Abstract
Nonlocal means filtering is a noise attenuation method based on redundancies in image information. It is also a nonlocal denoising method that uses the self-similarity of an image, assuming that the valid structures of the image have a certain degree of repeatability that the random noise lacks. In this paper, we use nonlocal means filtering in seismic random noise suppression. To overcome the problems caused by expensive computational costs and improper filter parameters, this paper proposes a block-wise implementation of the nonlocal means method with adaptive filter parameter estimation. Tests with synthetic data and real 2D post-stack seismic data demonstrate that the proposed algorithm better preserves valid seismic information and has a higher accuracy when compared with traditional seismic denoising methods (e.g., f-x deconvolution), which is important for subsequent seismic processing and interpretation.
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This work is supported by the National Natural Science Foundation of China (No.41074075), National Science and Technology Project (SinoProbe-03), National public industry special subject (No. 201011047-02), and Graduate Innovation Fund of Jilin University (No. 20121070).
Shang Shuai earned his graduate from the College of Geo-Exploration of Science and Technology, Jilin University in 2009 and then his MS from this College. Now, he is studying his PhD in Jilin University. His research interests mainly include seismic data processing and hydrocarbon detection.
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Shang, S., Han, LG., Lv, QT. et al. Seismic random noise suppression using an adaptive nonlocal means algorithm. Appl. Geophys. 10, 33–40 (2013). https://doi.org/10.1007/s11770-013-0362-8
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DOI: https://doi.org/10.1007/s11770-013-0362-8