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    Enhancing low-wavenumber information in RWI using energy norm Born scattering

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    Type
    Conference Paper
    Authors
    Wang, Guanchao
    Alkhalifah, Tariq
    Wang, Shangxu
    Date
    2019-08-10
    Permanent link to this record
    http://hdl.handle.net/10754/674935
    
    Metadata
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    Abstract
    Full waveform inversion (FWI) plays a central role in the field of exploration geophysics due to its potential in recovering the properties of subsurface at a high resolution. A starting model with ample long-wavelength components is essential for the success of most FWI algorithms. Reflection waveform inversion (RWI) is one popular way to invert for the long-wavelength velocity components from the short offset seismic data by decomposing the gradient of FWI into migration and tomographic terms. However, the transmitted part of Born scattering in conventional RWI still produces high-wavenumber artifacts, which would hinder its convergence. Thus, an efficient non-transmission energy norm born scattering is used in RWI to overcome the drawbacks of conventional RWI. We use numerical examples to show that the energy norm born scattering can provide clean transmission energy from the reflector and enhance the low-wavenumber information in the RWI gradient.
    Citation
    Wang, G., Alkhalifah, T., & Wang, S. (2019). Enhancing low-wavenumber information in RWI using energy norm Born scattering. SEG Technical Program Expanded Abstracts 2019. doi:10.1190/segam2019-3211346.1
    Sponsors
    We thank KAUST for its support and SWAG for the collaborative environment. The author Guanchao Wang would like to thank Bingbing Sun for the fruitful discussion and China Scholarship Council (CSC) for supporting his study in KAUST.
    Publisher
    Society of Exploration Geophysicists
    Conference/Event name
    Society of Exploration Geophysicists International Exposition and 89th Annual Meeting, SEG 2019
    DOI
    10.1190/segam2019-3211346.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2019-3211346.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2019-3211346.1
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