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In addition to the aforementioned one-step noise attenuation, Chen and Fomel ( 2015) proposed a two-step approach for retrieving the lost useful signals from the noise section, which can be seen as residual random noise attenuation.įreire and Ulrych ( 1988) proposed to carry out rank reduction of seismic images in the t– x domain via singular value decomposition (SVD). The transform operator can be a fixed-basis sparsity-promoting transform, and can also be an adaptively learned dictionary: the fixed-basis transform enjoys better efficiency and the learning-based dictionary enjoys better adaptivity. The fourth is based on a transformed domain thresholding strategy (Neelamani et al 2008, Fomel and Liu 2010, Chen et al 2014a). This type of approach is also related to those rank-reduction based approaches, eigenimage-based approaches. The third is based on extracting the principal components of seismic data, such as multichannel singular spectrum analysis (MSSA) (Oropeza and Sacchi 2011, Chen et al 2015) and EMD based approaches (Chen et al 2014c). The second is based on the statistical properties of seismic profiles, such as the mean filter (Bonar and Sacchi 2012) or median filter (Liu 2013, Chen 2014a, Chen et al 2014b).
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This approach does not require the spatial coherency assumption and has the potential to be widely used to denoise microseismic signal, the SNR of which is very low. Yang et al ( 2015) proposed a novel trace-by-trace random noise attenuation approach based on the predictable spectral components property of useful seismic reflections using regularized non-stationary autoregression (Fomel 2013). The first is based on the predictive property of useful signals in small spatial–temporal windows, such as f– x deconvolution (Canales 1984, Chen and Ma 2014), f– x– y prediction filtering (Wang 1999, Wang 2002), t– x prediction error filtering (Abma and Claerbout 1995), and f– x non-stationary polynomial fitting (Liu et al 2011). There are generally four different categories of random noise attenuation approaches that exist in the exploration geophysics literature. The enhanced seismic signals with higher signal-to-noise ratio (SNR) can help interpreters to make more accurate decisions. The attenuation of random noise is an important subject in seismic data processing. Singular value decomposition, random noise attenuation, global SVD, local SVD, structure-oriented SVD 1. Compared with global and local SVDs, and f– x deconvolution, the structure-oriented SVD can obtain much clearer reflections and preserve more useful energy. We use two synthetic examples with different complexities and one field data example to demonstrate the performance of the proposed structure-oriented SVD. The third dimension is then averaged to decrease the dimension. The added dimension is equivalent to flattening the seismic reflections within a neighbouring window. We create a third dimension for a 2D seismic profile by using the plane-wave prediction operator to predict each trace from its neighbour traces and apply SVD along this dimension. We introduce a novel denoising approach that utilizes a structure-oriented SVD, and this approach can enhance seismic reflections with continuous slopes.
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However, it can only be applied to seismic data with simple structure such that there is only one dip component in each processing window. Singular value decomposition (SVD) can be used both globally and locally to remove random noise in order to improve the signal-to-noise ratio (SNR) of seismic data.