@presentation {li2013SINBADmsd,
	title = {Model-space versus data-space FWI with the acoustic wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Inverting data with elastic phases using an acoustic wave equation can lead to erroneous results, especially when the number of iterations is too high, which may lead to over fitting the data. Several approaches have been proposed to address this issue. Most commonly, people data- filtering operations that are aimed to deemphasize the elastic phases in the data in favor of the acoustic phases. Examples of this approach are nested loops over offset range and Laplace parameters. In this presentation, we discuss two complementary optimization-driven methods where the minimization process decides adaptively which of the data or model components are consistent with the objective. Specifically, we compare the s t misfit function as the data-space alternative and curvelet-domain sparsity promotion as the model-space alternative. Application of these two methods to a realistic synthetic lead to comparable results that we believe can be improved by combining these two methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADmsd/li2013SINBADmsd.pdf},
	author = {Xiang Li and Anais Tamalet and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {tamalet2012SINBADvpe,
	title = {Variance parameters estimation - application to full waveform inversion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Many inverse problems include nuisance parameters. While not of direct interest, these parameters are required to recover primary parameters. In order to estimate these nuisance parameters as well as the primary parameters in large-scale inverse problems, a method based on variable projection, which consists in projecting out a subset over the variables, has been developed. We present here the application of this method to the problem of variance parameters estimation in multiple datasets, which is an important problem in many areas including geophysics. More precisely, we apply the method to Full Waveform Inversion and demonstrate the improvement in recovery of the model parameters in the case where the variance of the noise increases with the frequency.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/tamalet2012SINBADvpe/tamalet2012SINBADvpe_pres.pdf},
	author = {Anais Tamalet}
}
