Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization |

**Yangkang Chen, Sergey Fomel, and Jingwei Hu**

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

We introduce a novel iterative estimation scheme for separation of blended seismic data from simultaneous sources. The scheme is based on an augmented estimation problem, which can be solved by iteratively constraining the deblended data using shaping regularization in the seislet domain. We formulate the forward modeling operator in the common receiver domain, where two sources are assumed to be blended using a random time-shift dithering approach. The nonlinear shaping-regularization framework offers some freedom in designing a shaping operator to constrain the model in an underdetermined inverse problem. We design the backward operator and the shaping operator for the shaping regularization framework. The backward operator can be optimally chosen as a half of the identity operator in the two-source case, and the shaping operator can be chosen as coherency-promoting operator. Three numerically blended synthetic datasets and one numerically blended field dataset demonstrate the high-performance deblending effect of the proposed iterative framework. Compared with alternative
domain thresholding and
predictive filtering, seislet-domain soft thresholding exhibits the most robust behavior.

- Introduction
- Deblending using shaping regularization

- Backward operator
- Shaping operator

- Examples
- Numerically blended synthetic data - linear events
- Numerically blended synthetic data - hyperbolic events
- Numerically blended synthetic data - complex gather
- Numerically blended field data

- Conclusions
- Acknowledgments
- Bibliography
- About this document ...

Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization |

2014-08-20