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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;

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.
[1] Bayes, T., Price, R., and Canton, J., 1763, An essay towards solving a problem in the doctrine of chances: C. Davis, Printer to the Royal Society of London.
[2] Bonar, D., and Sacchi, M., 2012, Denoising seismic data using the nonlocal means algorithm: Geophysics, 77(1), A5−A8.
[3] Buades, A., Coll, B., and Morel, J. M., 2005, A review of image denoising algorithms, with a new one: SIAM Journal on Multiscale Modeling and Simulation, 4(2), 490−530.
[4] Buades, A., Coll, B., and Morel, J. M., 2010, Image denoising algorithms: A new nonlocal principle: SIAM Review, 52(1), 113−147.
[5] Chang, D. K., Wang, Y. H., and Zhang, G. Z., 2015, Seismic Data Denoising Based on Sparse and Redundant Representation: 85th Annual International Meeting, SEG,, Expanded Abstracts, 4693−4697.
[6] Elad, M., and Aharon, M., 2006, Image denoising via sparse and redundant representations over learned dictionaries: IEEE Transactions on Image Processing, 15(12), 3736−3745.
[7] Górszczyk, A., Adamczyk, A., and Malinowski, M., 2014, Application of curvelet denoising to 2D and 3D seismic data—Practical considerations: Journal of Applied Geophysics, 105, 78−94.
[8] Kustowski, B., Cole, J., Martin, H., and Hennenfent, G., 2013, Curvelet noise attenuation with adaptive adjustment for spatio-temporally varying noise: 83th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts 2013, 4299−4303.
[9] Lebrun, M., Buades, A., and Morel, J. M., 2013, A nonlocal Bayesian image denoising algorithm: SIAM Journal on Imaging Sciences, 6(3), 1665−1688.
[10] Neelamani, R., Baumstein, A. I., Gillard, D. G., Hadidi, M. T., and Soroka, W. L., 2008, Coherent and random noise attenuation using the curvelet transform: The Leading Edge, 27(2), 240−248.
[11] Shang, S., Han, L. G., Lv, Q. T., and Tan, C. Q., 2013, Seismic random noise suppression using an adaptive nonlocal means algorithm: Applied Geophysics, 10(1), 33−40.
[12] Tang, G., Ma, J. W., and Yang, H. Z., 2012, Seismic data denoising based on learning-type overcomplete dictionaries: Applied Geophysics, 9(1), 27−32.
[13] Yilmaz, Ö., 2001, Seismic data analysis: Processing, Inversion, and Interpretation of seismic data: Society of Exploration Geophysicists, 658−709.
[14] Yuan, S. Y., and Wang, S. X., 2013, Edge-preserving noise reduction based on Bayesian inversion with directional difference constraints: Journal of Geophysics and Engineering, 10(2), 025001.
[15] Yuan, S. Y., Wang, S. X., and Li, G., 2011, Random noise reduction using Bayesian inversion: Journal of Geophysics and Engineering, 9(1), 60−68.
[16] Zhang, G. Z., Chang, D. K., Wang, Y. H., Li, Z. Z., Zhao, Y., and Yin, X. Y., 2015, 3D seismic random noise suppression with sparse and redundant representation: Oil Geophysical Prospecting, 50(4), 600−606.
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