Large scale, parallel low rank matrix completion for seismic data interpolation
This applications is available only in the software release for members of SINBAD consortium.
This software implements the SPGLR algorithm using block parallel matrix-matrix multiplication, which allows it to be used on large scale problems.
Author: Curt Da Silva (curtd@math.ubc.ca)
Date: February, 2015
Contents
Downloading & Dependencies
The synthetic examples code can be found in the SLIM software release under applications/Processing/LargeScaleLRI.
The code has been tested with Matlab R2013a and requires the Parallel Computing Toolbox.
This code uses the following packages, also found in the tools part of the SLIM software release.
- _utilities/SPOT-SLIM - object oriented framework for matrix-free linear algebra.
- _utilities/functions/misc - miscellaneous functions, for plotting, experiment organization, etc.
- _tools/solvers/SPGLR_PAR - parallel SPGLR package
Running & Parallelism
All of the examples and results are produced by the scripts found in this software release under /applications/Processing/LargeScaleLRI/examples. Start matlab from /applications/Processing/LargeScaleLRI to add the appropriate paths.
To run the scripts, follow the instructions in the README.md file enclosed with the code
Functions
The SPGLR code consists of the spgLR.m function _tools/solvers/SPGLR_PAR, which performs the parameter-cooling method by solving the Lasso subproblems, and spgLRobj.m, which performs the distributed computation of the objective and its gradient. The accompanying documentation is in the README.md file.
Examples and results
The examples of large scale missing trace interpolation using these methods can be found in applications/Processing/LargeScaleLRI/examples
Results are of missing-receiver interpolation is shown in spgLR_bgdata_view.m
References
[1] A. Aravkin, et al. "Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation"
Acknowledgements
Thanks to our sponsors and NSERC for their financial support.