{"title":"Anisotropic Total Fractional Order Variation Model in Seismic Data Denoising","authors":"Jianwei Ma, Diriba Gemechu","volume":133,"journal":"International Journal of Geological and Environmental Engineering","pagesStart":40,"pagesEnd":45,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10008470","abstract":"In seismic data processing, attenuation of random noise
\r\nis the basic step to improve quality of data for further application
\r\nof seismic data in exploration and development in different gas
\r\nand oil industries. The signal-to-noise ratio of the data also highly
\r\ndetermines quality of seismic data. This factor affects the reliability
\r\nas well as the accuracy of seismic signal during interpretation
\r\nfor different purposes in different companies. To use seismic data
\r\nfor further application and interpretation, we need to improve the
\r\nsignal-to-noise ration while attenuating random noise effectively.
\r\nTo improve the signal-to-noise ration and attenuating seismic
\r\nrandom noise by preserving important features and information
\r\nabout seismic signals, we introduce the concept of anisotropic
\r\ntotal fractional order denoising algorithm. The anisotropic total
\r\nfractional order variation model defined in fractional order bounded
\r\nvariation is proposed as a regularization in seismic denoising. The
\r\nsplit Bregman algorithm is employed to solve the minimization
\r\nproblem of the anisotropic total fractional order variation model
\r\nand the corresponding denoising algorithm for the proposed method
\r\nis derived. We test the effectiveness of theproposed method for
\r\nsynthetic and real seismic data sets and the denoised result is
\r\ncompared with F-X deconvolution and non-local means denoising
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