Quality control of deep generator priors for statistical seismic inverse problems

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Quality control of deep generator priors for statistical seismic inverse problemsAuthors: Zhilong FangHongjian FangLaurent DemanetZhilong FangMassachusetts Institute of TechnologySearch for more papers by this author, Hongjian FangMassachusetts Institute of TechnologySearch for more papers by this author, and Laurent DemanetMassachusetts Institute of TechnologySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428013.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractPrior information is essential for statistical seismic inverse problems to quantify uncertainties of unknown subsurface parameters. Conventional prior knowledge is based on empirical observations of subsurface structures like the smoothness of the subsurface image. However, such hand-designed prior knowledge is too generic to describe detailed subsurface structures. Deep generator priors are a recent encouraging development, where a high-quality prior can be crafted solely from existing data and a deep neural network. Such deep generators can create detailed subsurface models from Gaussian vectors in a low-dimensional latent space. Alongside this potential advantage, comes the major risk that the prior might be too restrictive to give physically meaningful solutions a high enough likelihood. In this work, we attempt to mitigate this risk by presenting a measure to evaluate the quality of the learned deep generator prior, which can then be leveraged to tune its hyperparameters. Given a testing model and a generator, we first find the best generated model that minimizes the `2-norm misfit between the generated and testing models. To measure the quality of the generator, we suggest two criteria: (1) the `2-norm model error should be small; and (2) the `2-norm of the corresponding latent vector should be bounded and within a pre-defined reference range. Numerical examples show that a good prior generator in the sense of the proposed measure can help produce more accurate results for statistical seismic inverse problems.Presentation Date: Wednesday, October 14, 2020Session Start Time: 1:50 PMPresentation Time: 1:50 PMLocation: Poster Station 1Presentation Type: PosterKeywords: machine learning, full-waveform inversion, tomography, statisticsPermalink: https://doi.org/10.1190/segam2020-3428013.1FiguresReferencesRelatedDetailsCited byDeep Bayesian inference for seismic imaging with tasksAli Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann25 August 2022 | GEOPHYSICS, Vol. 87, No. 5Generative modeling for inverse problemsRami Nammour15 August 2022 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 Zhilong Fang, Hongjian Fang, and Laurent Demanet, (2020), "Quality control of deep generator priors for statistical seismic inverse problems," SEG Technical Program Expanded Abstracts : 1715-1719. https://doi.org/10.1190/segam2020-3428013.1 Plain-Language Summary Keywordsmachine learningfull-waveform inversiontomographystatisticsPDF DownloadLoading ...