A Multispectral Denoising Framework for Seismic Random Noise Attenuation

Random noise attenuation plays an important role in the seismic data processing. The global coherency among different spectral segments is often neglected in the traditional denoising methods, even though the seismic data are naturally broadband in the frequency spectrum. We proposed a multispectral denoising framework (MDF) for seismic random noise attenuation. The MDF contains three key components. First, we use a series of narrow-band filters on the noisy data to construct the multispectral data. Second, we normalize the constructed multispectral data to mitigate the amplitude discrepancy between different spectral slices. Third, we apply the intrinsic tensor sparsity regularization method to denoise the multispectral data. Numerical experiments on synthetic and field data show that the proposed framework can reduce the random noise as well as the erratic noise, and it can achieve improved denoised results, compared with a traditional method (i.e., the $f$ - $x$ deconvolution method) and the single-spectral version of the proposed method (i.e., the weighted nuclear norm minimization (WNNM) method). The proposed framework can be applied to both prestack and stacked data and generally well preserve the amplitude of useful signals, compared with the other two methods.