IGGCAS OpenIR  > 油气资源研究院重点实验室
Prediction of shear-wave velocity using receiver functions based on the deep learning method
Yang TingWei1; Cao DanPing1; Du NanQiao2,3,4; Cui RongAng1; Nan FangZhou2,4; Xu Ya2,3,4; Liang Ce1
2022
Source PublicationCHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION
ISSN0001-5733
Volume65Issue:1Pages:214-226
AbstractThe 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.
KeywordReceiver function Deep learning Convolutional neural network Velocity structure
DOI10.6038/cjg2022P0025
WOS KeywordINVERSION ; ALGORITHM
Language英语
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000776136800017
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/105117
Collection油气资源研究院重点实验室
Corresponding AuthorCao DanPing
Affiliation1.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
Recommended Citation
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.
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