Simultaneous seismic data interpolation and denoising using SVD-free low-rank matrix factorization.

This applications is available only in the software release for members of SINBAD consortium.

This software provides an algorithm for simultaneous seismic data interpolation and denoising (using Generalized SPGl1 as solver). The algorithm solves the system in parallel over frequencies. The missing trace interpolation and denoising is done using robust-rank regularized formulation. We illustrate the advantages of the new approach using a seismic line from Gulf of Suez.

Author: Rajiv Kumar (rakumar@eos.ubc.ca)

Date: April,2013

Contents

Downloading & Dependencies

The synthetic examples code can be found in the SLIM software release under applications/Processing/LowRankInterpolationAndDenoising.

The code has been tested with Matlab R2012b and require the Parallel Computing Toolbox.

This code uses the following packages, also found in the tools part of the SLIM software release.

Running & Parallelism

All the examples and results are produced by the scripts found in this software release under /applications/Processing/LowRankInterpolationAndDenoising/examples/. Start matlab from /applications/Processing/LowRankInterpolationAndDenoising to add the appropriate paths.

To run the scripts follow the instrictions in the README file enclosed with the code.

Functions

The missing trace interpolation and denoising code can be found in tools/algorithms/LowRankMinimization. The main components are listed below

algorithms/LowRankMinimization

Examples and results

An examples of interpolation and denoising can be found in applications/Processing/LowRankInterpolationAndDenoising

Results of missing-trace interpolation and denoising is shown in GofS_Interp.m.

References

[1] A.Y. Aravkin, R. Kumar, H. Mansour, B. Recht, F. J. Herrmann, 2013. An SVD-free Pareto curve approach to rank minimization.

[2] R. Kumar, A.Y. Aravkin, H. Mansour, B. Recht, F. J. Herrmann, 2013. Seismic data interpolation and denoising using SVD-free low-rank matrix factorization, EAGE.

Acknowledgements

Thanks to our sponsors and NSERC for their financial support.