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    Deep Learning Tomography by Mapping Full Seismic Waveforms to Vertical Velocity Profiles

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    Type
    Conference Paper
    Authors
    Kazei, Vladimir cc
    Ovcharenko, Oleg cc
    Plotnitskii, Pavel
    Peter, Daniel cc
    Zhang, X.
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Physical Science and Engineering (PSE) Division
    Earth Science and Engineering Program
    Earth Science and Engineering
    Extreme Computing Research Center
    KAUST
    Date
    2020
    Embargo End Date
    2021-12-30
    Permanent link to this record
    http://hdl.handle.net/10754/668224
    
    Metadata
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    Abstract
    Building realistic and reliable models of the subsurface is the primary goal of seismic imaging. By employing an ensemble of convolutional neural networks (CNNs), we build velocity models directly from seismic pre-stack data and quantify model uncertainties by analyzing all ensemble results. Most attempts are made to infer models as a whole. Here, CNNs are trained to map subsets of seismic data directly into 1D vertical velocity logs. This allows us to integrate well data into the inversion and to simplify the mapping by using the regularity of active seismic acquisition geometries. The presented approach uses neighboring common midpoint gathers (CMPs) for the estimation of individual vertical velocity logs. Trained on augmentations of the Marmousi model, our CNNs allow for the inversion of sections of the Marmousi II and the Overthrust models. Once the ensemble is trained on a particular dataset, similar datasets can be inverted much faster than with conventional full-waveform inversion.
    Citation
    Kazei, V., Ovcharenko, O., Plotnitskii, P., Peter, D., Zhang, X., & Alkhalifah, T. (2020). Deep Learning Tomography by Mapping Full Seismic Waveforms to Vertical Velocity Profiles. EAGE 2020 Annual Conference & Exhibition Online. doi:10.3997/2214-4609.202011980
    Sponsors
    We thank Jan Walda of Uni Hamburg, Andrey Bakulin of Saudi Aramco, members of the Seismic Wave Analysis Group (SWAG) at KAUST for constructive discussions. We are grateful to Saudi Aramco for support. The research reported in this publication was supported by funding from KAUST.
    Publisher
    EAGE Publications
    Conference/Event name
    EAGE2020: Annual Conference Online
    DOI
    10.3997/2214-4609.202011980
    Additional Links
    https://www.earthdoc.org/content/papers/10.3997/2214-4609.202011980
    Relations
    Is Supplemented By:
    • [Software]
      Title: vkazei/deeplogs: Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.. Publication Date: 2019-06-30. github: vkazei/deeplogs Handle: 10754/668289
    ae974a485f413a2113503eed53cd6c53
    10.3997/2214-4609.202011980
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program

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