Seismic denoising based on nonlocal structured Bayesian nonparametric dictionary
- Nanying Lan (China University of Petroleum (East china)) | Fanchang Zhang (China University of Petroleum (East china))
- Document ID
- Society of Exploration Geophysicists
- SEG International Exposition and Annual Meeting, 15-20 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Exploration Geophysicists
- Statistics, Signal processing, Sparse
- 3 in the last 30 days
- 5 since 2007
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Sparse representation is widely used in seismic random noise attenuation, which typically assumes that the noise variance is known a priori. However, in practical applications, the noise variance is unknown and not easily available. Therefore, this paper proposes a denoising method by nonlocal structured Bayesian nonparametric dictionary learning, which does not rely on noise variance prior. The method utilizes Bayesian nonparametric dictionary learning to avoid estimating noise variance, and using the nonlocal structured beta process to introduce non-local self-similarity into the structure prior to enhance the performance of seismic denoising. We test the performance of this method on synthetic and field data with random noise. The results demonstrate that, compared to conventional K-SVD method, our method achieves preferable denoising performance in terms of random noise attenuation and signal preservation.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 1:50 PM
Location: Poster Station 2
Presentation Type: Poster
|File Size||890 KB||Number of Pages||5|
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