FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2892149712> ?p ?o ?g. }

• W2892149712 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Stacking velocity estimation using recurrent neural networkAuthors: Reetam BiswasAnthony VassiliouRodney StrombergMrinal K. SenReetam BiswasUniversity of Texas at AustinSearch for more papers by this author, Anthony VassiliouGeoEnergy Inc.Search for more papers by this author, Rodney StrombergGeoEnergy Inc.Search for more papers by this author, and Mrinal K. SenUniversity of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2997208.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe describe a new method based on the Machine Learning (ML) technique for normal moveout correction (NMO) and estimation of stacking velocity. A Recurrent Neural Network (RNN) is used to calculate stacking velocity directly from the seismic data. Finally, this velocity is used for NMO correction of the data. We used the Adam optimization algorithm to train the network of neurons to estimate stacking velocity for a batch of seismic gathers. This velocity is then compared with the correct stacking velocity to update the weight. The training method minimizes a cost function defined as the mean squared error between the estimated and the correct velocities. The trained network is then used to estimate stacking velocity for rest of the gathers. Here we illustrate out method on a noisy real data set from Poland. We first trained the network using only 18 percent of gathers and then used the network to calculate stacking velocity for the remaining gathers. We used these stacking velocity to perform Normal moveout correction and finally we stacked to get the post-stack seismic section. We also show comparison between the stacks generated from the two velocitiesPresentation Date: Wednesday, October 17, 2018Start Time: 9:20:00 AMLocation: Poster Station 1Presentation Type: PosterKeywords: neural networks, stacking, velocity analysis, NMO, machine learningPermalink: https://doi.org/10.1190/segam2018-2997208.1FiguresReferencesRelatedDetailsCited byAutomatic velocity picking with restricted weighted k-means clustering using prior information16 January 2023 | Frontiers in Earth Science, Vol. 10Hierarchical transfer learning for deep learning velocity model buildingJérome Simon, Gabriel Fabien-Ouellet, Erwan Gloaguen, and Ishan Khurjekar5 January 2023 | GEOPHYSICS, Vol. 88, No. 1Time-lapse data matching using a recurrent neural network approachAbdullah Alali, Vladimir Kazei, Bingbing Sun, and Tariq Alkhalifah13 July 2022 | GEOPHYSICS, Vol. 87, No. 5An automatic velocity picking method based on object detectionCe Bian, Weifeng Geng, Ping Yang, Pengyuan Sun, Guiren Xue, and Haikun Lin15 August 2022Seismic inversion with dictionary learning using unsupervised machine learningDebajeet Barman and Mrinal K. Sen15 August 2022Elastic isotropic and anisotropic full-waveform inversions using automatic differentiation for gradient calculations in a framework of recurrent neural networksWenlong Wang, George A. McMechan, and Jianwei Ma24 September 2021 | GEOPHYSICS, Vol. 86, No. 6Automatic velocity picking from semblances with a new deep-learning regression strategy: Comparison with a classification approachWenlong Wang, George A. McMechan, Jianwei Ma, and Fei Xie5 February 2021 | GEOPHYSICS, Vol. 86, No. 2Simulating the Behavior of Reservoirs with Convolutional and Recurrent Neural Networks20 May 2020 | SPE Reservoir Evaluation & Engineering, Vol. 23, No. 03Simulating the Behavior of Reservoirs with Convolutional and Recurrent Neural Networks13 January 2020Automatic velocity picking based on deep learningHao Zhang, Peimin Zhu, Yuan Gu, and Xiaozhang Li10 August 2019Anisotropic moveout correction using a Hough transform neural networkJanaki Vamaraju and Mrinal K. Sen10 August 2019Estimation of acoustic impedance from seismic data using temporal convolutional networkAhmad Mustafa, Motaz Alfarraj, and Ghassan AlRegib10 August 2019Prestack and poststack inversion using a physics-guided convolutional neural networkReetam Biswas, Mrinal K. Sen, Vishal Das, and Tapan Mukerji15 July 2019 | Interpretation, Vol. 7, No. 3Semisupervised sequence modeling for elastic impedance inversionMotaz Alfarraj and Ghassan AlRegib5 August 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 Reetam Biswas, Anthony Vassiliou, Rodney Stromberg, and Mrinal K. Sen, (2018), Stacking velocity estimation using recurrent neural network, SEG Technical Program Expanded Abstracts : 2241-2245. https://doi.org/10.1190/segam2018-2997208.1 Plain-Language Summary Keywordsneural networksstackingvelocity analysisNMOmachine learningPDF DownloadLoading ..." @default.
• W2892149712 created "2018-09-27" @default.
• W2892149712 creator A5041533172 @default.
• W2892149712 creator A5045824622 @default.
• W2892149712 creator A5049953220 @default.
• W2892149712 creator A5079645317 @default.
• W2892149712 date "2018-08-27" @default.
