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- W2890923745 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Noise attenuation for seismic image using a deep-residual learningAuthors: Yushu ZhangHongbo LinYue LiYushu ZhangJilin UniversitySearch for more papers by this author, Hongbo LinJilin UniversitySearch for more papers by this author, and Yue LiJilin UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2997974.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRandom noise elimination acts as an significant role in the seismic signal processing. In this paper, we are investigating the use of a Convolutional Neural Network (CNN) for noise removal of seismic image. We use a residual learning framework to train the neural network and enlarge the receptive filed of the network by introducing dilated convolution layer, without increasing the number of parameters and computational burden. Besides, the pool layer is also added to speed up the training processing and prevent the overfitting. The experimental results show that the proposed method can not only effectively attenuate the random noise of the seismic image, but also preserve the detail information of the texture structures. It is demonstrated that the convolutional neural network based the residual learning can provide an appropriate method for the seismic image denoising.Presentation Date: Tuesday, October 16, 2018Start Time: 9:20:00 AMLocation: Poster Station 3Presentation Type: PosterKeywords: signal processing, machine learning, neural networksPermalink: https://doi.org/10.1190/segam2018-2997974.1FiguresReferencesRelatedDetailsCited byIntelligent AVA Inversion Using a Convolution Neural Network Trained with Pseudo-Well Datasets30 January 2023 | Surveys in Geophysics, Vol. 86Continuous denoising level adjustment of seismic data through filter modificationYuxing Zhao, Yue Li, Shaoping Lu, Xintong Dong, and Ning Wu2 June 2022 | GEOPHYSICS, Vol. 87, No. 4A cyclic learning approach for improving pre-stack seismic processing21 April 2021 | Scientific Reports, Vol. 11, No. 1Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network3 October 2021 | Minerals, Vol. 11, No. 10Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural NetworkIEEE Access, Vol. 7 SEG Technical Program Expanded Abstracts 2018ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2018 Pages: 5520 publication data© 2018 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 27 Aug 2018 CITATION INFORMATION Yushu Zhang, Hongbo Lin, and Yue Li, (2018), Noise attenuation for seismic image using a deep-residual learning, SEG Technical Program Expanded Abstracts : 2176-2180. https://doi.org/10.1190/segam2018-2997974.1 Plain-Language Summary Keywordssignal processingmachine learningneural networksPDF DownloadLoading ..." @default.
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