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    High-Wavenumber Extrapolation for Band-Limited FWI Results Using Deep Learning

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    Type
    Conference Paper
    Authors
    Li, Y.
    Alali, A.
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    King Abdullah University Of Science And Technology
    King Abdullah University of Science and Technology
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2022
    Permanent link to this record
    http://hdl.handle.net/10754/678310
    
    Metadata
    Show full item record
    Abstract
    A reliable estimation of high-resolution subsurface properties is a very challenging task for seismic inversion. Seismic data and well logs can jointly contribute to this task by combining their advantages. We have developed a deep learning method to extrapolate high-wavenumber model components for band-limited full waveform inversion (FWI) results by incorporating high-resolution well-log velocity information. A U-Net architecture, that is adapted to 1D data, is used to build the mapping relationship between the FWI result and the well velocities. We apply a window warping approach to the available well logs to augment the training data, which helps reduce potential overfitting. These one dimensional well models, which serve as labels for the neural network (NN) model training, will be used to generate synthetic data for FWI to obtain the corresponding input FWI velocities to the NN model. After the training process, we can employ the trained NN model to infer high-resolution velocities for each profile of the band-limited FWI result. The test and analysis on the Otway model demonstrate that the deep learning can help inject high wavenumbers into the band-limited FWI result.
    Citation
    Li, Y., Alali, A., & Alkhalifah, T. (2022). High-Wavenumber Extrapolation for Band-Limited FWI Results Using Deep Learning. 83rd EAGE Annual Conference & Exhibition. https://doi.org/10.3997/2214-4609.202210778
    Sponsors
    We thank the Shaheen supercomputing Laboratory in KAUST for providing the computational support. We appreciate the fruitful discussions and collaborative environment in our group (SWAG).
    Publisher
    European Association of Geoscientists & Engineers
    Conference/Event name
    83rd EAGE Annual Conference & Exhibition
    DOI
    10.3997/2214-4609.202210778
    Additional Links
    https://www.earthdoc.org/content/papers/10.3997/2214-4609.202210778
    ae974a485f413a2113503eed53cd6c53
    10.3997/2214-4609.202210778
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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