Low-frequency generation and denoising with recursive convolutional neural networks

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Low-frequency generation and denoising with recursive convolutional neural networksAuthors: Gabriel Fabien-OuelletGabriel Fabien-OuelletPolytechnique MontrealSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428270.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe absence of low frequencies in seismic data is a major roadblock to the inversion of low wavenumbers with full waveform inversion. We propose to use a recurrent convolutional neural network to denoise existing low frequencies and generate artificial ones from high frequency seismic data. The neural network iteratively uses the same set of filters to halve the central frequency of a gather at each iteration. Under the presence of white noise, the network can lower the frequency content of a seismic gather up to 64 times. The method performs well on real marine data and generates denoised gathers with central frequencies from 40 to 0.64 Hz. The method is readily applicable to hierarchical full waveform inversion, allowing the use of very crude starting models without being affected by cycle skipping.Presentation Date: Wednesday, October 14, 2020Session Start Time: 9:20 AMPresentation Time: 11:00 AMLocation: Poster Station 3Presentation Type: PosterKeywords: machine learning, low frequency, full-waveform inversion, filtering, data reconstructionPermalink: https://doi.org/10.1190/segam2020-3428270.1FiguresReferencesRelatedDetailsCited byHierarchical transfer learning for deep learning velocity model buildingJérome Simon, Gabriel Fabien-Ouellet, Erwan Gloaguen, and Ishan Khurjekar5 January 2023 | GEOPHYSICS, Vol. 88, No. 1Extrapolated surface-wave dispersion inversionHongyu Sun and Laurent Demanet15 August 2022Efficient Progressive Transfer Learning for Full-Waveform Inversion With Extrapolated Low-Frequency Reflection Seismic DataIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Dual-band generative learning for low-frequency extrapolation in seismic land dataOleg Ovcharenko, Vladimir Kazei, Daniel Peter, Ilya Silvestrov, Andrey Bakulin, and Tariq Alkhalifah1 September 2021 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Gabriel Fabien-Ouellet, (2020), "Low-frequency generation and denoising with recursive convolutional neural networks," SEG Technical Program Expanded Abstracts : 870-874. https://doi.org/10.1190/segam2020-3428270.1 Plain-Language Summary Keywordsmachine learninglow frequencyfull-waveform inversionfilteringdata reconstructionPDF DownloadLoading ...