Source separation for towed-streamer marine data via sparsity promotion

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

This package contains a MATLAB implementation of a 2-D over/under blended marine acquisition scheme, and a deblending (or source separation) algorithm based on sparsity-promoting inversion in the curvelet domain using L1 minimization.

Author: Haneet Wason (hwason@eos.ubc.ca)

Date: July, 2016

Contents

Downloading & Dependencies

The code can be found in the SLIM sofware release under applications/Acquisition/SourceSeparationL1.

The code has been tested with Matlab R2015b.

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

The CurveLab package is downloaded from the tools part of the SLIM-release-comp repository. * transforms/CurveLab-2.1.2-SLIM - curvelet transform functions.

Functions

Some functions specific to this package can be found in the applications/Acquisition/SourceSeparationL1/misc_funcs directory.

Running & Parallelism

All the examples and results are produced by the scripts found in this software release under applications/Acquisition/SourceSeparationL1. 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 under applications/Acquisition/SourceSeparationL1/examples. The scripts are run in serial mode but can also be run in parallel with slight modifications to the code. Please see the README file.

Read more about SLIM's approach to parallel computing in Matlab.

Examples and results

Examples and results are shown here. Scripts to reproduce the results can be found under applications/Acquisition/SourceSeparationL1/examples.

References

[1] Rajiv Kumar, Haneet Wason, and Felix J. Herrmann [2015]. Source separation for simultaneous towed-streamer marine acquisition --- a compressed sensing approach, Geophysics, vol. 80, pp. WD73-WD88.

Acknowledgements

Thanks to our sponsors and NSERC for their financial support.