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Prediction of shear-wave velocity using receiver functions based on the deep learning method

文献类型：期刊论文

作者Yang TingWei1; Cao DanPing1; Du NanQiao2,3,4; Cui RongAng1; Nan FangZhou2,4; Xu Ya2,3,4; Liang Ce1
刊名CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION
出版日期2022
卷号65期号:1页码:214-226
关键词Receiver function Deep learning Convolutional neural network Velocity structure
ISSN号0001-5733
DOI10.6038/cjg2022P0025
英文摘要The teleseismic receiver function contains a lot of information on converted P-s waves and multiple reflections generated by velocity discontinuities below stations, which is widely used to invert fine crustal and upper mantle velocity structures. Due to the complexity of crustal structures, however, such as the existence of sedimentary or high-velocity layers, the arrival time and amplitude of converted and multiple waves change a lot, resulting in strong non-uniqueness of receiver function inversion. The convolutional neural network, as an efficient feature extraction method, can build the relationship between the receiver function and the shear wave velocity. Therefore, a convolutional neural network is designed to predict the shear wave velocity, whose sample set is built from global model data and high-quality observation receiver function dataset. Tests on dataset shows that the shear wave velocity predicted by the synthetic data is in good agreement with corresponding models. The predicted shear wave velocity from observed data is largely consistent with the global inversion results, and the prediction of the shear wave velocity discontinuities is in accordance with the traditional H-kappa stacking results. Using this method, we image the fine shear wave velocity and crustal structure by inversion of data from an OBS array deployed in the Ryukyu Trench. Test experiments and applications both show this method is not only of high computational efficiency but also of high reliability.
WOS关键词INVERSION ; ALGORITHM
WOS研究方向Geochemistry & Geophysics
语种英语
WOS记录号WOS:000776136800017
出版者SCIENCE PRESS
源URL[http://ir.iggcas.ac.cn/handle/132A11/105117]  
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Cao DanPing
作者单位1.China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Yang TingWei,Cao DanPing,Du NanQiao,et al. Prediction of shear-wave velocity using receiver functions based on the deep learning method[J]. CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION,2022,65(1):214-226.
APA Yang TingWei.,Cao DanPing.,Du NanQiao.,Cui RongAng.,Nan FangZhou.,...&Liang Ce.(2022).Prediction of shear-wave velocity using receiver functions based on the deep learning method.CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION,65(1),214-226.
MLA Yang TingWei,et al."Prediction of shear-wave velocity using receiver functions based on the deep learning method".CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION 65.1(2022):214-226.

入库方式： OAI收割

来源：地质与地球物理研究所

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