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    Velocity model building by deep learning: From general synthetics to field data application

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    Velocity_model_building_by_deep_learning__from_general_synthetics_to_field_data_application (1).pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Kazei, Vladimir cc
    Ovcharenko, Oleg cc
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2020-09-30
    Online Publication Date
    2020-09-30
    Print Publication Date
    2020-09-30
    Permanent link to this record
    http://hdl.handle.net/10754/665479
    
    Metadata
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    Abstract
    Velocity model building is not straightforward in geologically complex environments. We train a convolutional neural network (CNN) to map full wavefields to smooth subsurface parameter distributions to address the problem. Specifically, cubes of neighboring CMP gathers are mapped into in 1D vertical profiles to simplify the training phase and to make it easier to utilize well logs in future applications. We train the CNN using a total of one hundred thousand random subsurface models generated on-the-fly and the corresponding synthetic data. The application of the trained CNN on synthetic and real data admitted reasonably accurate models representing mostly the low wavenumber features of the true models.
    Citation
    Kazei, V., Ovcharenko, O., & Alkhalifah, T. (2020). Velocity model building by deep learning: From general synthetics to field data application. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3428324.1
    Sponsors
    We thank Saudi Aramco for its support and CGG for sharingthe broadseis field data. The research reported in this publication was supported by funding from King Abdullah Universityof Science and Technology (KAUST), Thuwal, 23955-6900,Saudi Arabia
    Publisher
    Society of Exploration Geophysicists
    DOI
    10.1190/segam2020-3428324.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2020-3428324.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2020-3428324.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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