10.3389/feart.2022.921830
英文摘要Receiver function has been routinely used for studying the discontinuity structure in the crust and upper mantle. The manual quality control of receiver functions, which plays a key role in high-quality data selection and accurate structural imaging, has been challenged by today's booming data volumes. Traditional automatic quality control methods usually require tuning hyperparameters and fail to generalize to low signal-to-noise ratio data. Deep learning has been increasingly used to deal with extensive seismic data. However, it generally requires a manually labeled dataset, and its performance is highly related to the network design. In this study, we develop and compare four different deep learning network designs with manual and traditional quality control methods using 53293 receiver functions from three broadband seismic stations. Our results show that a combination of convolutional and long-short memory layers achieves the best performance of similar to 91% accuracy. We also propose a fully automatic training schema that requires zero manually labeled receiver function yet achieves similar performance to that using carefully labeled ones. Compared with the traditional automatic method, our model retrieves similar to 5 times more reliable receiver functions from relatively small earthquakes with magnitudes between 5.0 and 5.5. The average waveforms and H-kappa stacking results of these receiver functions are comparable to those obtained by manual quality control from earthquakes with magnitudes larger than 5.5, which further demonstrates the validity of our method and indicates its potential for making use of smaller earthquakes in the receiver function analysis.
WOS关键词MOHO DEPTH ; BENEATH
资助项目National Natural Science Foundation of China ; Strategic Priority Research Program (A) of Chinese Academy of Sciences[42288201] ; [XDA20070302]
WOS研究方向Geology
语种英语
WOS记录号WOS:000844087200001
出版者FRONTIERS MEDIA SA
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 ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences ; Strategic Priority Research Program (A) of Chinese Academy of Sciences
源URL[http://ir.iggcas.ac.cn/handle/132A11/108431]  
专题地质与地球物理研究所_岩石圈演化国家重点实验室
地质与地球物理研究所_中国科学院矿产资源研究重点实验室
通讯作者Chen, Ling
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Gong, Chang,Chen, Ling,Xiao, Zhuowei,et al. Deep learning for quality control of receiver functions[J]. FRONTIERS IN EARTH SCIENCE,2022,10:12.
APA Gong, Chang,Chen, Ling,Xiao, Zhuowei,&Wang, Xu.(2022).Deep learning for quality control of receiver functions.FRONTIERS IN EARTH SCIENCE,10,12.
MLA Gong, Chang,et al."Deep learning for quality control of receiver functions".FRONTIERS IN EARTH SCIENCE 10(2022):12.

入库方式： OAI收割

来源：地质与地球物理研究所

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