Random noise attenuation by - empirical mode decomposition predictive filtering |

**Yangkang Chen ^{}and Jitao Ma^{}**

John A. and Katherine G. Jackson School of Geosciences

The University of Texas at Austin

University Station, Box X

Austin, TX 78713-8924

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.

- Introduction
*f-x*predictive filtering- Empirical mode decomposition

*f-x*empirical mode decomposition predictive filtering- EMD based dip filter
- Examples
- Conclusions
- Acknowledgements
- Bibliography
- Appendix A: Sifting algorithm for empirical mode decomposition
- About this document ...

Random noise attenuation by - empirical mode decomposition predictive filtering |

2014-08-20