IGGCAS OpenIR  > 油气资源研究院重点实验室
Extracting Fresnel zones from migrated dip-angle gathers using a convolutional neural network
Cheng, Qian1,2,3; Zhang, Jianfeng4; Liu, Wei5
2020-07-28
Source PublicationEXPLORATION GEOPHYSICS
ISSN0812-3985
Pages10
AbstractFresnel zones are helpful for obtaining a high signal-to-noise ratio (S/N)-migrated result. A migrated dip-angle gather provides a simple domain for estimating 2D Fresnel zones for 3D migration. We develop a deep-learning-based technology to automatically estimate Fresnel zones from migrated dip-angle gathers, thus avoiding the cumbersome task of manually checking and modifying the boundaries of the Fresnel zones. A pair of 1D Fresnel zones are incorporated to represent a 2D Fresnel zone in terms of the inline and crossline dip angles because it is difficult to directly extract 2D Fresnel zones from a 2D dip-angle gather. The proposed convolutional neural network (CNN) is established by modifying VGGNet. As picking boundaries of the Fresnel zones is a regression problem, we remove the last soft-max layer from the VGGNet. The last three convolution layers and a pooling layer are also removed because the feature maps are small enough. To improve the contrast and definition, we enhance the features of the reflected events in the dip-angle gather. Data normalisation is carried out to accelerate the training process using a simple-rescaling method before training the modified VGGNet. Field data examples demonstrate the effectiveness and efficiency of the proposed method.
KeywordImaging migration neural networks noise
DOI10.1080/08123985.2020.1798755
Funding OrganizationNational 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 ; 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
Language英语
Funding ProjectNational Natural Science Foundation of China[41574135] ; National Natural Science Foundation of China[41604120]
Funding OrganizationNational 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 ; 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
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000558123300001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/97632
Collection油气资源研究院重点实验室
Corresponding AuthorZhang, Jianfeng
Affiliation1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Earth Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
4.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
5.China Univ Geosci, Sch Geophys & Informat Technol, Beijing, Peoples R China
First Author AffilicationInstitute of Geology and Geophysics, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Cheng, Qian,Zhang, Jianfeng,Liu, Wei. Extracting Fresnel zones from migrated dip-angle gathers using a convolutional neural network[J]. EXPLORATION GEOPHYSICS,2020:10.
APA Cheng, Qian,Zhang, Jianfeng,&Liu, Wei.(2020).Extracting Fresnel zones from migrated dip-angle gathers using a convolutional neural network.EXPLORATION GEOPHYSICS,10.
MLA Cheng, Qian,et al."Extracting Fresnel zones from migrated dip-angle gathers using a convolutional neural network".EXPLORATION GEOPHYSICS (2020):10.
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