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- W2891877332 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Seismic data denoising by deep-residual networksAuthors: Yuchen JinXuqing WuJiefu ChenZhu HanWenyi HuYuchen JinUniversity of HoustonSearch for more papers by this author, Xuqing WuUniversity of HoustonSearch for more papers by this author, Jiefu ChenUniversity of HoustonSearch for more papers by this author, Zhu HanUniversity of HoustonSearch for more papers by this author, and Wenyi HuAdvanced Geophysical Technology, Inc.Search for more papers by this authorhttps://doi.org/10.1190/segam2018-2998619.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Deep learning leverages multi-layer neural networks architecture and demonstrates superb power in many machine learning applications. The deep denoising autoencoder technique extracts better coherent features from the seismic data. The technique allows us to automatically extract low-dimensional features from high dimensional feature space in a non-linear, data-driven, and unsupervised way. A properly trained denoising autoencoder takes a partially corrupted input and recovers the original undistorted input. In this paper, a novel autoencoder built upon the deep residual network is proposed to perform noise attenuation on the seismic data. We evaluate the proposed method with synthetic datasets and the result confirms the effective denoising performance of the proposed approach. Presentation Date: Tuesday, October 16, 2018 Start Time: 1:50:00 PM Location: 211A (Anaheim Convention Center) Presentation Type: Oral Keywords: machine learning, attenuation, signal processingPermalink: https://doi.org/10.1190/segam2018-2998619.1FiguresReferencesRelatedDetailsCited byIntelligent AVA Inversion Using a Convolution Neural Network Trained with Pseudo-Well Datasets30 January 2023 | Surveys in Geophysics, Vol. 86Deep unfolding dictionary learning for seismic denoisingYuhan Sui, Xiaojing Wang, and Jianwei Ma1 December 2022 | GEOPHYSICS, Vol. 88, No. 1Deep Velocity Generator: A Plug-In Network for FWI EnhancementIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Random Noise Attenuation by Self-supervised Learning from Single Seismic Data11 November 2022 | Mathematical Geosciences, Vol. 79BSnet: An Unsupervised Blind Spot Network for Seismic Data Random Noise AttenuationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks7 July 2021 | Frontiers in Earth Science, Vol. 9Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach16 May 2021 | Geophysical Prospecting, Vol. 69, No. 5Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric DataIEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 1Attenuation of seismic swell noise using convolutional neural networks in frequency domain and transfer learningJiachun You, Yajuan Xue, Junxing Cao, and Canping Li12 October 2020 | Interpretation, Vol. 8, No. 4A physics-augmented deep learning method for seismic data deblendingShirui Wang, Wenyi Hu, Yanyan Hu, Xuqing Wu, and Jiefu Chen30 September 2020A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic dataDario Grana, Leonardo Azevedo, and Mingliang Liu16 January 2020 | GEOPHYSICS, Vol. 85, No. 4Style transfer for generation of realistically textured subsurface modelsOleg Ovcharenko, Vladimir Kazei, Daniel Peter, and Tariq Alkhalifah10 August 2019Applying machine learning to 3D seismic image denoising and enhancementEnning Wang and Jeff Nealon7 June 2019 | Interpretation, Vol. 7, No. 3 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 Yuchen Jin, Xuqing Wu, Jiefu Chen, Zhu Han, and Wenyi Hu, (2018), Seismic data denoising by deep-residual networks, SEG Technical Program Expanded Abstracts : 4593-4597. https://doi.org/10.1190/segam2018-2998619.1 Plain-Language Summary Keywordsmachine learningattenuationsignal processingPDF DownloadLoading ..." @default.
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