Sparsity-promoting denoising of seismic data
This application is available only in the software release for members of SINBAD consortium.
This package contains a MATLAB implementation of sparsity-promoting denoising of seismic data in the curvelet domain using one-norm minimization. For details, please see Reference [1].
Author: Haneet Wason (hwason@eos.ubc.ca)
Date: December, 2015
Contents
Downloading & Dependencies
The code can be found in the SLIM sofware release under /applications/Processing/SparsityPromotingDenoising/.
The code has been tested with Matlab R2014b.
This code uses the following packages, also found in the tools part of the SLIM software release.
- utilities/SPOT - object oriented framework for matrix-free linear algebra.
- utilities/SegyMAT - Matlab/Octave toolbox for reading and writing SEG-Y formatted files.
- solvers/SPGL1-SLIM - SLIM version of SPGL1 solver.
- transforms/CurveLab-2.1.2-SLIM - curvelet transform functions.
Running
All the examples and results are produced by the scripts found in this software release under applications/Processing/SparsityPromotingDenoising/. Start matlab from that directory or run startup in that directory to add the appropriate paths.
To run the scripts follow the instructions in the README file enclosed with the code. The scripts are run in serial mode.
Examples and results
Examples of denoising frequency slices (extracted from a 3D seismic data cube) are shown here.
References
[1] SLIM's research webpage on processing. See the 'Denoising' section (https://www.slim.eos.ubc.ca/research/processing#denoising-via-hybrid-support-identification-and-projection).
[2] Felix J. Herrmann, Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Xiang Li, Brendan Smithyman, Eric Takam Takougang, and Haneet Wason [2013].
Acknowledgements
Thanks to our sponsors and NSERC for their financial support.