**Shuwei Gan ^{}, Yangkang Chen^{}, Shaohuan Zu ^{}, Shan Qu^{}and Wei Zhong ^{}**

China University of Petroleum

Fuxue Road 18th

Beijing, China, 102200

gsw19900128@126.com

John A. and Katherine G. Jackson School of Geosciences

The University of Texas at Austin

University Station, Box X

Austin, TX 78713-8924

ykchen@utexas.edu

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. However, they can only be applied to seismic data with simple structure such that there is only one dip component in each processing window. We introduce a novel denoising approach that utilizes a structure-oriented SVD and this approach can enhance seismic reflections with continuous slopes. 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. The added dimension is equal to flattening the seismic reflections within a neighbouring window. The third dimension is then averaged to decrease the dimension. We use two synthetic examples with different complexities and one field data example to demonstrate the performance of the proposed structure-oriented SVD. Compared with global and local SVDs, and deconvolution, the structure-oriented SVD can obtain much clearer reflections and preserve more useful energy.

- Introduction
- Theory

- Examples
- Conclusions
- Acknowledgement
- Appendix A: Review of deconvolution
- Bibliography
- About this document ...

2020-03-09