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• W2890599198 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Convolutional neural network for seismic impedance inversionAuthors: Vishal DasAhinoam PollackUri WollnerTapan MukerjiVishal DasStanford UniversitySearch for more papers by this author, Ahinoam PollackStanford UniversitySearch for more papers by this author, Uri WollnerStanford UniversitySearch for more papers by this author, and Tapan MukerjiStanford UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2994378.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn this work, we solve the seismic inversion problem of obtaining an elastic model of the subsurface from recorded seismic data using a convolutional neural network (CNN). For simplicity we consider a 1D layered earth model and normal incidence seismic data. We systematically test the robustness of the network in predicting P-impedance (Ip) of new, previously unobserved, earth models when the input to the network consisted of seismograms generated with (1) different source wavelets; (2) earth models that had different geostatistical spatial correlations; and (3) earth models that had different underlying rock physics relation than that in the training data. Results show that the CNN successfully predicts impedances generated with both variograms ranges on which it was trained and variogram ranges on which it was not trained. The CNN was able to predict with medium success samples generated with rock physics model parameters and source wavelet phase outside of the training range. The CNN was not able to predict either the training set or any of the testing sets in the presence of various source wavelet frequencies, showing the importance of knowing a-priori the value of the wavelet frequency when generating the synthetic seismic data. Overall, the CNN has shown great promise in predicting a high frequency impedance model from a low frequency seismic signal, given appropriate training data.Presentation Date: Wednesday, October 17, 2018Start Time: 1:50:00 PMLocation: 204B (Anaheim Convention Center)Presentation Type: OralKeywords: machine learning, reservoir characterization, neural networks, artificial intelligence, seismic impedancePermalink: https://doi.org/10.1190/segam2018-2994378.1FiguresReferencesRelatedDetailsCited byMagnetic anomalies characterization: Deep learning and explainabilityComputers & Geosciences, Vol. 169Improved Unet in Lithology Identification of Coal Measure Strata24 August 2022 | Lithosphere, Vol. 2022, No. Special 12Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversionSagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota, Long Jin, and Ilya Tsvankin15 August 2022Seismic Impedance Inversion based on Residual Attention NetworkDeep learning-based point-spread function deconvolution for migration image deblurringCewen Liu, Mengyao Sun, Nanxun Dai, Wei Wu, Yanwen Wei, Mingjie Guo, and Haohuan Fu27 June 2022 | GEOPHYSICS, Vol. 87, No. 4Seismic inversion with deep learning21 December 2021 | Computational Geosciences, Vol. 26, No. 2Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstructionThomas André Larsen Greiner, Jan Erik Lie, Odd Kolbjørnsen, Andreas Kjelsrud Evensen, Espen Harris Nilsen, Hao Zhao, Vasily Demyanov, and Leiv-J. Gelius31 December 2021 | GEOPHYSICS, Vol. 87, No. 2Physics-Constrained Seismic Impedance Inversion Based on Deep LearningIEEE Geoscience and Remote Sensing Letters, Vol. 19UB-Net: Improved Seismic Inversion Based on Uncertainty BackpropagationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic InterpretationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Desert Noise Suppression for Seismic Data Based on Feature Enhancement Denoising Network6 December 2021 | Izvestiya, Physics of the Solid Earth, Vol. 57, No. 6GPRInvNet: Deep Learning-Based Ground-Penetrating Radar Data Inversion for Tunnel LiningsIEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 10Metallic deposits imaging based on U-net deep learning methodXiaojie Wan, Xiangbo Gong, Zhuo Xu, and Minghao Yu1 September 2021Building training data set for deep learning-based P- and S-wave separation: Field data caseYanwen Wei, Yunyue Elita Li, and Haohuan Fu1 September 2021High-resolution seismic acoustic impedance inversion with the sparsity-based statistical modelLingqian Wang, Hui Zhou, Wenling Liu, Bo Yu, and