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    Multiscale Reflection Phase Inversion with Migration Deconvolution

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    Type
    Article
    Authors
    Chen, Yuqing cc
    Feng, Zongcai cc
    Fu, Lei
    AlTheyab, Abdullah
    Feng, Shihang cc
    Schuster, Gerard T. cc
    KAUST Department
    Center for Subsurface Imaging and Fluid Modeling
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2019-09-14
    Online Publication Date
    2019-09-14
    Print Publication Date
    2020-01-01
    Permanent link to this record
    http://hdl.handle.net/10754/660205
    
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    Abstract
    Reflection full waveform inversion (RFWI) can recover the low-wavenumber components of the velocity model along with the reflection wavepaths. However, this requires an expensive least-square reverse time migration (LSRTM) to construct the perturbation image and RFWI also suffers from cycle-skipping problems. As an inexpensive alternative to LSRTM, we propose the use of migration deconvolution (MD) with RFWI. To mitigate cycle-skipping problems, we introduce a multiscale reflection phase inversion (MRPI) strategy which boosts the low-frequency data and only needs to explain the phase information in the recorded data, not its amplitude spectrum. To mitigate cycle-skipping problems, we use the rolling-offset strategy which gradually extends the offset range of data with an increasing number of iterations. Numerical results show that the MRPI + MD method can efficiently recover the low-wavenumber components of the velocity model and is less prone to getting stuck in local minima compared to conventional RFWI.
    Citation
    Chen, Y., Feng, Z., Fu, L., AlTheyab, A., Feng, S., & Schuster, G. (2019). Multiscale Reflection Phase Inversion with Migration Deconvolution. GEOPHYSICS, 1–61. doi:10.1190/geo2018-0751.1
    Sponsors
    The research reported in this paper was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We are grateful to the sponsors of the Center for Subsurface Imaging and Modeling (CSIM) Consortium for their financial support. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST. We thank them for providing the computational resources required for carrying out this work. The authors would like to thank Statoil ASA and the Volve license partners ExxonMobil E&P Norway AS and Bayerngas Norge AS, for the release of the Volve data. The views expressed in this paper are the views of the authors and do not necessarily reflect the views of Statoil ASA and the Volve field license partners.
    Publisher
    Society of Exploration Geophysicists
    Journal
    GEOPHYSICS
    DOI
    10.1190/geo2018-0751.1
    Additional Links
    https://library.seg.org/doi/10.1190/geo2018-0751.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/geo2018-0751.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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