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Sparse Bayesian Learning-Based Seismic Denoise by Using Physical Wavelet as Basis Functions
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DOI：10.1109/LGRS.2017.2745564.png)
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Abstract
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Attenuating random noise is a fundamental yet necessary step for subsequent seismic image processing and interpretation. We introduce a sparse Bayesian learning (SBL)-based seismic denoise method by using the physical wavelet as the basis function. The physical wavelet estimated from seismic and well logging data can appropriately describe the characteristics of the seismic data. Thus, it is an appropriate choice of basis function. Moreover, the tradeoff regularization parameter for determining denoise quality can be adaptively estimated according to the updated data misfit and sparseness degree during the iterative process of the SBL algorithm. The motivation behind the denoise method using sparse representations is that seismic signals can be sparsely represented by using several physical wavelets, whereas noise cannot. Both synthetic and real seismic data examples are adopted to demonstrate the effectiveness of the method.
Keywords:
Denoising
physical wavelet
seismic data
sparse Bayesian learning (SBL)
sparse representations
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