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Published as Geophysical Prospecting, 58, 415-427, (2010)

Nonlinear structure-enhancing filtering using plane-wave prediction

Yang Liu% latex2html id marker 1945
\setcounter{footnote}{1}\fnsymbol{footnote}% latex2html id marker 1946
\setcounter{footnote}{2}\fnsymbol{footnote}, Sergey Fomel% latex2html id marker 1947
\setcounter{footnote}{2}\fnsymbol{footnote}, Guochang Liu% latex2html id marker 1948
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% latex2html id marker 1942
\setcounter{footnote}{1}\fnsymbol{footnote}College of Geo-Exploration Science and Technology,
Jilin University
No.6 Xi minzhu street,
Changchun, China, 130026
% latex2html id marker 1943
\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, USA, 78713-8924
% latex2html id marker 1944
\setcounter{footnote}{3}\fnsymbol{footnote}State Key Laboratory of Petroleum Resource and Prospecting,
China University of Petroleum-Beijing
18 Fuxue road, Changping,
Beijing, China, 102249

Abstract:

Attenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation. We present a new technique, which aims at reducing random noise while protecting structural information. The technique is based on combining structure prediction with either similarity-mean filtering or lower-upper-middle (LUM) filtering. We use structure prediction to form a structural prediction of seismic traces from neighboring traces. We apply a nonlinear similarity-mean filter or an LUM filter to select best samples from different predictions. In comparison with other common filters, such as mean or median, the additional parameters of the nonlinear filters allow us to better control the balance between eliminating random noise and protecting structural information. Numerical tests using synthetic and field data show the effectiveness of the proposed structure-enhancing filters.




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2013-03-02