Abstract
For random noise suppression of seismic data, we present a non-local Bayes (NLBayes) 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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The authors would like to thank anonymous reviewers for their constructive comments on this paper and the board editor’s help and guidance.
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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).
Chang De-Kuan works as an engineer in Research Institute of Petroleum Exploration & Development-Northwest, PetroChina. He received his Bachelor's degree from China University of Petroleum (East China) (2012), Master's degree from China University of Petroleum (East China) (2015). He has worked on seismic data processing and reservoir fractures prediction. His general interests lie in deep learning and computer vision and their application to seismic exploration.
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Chang, DK., Yang, WY., Wang, YH. et al. Random noise suppression for seismic data using a non-local Bayes algorithm. Appl. Geophys. 15, 91–98 (2018). https://doi.org/10.1007/s11770-018-0657-x
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DOI: https://doi.org/10.1007/s11770-018-0657-x