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    Convolutional sparse coding for noise attenuation in seismic data

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    06_csc_denoising.pdf
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Liu, Zhaolun cc
    Lu, Kai cc
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2021-01-04
    Online Publication Date
    2021-01-04
    Print Publication Date
    2021-01-01
    Submitted Date
    2019-11-17
    Permanent link to this record
    http://hdl.handle.net/10754/667240
    
    Metadata
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    Abstract
    We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training with the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in complex field data. By excluding filters with coherent noise features, our method can further attenuate coherent noise and separate ground roll.
    Citation
    Liu, Z., & Lu, K. (2021). Convolutional sparse coding for noise attenuation in seismic data. GEOPHYSICS, 86(1), V23–V30. doi:10.1190/geo2019-0746.1
    Sponsors
    The research reported in this publication 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 Consortium for their financial support. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST and the IT Research Computing Group. We thank them for providing the computational resources required for carrying out this work.
    Publisher
    Society of Exploration Geophysicists
    Journal
    GEOPHYSICS
    DOI
    10.1190/geo2019-0746.1
    Additional Links
    http://mr.crossref.org/iPage?doi=10.1190%2Fgeo2019-0746.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/geo2019-0746.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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