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    Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

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    geo2016-02532E1.pdf
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    Type
    Article
    Authors
    Liu, Zhaolun cc
    AlTheyab, Abdullah cc
    Hanafy, Sherif M.
    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
    2017-03-06
    Online Publication Date
    2017-03-06
    Print Publication Date
    2017-05
    Permanent link to this record
    http://hdl.handle.net/10754/622980
    
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    Abstract
    We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength λ of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third λ. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half λ.
    Citation
    Liu Z, AlTheyab A, Hanafy SM, Schuster G (2017) Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test. GEOPHYSICS 82: S197–S205. Available: http://dx.doi.org/10.1190/geo2016-0253.1.
    Sponsors
    The research reported in this paper was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We thank the sponsors of the Center for Subsurface Imaging and Fluid Modeling (CSIM) consortium for their support. We would also like to thank the high-performance computing center of KAUST for providing access to supercomputing facilities. We thank B. Guo and Z. Feng for editing the paper. A. AlTheyab thanks Saudi Aramco for sponsoring his graduate studies. We also thank the associate editor J. van der Neut and three anonymous reviewers whose reviews improved the quality of this manuscript.
    Publisher
    Society of Exploration Geophysicists
    Journal
    GEOPHYSICS
    DOI
    10.1190/geo2016-0253.1
    Additional Links
    http://library.seg.org/doi/10.1190/geo2016-0253.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/geo2016-0253.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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