Why the modified Gauss-Newton method?

TitleWhy the modified {Gauss-Newton} method?
Publication TypeSINBAD Presentation
AuthorsXiang Li, Ernie Esser, Felix J. Herrmann
PublisherSINBAD
Year of Publication2014
Abstract

In our earlier work, we develop the modified Gauss-Newton method for FWI, which requires each update of FWI to be sparse in the curvelet domain. Our empirical observation of the MGN method is that it can find us a solution for FWI problem with sparse perturbation of the initial guess without changing the underlying objective. In this talk, we will analyze the MGN method to find out the reason why it can generate a sparse perturbation of the initial model, because sum of spares updates could easily generate non-sparse perturbations. Moreover, we will illustrate when do we expect the modified Gauss-Newton method to yield a solution with sparse perturbation and in what circumstances should we use it in place of other algorithms like standard Gauss-Newton.

KeywordsPresentation, private, SINBAD, SINBADFALL2014, SLIM
URLhttps://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2014/Fall/li2014SINBADwmg/li2014SINBADwmg.pdf
Citation Keyli2014SINBADwmg