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![]() | Random noise attenuation by a selective hybrid approach using f-x empirical mode decomposition | ![]() |
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We can retrieve the useful dipping events by applying another denoising operator onto the noise section,
The denoising operator
in equation 5 can be chosen as
predictive filtering (Chen and Ma, 2014), wavelet transform (Chen et al., 2012), or curvelet transform (Dong et al., 2013). Thus, equation 5 becomes a general framework for all those
EMD based random noise attenuation approaches. To extend its generality, we propose to use
SSA (Oropeza and Sacchi, 2011) as
in this paper.
The effectiveness of the novel approach can be ascribed to the strong horizontal-preservation ability of
EMD. When most of the useful horizontal energy is preserved after
EMD, we turn to deal with less number of plane-wave components in the noise section, which is much easier because less signal components usually correspond to a more strict control over the random noise, e.g., smaller prediction length in
predictive filtering (Canales, 1984) and lower rank for extracting useful components in
SSA (Oropeza and Sacchi, 2011). For example, lower rank corresponds to less-serious rank-mixing problem, that is , less amount of random noise can be leaked into the denoised section.
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![]() | Random noise attenuation by a selective hybrid approach using f-x empirical mode decomposition | ![]() |
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