• W2892149712 modified "2023-09-30" @default.
• W2892149712 title "Stacking velocity estimation using recurrent neural network" @default.
• W2892149712 cites W2009990396 @default.
• W2892149712 cites W2021974973 @default.
• W2892149712 cites W2024235597 @default.
• W2892149712 cites W2064675550 @default.
• W2892149712 cites W2082380532 @default.
• W2892149712 cites W2091121667 @default.
• W2892149712 cites W2097749632 @default.
• W2892149712 cites W2102589063 @default.
• W2892149712 cites W2106092743 @default.
• W2892149712 cites W2128084896 @default.
• W2892149712 doi "https://doi.org/10.1190/segam2018-2997208.1" @default.
• W2892149712 hasPublicationYear "2018" @default.
• W2892149712 type Work @default.
• W2892149712 sameAs 2892149712 @default.
• W2892149712 citedByCount "21" @default.
• W2892149712 countsByYear W28921497122019 @default.
• W2892149712 countsByYear W28921497122020 @default.
• W2892149712 countsByYear W28921497122021 @default.
• W2892149712 countsByYear W28921497122022 @default.
• W2892149712 countsByYear W28921497122023 @default.
• W2892149712 crossrefType "proceedings-article" @default.
• W2892149712 hasAuthorship W2892149712A5041533172 @default.
• W2892149712 hasAuthorship W2892149712A5045824622 @default.
• W2892149712 hasAuthorship W2892149712A5049953220 @default.
• W2892149712 hasAuthorship W2892149712A5079645317 @default.
• W2892149712 hasConcept C105795698 @default.
• W2892149712 hasConcept C11413529 @default.
• W2892149712 hasConcept C121332964 @default.
• W2892149712 hasConcept C127313418 @default.
• W2892149712 hasConcept C139945424 @default.
• W2892149712 hasConcept C147168706 @default.
• W2892149712 hasConcept C154945302 @default.
• W2892149712 hasConcept C165205528 @default.
• W2892149712 hasConcept C175291020 @default.
• W2892149712 hasConcept C177264268 @default.
• W2892149712 hasConcept C199360897 @default.
• W2892149712 hasConcept C2993808335 @default.
• W2892149712 hasConcept C33347731 @default.
• W2892149712 hasConcept C33923547 @default.
• W2892149712 hasConcept C41008148 @default.
• W2892149712 hasConcept C46141821 @default.
• W2892149712 hasConcept C50644808 @default.
• W2892149712 hasConcept C58489278 @default.
• W2892149712 hasConcept C9984622 @default.
• W2892149712 hasConceptScore W2892149712C105795698 @default.
• W2892149712 hasConceptScore W2892149712C11413529 @default.
• W2892149712 hasConceptScore W2892149712C121332964 @default.
• W2892149712 hasConceptScore W2892149712C127313418 @default.
• W2892149712 hasConceptScore W2892149712C139945424 @default.
• W2892149712 hasConceptScore W2892149712C147168706 @default.
• W2892149712 hasConceptScore W2892149712C154945302 @default.
• W2892149712 hasConceptScore W2892149712C165205528 @default.
• W2892149712 hasConceptScore W2892149712C175291020 @default.
• W2892149712 hasConceptScore W2892149712C177264268 @default.
• W2892149712 hasConceptScore W2892149712C199360897 @default.
• W2892149712 hasConceptScore W2892149712C2993808335 @default.
• W2892149712 hasConceptScore W2892149712C33347731 @default.
• W2892149712 hasConceptScore W2892149712C33923547 @default.
• W2892149712 hasConceptScore W2892149712C41008148 @default.
• W2892149712 hasConceptScore W2892149712C46141821 @default.
• W2892149712 hasConceptScore W2892149712C50644808 @default.
• W2892149712 hasConceptScore W2892149712C58489278 @default.
• W2892149712 hasConceptScore W2892149712C9984622 @default.
• W2892149712 hasLocation W28921497121 @default.
• W2892149712 hasOpenAccess W2892149712 @default.
• W2892149712 hasPrimaryLocation W28921497121 @default.
• W2892149712 hasRelatedWork W1799226889 @default.
• W2892149712 hasRelatedWork W2031315338 @default.
• W2892149712 hasRelatedWork W2054652573 @default.
• W2892149712 hasRelatedWork W2065487098 @default.
• W2892149712 hasRelatedWork W2386387936 @default.
• W2892149712 hasRelatedWork W2789595154 @default.
• W2892149712 hasRelatedWork W2892149712 @default.
• W2892149712 hasRelatedWork W2902723393 @default.
• W2892149712 hasRelatedWork W3212632204 @default.
• W2892149712 hasRelatedWork W4386450788 @default.
• W2892149712 isParatext "false" @default.
• W2892149712 isRetracted "false" @default.
• W2892149712 magId "2892149712" @default.
• W2892149712 workType "article" @default.