Matrix probing: A randomized preconditioner for the wave-equation Hessian
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Author(s)
Demanet, Laurent
Letourneau, Pierre-David
Boumal, Nicolas
Calandra, Henri
Chiu, Jiawei
Snelson, Stanley
Date Issued
March 2011
Journal
Applied and Computational Harmonic Analysis
Publisher
Elsevier
Citation
Demanet, Laurent, Pierre-David Letourneau, Nicolas Boumal, Henri Calandra, Jiawei Chiu, and Stanley Snelson. “Matrix Probing: A Randomized Preconditioner for the Wave-Equation Hessian.” Applied and Computational Harmonic Analysis 32, no. 2 (March 2012): 155–68.
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Author's final manuscript
Abstract
This paper considers the problem of approximating the inverse of the wave-equation Hessian, also called normal operator, in seismology and other types of wave-based imaging. An expansion scheme for the pseudodifferential symbol of the inverse Hessian is set up. The coefficients in this expansion are found via least-squares fitting from a certain number of applications of the normal operator on adequate randomized trial functions built in curvelet space. It is found that the number of parameters that can be fitted increases with the amount of information present in the trial functions, with high probability. Once an approximate inverse Hessian is available, application to an image of the model can be done in very low complexity. Numerical experiments show that randomized operator fitting offers a compelling preconditioner for the linearized seismic inversion problem.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1016/j.acha.2011.03.006
