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empirical mode decomposition predictive filtering 
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empirical mode decomposition predictive filtering | |
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empirical mode decomposition predictive filtering Yangkang Chen
and Jitao Ma
Bureau of Economic Geology
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, TX 78713-8924
State Key Laboratory of Petroleum Resources and Prospecting
China University of Petroleum
Fuxue Road 18th
Beijing, China, 102200
Random noise attenuation always plays an important role in seismic data processing. One of the most widely used methods for suppressing random noise is 
predictive filtering. When the subsurface structure becomes complex, this method suffers from higher prediction errors owing to the large number of different dip components that need to be predicted. In this paper, we propose a novel denoising method termed
empirical mode decomposition predictive filtering (EMDPF). This new scheme solves the problem that makes
empirical mode decomposition (EMD) ineffective with complex seismic data. Also, by making the prediction more precise, the new scheme removes the limitation of conventional
predictive filtering when dealing with multi-dip seismic profiles. In this new method, we first apply EMD to each frequency slice in the
domain and obtain several intrinsic mode functions (IMF). Then an auto-regressive (AR) model is applied to the sum of the first few IMFs, which contain the high-dip-angle components, in order to predict the useful steeper events. Finally, the predicted events are added to the sum of the remaining IMFs. This process improves the prediction precision by utilizing an EMD based dip filter to reduce the dip components before
predictive filtering. Both synthetic and real data sets demonstrate the performance of our proposed method in preserving more useful energy.
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empirical mode decomposition predictive filtering | |
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