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    Wavefield Solutions from Machine Learned Functions that Approximately Satisfy the Wave Equation

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    Thumbnail
    Name:
    eageabs.pdf
    Size:
    17.31Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2021-06-11
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    Type
    Conference Paper
    Authors
    Alkhalifah, Tariq Ali cc
    Song, Chao cc
    Waheed, U. Bin
    Hao, Q.
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2020
    Embargo End Date
    2021-06-11
    Permanent link to this record
    http://hdl.handle.net/10754/668213
    
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    Abstract
    Solving the Helmholtz wave equation provides wavefield solutions that are dimensionally compressed, per frequency, compared to the time domain, which is useful for many applications, like full waveform inversion (FWI). However, the efficiency in attaining such wavefield solutions depends often on the size of the model, which tends to be large at high frequencies and for 3D problems. Thus, we use a recently introduced framework based on predicting such functional solutions through setting the underlying physical equation as a cost function to optimize a neural network for such a task. We specifically seek the solution of the functional scattered wavefield in the frequency domain through a neural network considering a simple homogeneous background model. Feeding the network a reasonable number random points from the model space will ultimately train a fully connected 8-layer deep neural network with each layer having a dimension of 20, to predict the scattered wavefield function. Initial tests on a two-box-shaped scatterer model with a source in the middle, as well as, a layered model with a source on the surface demonstrate the successful training of the NN for this application and provide us with a peek into the potential of such an approach.
    Citation
    Alkhalifah, T., Song, C., Waheed, U. B., & Hao, Q. (2020). Wavefield Solutions from Machine Learned Functions that Approximately Satisfy the Wave Equation. EAGE 2020 Annual Conference & Exhibition Online. doi:10.3997/2214-4609.202010588
    Publisher
    EAGE Publications
    Conference/Event name
    EAGE2020: Annual Conference Online
    DOI
    10.3997/2214-4609.202010588
    Additional Links
    https://www.earthdoc.org/content/papers/10.3997/2214-4609.202010588
    ae974a485f413a2113503eed53cd6c53
    10.3997/2214-4609.202010588
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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