Inversionet: Accurate and efficient seismic-waveform inversion with convolutional neural networks

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Inversionet: Accurate and efficient seismic-waveform inversion with convolutional neural networksAuthors: Yue WuYouzuo LinZheng ZhouYue WuLos Alamos National LaboratorySearch for more papers by this author, Youzuo LinLos Alamos National LaboratorySearch for more papers by this author, and Zheng ZhouLos Alamos National LaboratorySearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2998603.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic full-waveform inversion has become a promising tool for velocity estimation in complex geological structures. The traditional seismic full-waveform inversion problems are usually posed as nonlinear optimization problems. Solving fullwaveform inversion can be computationally challenging for two major reasons. One is the expensive computational cost and the other is the issue of local minima. In this work, we develop an end-to-end data-driven inversion technique, called “InversionNet”, to learn a regression relationship from seismic waveform datasets to subsurface models. Specifically, we build a novel deep convolutional neural network with an encoder-decoder structure, where the encoder learns an abstract representation of the seismic data, which is then used by the decoder to produce a subsurface model. We further incorporate atrous convolutions in our network structure to account for contextural information from the subsurface model. We evaluate the performance of our InversionNet with synthetic seismic waveform data. The experiment results demonstrate that our InversionNet not only yields accurate inversion results but also produces almost real-time inversion.Presentation Date: Wednesday, October 17, 2018Start Time: 1:50:00 PMLocation: 204B (Anaheim Convention Center)Presentation Type: OralKeywords: 2D, inversion, machine learningPermalink: https://doi.org/10.1190/segam2018-2998603.1FiguresReferencesRelatedDetailsCited byImplicit Seismic Full Waveform Inversion With Deep Neural Representation27 February 2023 | Journal of Geophysical Research: Solid Earth, Vol. 128, No. 33-D Gravity Intelligent Inversion by U-Net Network With Data AugmentationIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Deep Velocity Generator: A Plug-In Network for FWI EnhancementIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Data-driven seismic prestack velocity inversion via combining residual network with convolutional autoencoderJournal of Applied Geophysics, Vol. 38Inversion of 1-D magnetotelluric data using CNN-LSTM hybrid network20 August 2022 | Arabian Journal of Geosciences, Vol. 15, No. 17Estimate near-surface velocity with reversals using deep learning and full-waveform inversionYong Ma, Xu Ji, Weiguang He, and Tong Fei15 August 2022Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantificationWeiqiang Zhu, Kailai Xu, Eric Darve, Biondo Biondi, and Gregory C. 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