Deep learning prior models from seismic images for full-waveform inversion

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2017Deep learning prior models from seismic images for full-waveform inversionAuthors: Winston LewisDenes VighWinston LewisSchlumbergerSearch for more papers by this author and Denes VighWesternGecoSearch for more papers by this authorhttps://doi.org/10.1190/segam2017-17627643.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Full-waveform inversion (FWI) is now a mature technology that is routinely used in exploration around the world to obtain high resolution earth models. In geological areas such as the Gulf of Mexico, however, reconstructing complex salt geobodies poses a huge challenge to FWI due to the absence of low frequencies in the data needed to resolve such features. A skilled seismic interpreter has to interpret these geobodies and manually insert them into the earth model and repeat this process several times in the earth model building workflow. Deep learning algorithms have gained a lot of interest in recent years by obtaining state-of-the art results in various problems arising in the fields of computer vision, automatic speech recognition and natural language processing. We investigate the use of these algorithms to generate useful prior models for full-waveform inversion by learning features relevant to earth model building from a seismic image. We test this methodology in full-waveform inversion by generating a probability map of salt bodies in the migrated image along with a prior model and incorporating it in the FWI objective function. This approach is shown to be promising in enabling an automated salt body reconstruction using FWI. Presentation Date: Thursday, September 28, 2017 Start Time: 10:10 AM Location: 361F Presentation Type: ORAL Keywords: neural networks, salt, Gulf of Mexico, low frequency, full-waveform inversionPermalink: https://doi.org/10.1190/segam2017-17627643.1FiguresReferencesRelatedDetailsCited byIntelligent AVA Inversion Using a Convolution Neural Network Trained with Pseudo-Well Datasets30 January 2023 | Surveys in Geophysics, Vol. 86Hierarchical transfer learning for deep learning velocity model buildingJérome Simon, Gabriel Fabien-Ouellet, Erwan Gloaguen, and Ishan Khurjekar5 January 2023 | GEOPHYSICS, Vol. 88, No. 1Inverse-Scattering Theory Guided U-Net Neural Networks for Internal Multiple EliminationIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Deep Velocity Generator: A Plug-In Network for FWI EnhancementIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Wasserstein Distance-Based Full-Waveform Inversion With a Regularizer Powered by Learned GradientIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Quality classification and inversion of receiver functions using convolutional neural network14 November 2022 | Geophysical Journal International, Vol. 232, No. 3Deep-learning application of salt geometry detection in deep water BrazilRuichao Ye, Anatoly Baumstein, Kirk A. 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