Published as Journal of Geophysics and Engineering, 12, 262-272, (2015)

Structure-oriented singular value decomposition for random noise attenuation of seismic data

Shuwei Gan% latex2html id marker 2146
\setcounter{footnote}{1}\fnsymbol{footnote}, Yangkang Chen% latex2html id marker 2147
\setcounter{footnote}{2}\fnsymbol{footnote}, Shaohuan Zu % latex2html id marker 2148
\setcounter{footnote}{1}\fnsymbol{footnote}, Shan Qu% latex2html id marker 2149
\setcounter{footnote}{1}\fnsymbol{footnote}and Wei Zhong % latex2html id marker 2150
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\setcounter{footnote}{1}\fnsymbol{footnote}State Key Laboratory of Petroleum Resources and Prospecting
China University of Petroleum
Fuxue Road 18th
Beijing, China, 102200
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\setcounter{footnote}{2}\fnsymbol{footnote}Bureau of Economic Geology
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, TX 78713-8924


Singular value decomposition (SVD) can be used both globally and locally to remove random noise in order to improve the signal-to-noise ratio (SNR) of seismic data. However, they can only be applied to seismic data with simple structure such that there is only one dip component in each processing window. We introduce a novel denoising approach that utilizes a structure-oriented SVD and this approach can enhance seismic reflections with continuous slopes. We create a third dimension for a 2D seismic profile by using the plane-wave prediction operator to predict each trace from its neighbour traces and apply SVD along this dimension. The added dimension is equal to flattening the seismic reflections within a neighbouring window. The third dimension is then averaged to decrease the dimension. We use two synthetic examples with different complexities and one field data example to demonstrate the performance of the proposed structure-oriented SVD. Compared with global and local SVDs, and $f-x$ deconvolution, the structure-oriented SVD can obtain much clearer reflections and preserve more useful energy.