Sheng Zhang1 July 2021 | GEOPHYSICS, Vol. 86, No. 4Semi‐supervised deep autoencoder for seismic facies classification27 May 2021 | Geophysical Prospecting, Vol. 69, No. 6Research on fault recognition method combining 3D Res-UNet and knowledge distillation8 November 2021 | Applied Geophysics, Vol. 18, No. 2Research on seismic impedance inversion method based on pre-training and improved residual networkAcoustic impedance inversion base on dual learningMachine Learning-Based Seafloor Seismic Prestack InversionIEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 5Robust deep learning seismic inversion with a priori initial model constraint20 February 2021 | Geophysical Journal International, Vol. 225, No. 3Data-driven multichannel poststack seismic impedance inversion via patch-ordering regularizationLingqian Wang, Hui Zhou, Wenling Liu, Bo Yu, Huili He, Hanming Chen, and Ning Wang5 February 2021 | GEOPHYSICS, Vol. 86, No. 2Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis WorkflowsIEEE Signal Processing Magazine, Vol. 38, No. 2Inversion of Oceanic Parameters Represented by CTD Utilizing Seismic Multi-Attributes Based on Convolutional Neural Network15 November 2020 | Journal of Ocean University of China, Vol. 19, No. 6CNN-boosted full-waveform inversionYulang Wu and George A. McMechan30 September 2020Uncertainty estimation in impedance inversion using Bayesian deep learningJunhwan Choi, Dowan Kim, and Joongmoo Byun30 September 2020Low-frequency swell noise suppression based on U-Net5 January 2021 | Applied Geophysics, Vol. 17, No. 3Well-Logging Constrained Seismic Inversion Based on Closed-Loop Convolutional Neural NetworkIEEE Transactions on Geoscience and Remote Sensing, Vol. 58, No. 8Ü-Net: Deep-Learning Schemes for Ground Penetrating Radar Data InversionLonghao Xie, Qing Zhao, Chunguang Ma, Binbin Liao, and Jianjian Huo29 July 2020 | Journal of Environmental and Engineering Geophysics, Vol. 25, No. 2Deep neural networks for 1D impedance inversion11 November 2019 | ASEG Extended Abstracts, Vol. 2019, No. 1Convolutional neural network for seismic impedance inversionVishal Das, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji9 October 2019 | GEOPHYSICS, Vol. 84, No. 6Tuning a Fully Convolutional Network for Velocity Model Estimation28 October 2019First arrival traveltime tomography using supervised descent learning technique13 September 2019 | Inverse Problems, Vol. 35, No. 10Seismic impedance inversion based on cycle-consistent generative adversarial networkYuqing Wang, Qiang Ge, Wenkai Lu, and Xinfei Yan10 August 2019Pre-stack inversion using a physics-guided convolutional neural networkReetam Biswas, Mrinal K. Sen, Vishal Das, and Tapan Mukerji1 August 2019Petrophysical properties prediction from pre-stack seismic data using convolutional neural networksVishal Das and Tapan Mukerji10 August 2019Regularized supervised descent method for 2-D magnetotelluric data inversionRui Guo, Maokun Li, Fan Yang, Shenheng Xu, and Aria Abubakar10 August 2019Pre-stack seismic inversion using SeisInv-ResNetJiameng Du, Junzhou Liu, Guangzhi Zhang, Lei Han, and Ning Li10 August 2019Semi-supervised learning for acoustic impedance inversionMotaz Alfarraj and Ghassan AlRegib10 August 2019End-to-end deep neural network for seismic inversionKe Wang, Laura Bandura, Dimitri Bevc, Shuxing Cheng, Jim DiSiena, Adam Halpert, Konstantin Osypov, Bruce Power, and Ellen Xu1 August 2019Surface-related multiple elimination with deep learningAli Siahkoohi, Dirk J. Verschuur, and Felix J. Herrmann1 August 2019Estimation of acoustic impedance from seismic data using temporal convolutional networkAhmad Mustafa, Motaz Alfarraj, and Ghassan AlRegib10 August 2019Seismic image processing through the generative adversarial networkFrancesco Picetti, Vincenzo Lipari, Paolo Bestagini, and Stefano Tubaro28 May 2019 | Interpretation, Vol. 7, No. 3Prestack 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 Vishal Das, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji, (2018), Convolutional neural network for seismic impedance inversion, SEG Technical Program Expanded Abstracts : 2071-2075. https://doi.org/10.1190/segam2018-2994378.1 Plain-Language Summary Keywordsmachine learningreservoir characterizationneural networksartificial intelligenceseismic impedancePDF DownloadLoading ..." @default.
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