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APPLIED GEOPHYSICS  2018, Vol. 15 Issue (1): 91-98    DOI: 10.1007/s11770-018-0657-x
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Random noise suppression for seismic data using a non-local Bayes algorithm
Chang De-Kuan1, Yang Wu-Yang1, Wang Yi-Hui2, Yang Qing1, Wei Xin-Jian1, and Feng Xiao-Ying2
1. Research Institute of Petroleum Exploration & Development-Northwest, PetroChina, Lanzhou 730020, China.
2. Research Institute of  Exploration & Development, Huabei Oilfield Company, Renqiu 062552, China.
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Abstract For random noise suppression of seismic data, we present a non-local Bayes (NL- Bayes) filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data,  the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.
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Key wordsNon-local Bayes   random noise suppression   block-matching   Gaussian model     
Received: 2016-07-22;
Fund:

This work is financially sponsored by Research Institute of Petroleum Exploration & Development (PETROCHINA) Scientific Research And Technology Development Projects (No. 2016ycq02), China National Petroleum Corporation Science & Technology Development Projects (No. 2015B-3712) and Ministry of National Science & Technique (No. 2016ZX05007-006).

Cite this article:   
. Random noise suppression for seismic data using a non-local Bayes algorithm[J]. APPLIED GEOPHYSICS, 2018, 15(1): 91-98.
 
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