A hybrid stocahstic-deterministic method for waveform inversion

TitleA hybrid stocahstic-deterministic method for waveform inversion
Publication TypeConference
Year of Publication2011
AuthorsTristan van Leeuwen, Mark Schmidt, Michael P. Friedlander, Felix J. Herrmann
Conference NameAMP
Month07
OrganizationWAVES 2011
KeywordsPresentation
Abstract

A lot of seismic and medical imaging problems can be written as a least-squares data- fitting problem. In particular, we consider the case of multi-experiment data, where the data consists of a large number of "independent" measurements. Solving the inverse problem then involves repeatedly forward modeling the data for each of these experiments. In case the number of experiments is large and the modeling kernel expensive to apply, such an approach may be prohibitively expensive. We review techniques from stochastic optimization which aim at dramatically reducing the number of experiments that need to be modeled at each iteration. This reduction is typically achieved by randomly subsampling the data. Special care needs to be taken in the optimization to deal with the stochasticity that is introduced in this way.

Notes

Presented at AMP Medical and Seismic Imaging, 2011, Vancouver BC

URLhttps://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/vanleeuwen2011AMPhsdmwi/vanleeuwen2011AMPhsdmwi.pdf
Citation Keyvanleeuwen2011AMPhsdmwi