@presentation {dasilva2016SINBADFuse,
	title = {A unified 2D/3D software environment for large scale time-harmonic full waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Full Waveform Inversion is a costly and complex procedure, resulting in industrial codebases that are often written entirely in low-level languages, making them hard to understand, maintain, improve, and extend. In this work, we propose a software framework for organizing a 3D FWI environment that helps mitigate design complexities inherent in the problem in a straightforward way while delegating the performance-critical portions to low level languages to maintain efficiency. The result is a software framework that has a unified interface for 2D and 3D and is flexible, efficient, scalable, and demonstrably correct. We demonstrate the effectiveness of this approach on a large scale 3D FWI example.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.mov},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {fang2015SINBADFuqw,
	title = {Uncertainty quantification for Wavefield-Reconstruction Inversion using a positive-definite approximated Hessian},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We analyze the Hessian of wavefield reconstruction inversion (WRI) and propose a new approximated Hessian. Instead of requiring PDE solves, the matrix-vector multiplication action of the approximate Hessian can be achieved with several matrix-vector multiplications. The diagonal part of the approximated Hessian can be also calculated without additional PDE solves. We apply this approximated Hessian to uncertainty quantification and obtain statistical parameters such as standard deviation and confidence interval. Numerical example illustrate the accuracy of the estimated Hessian and the feasibility of the method to quantify uncertainties.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.mov},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@presentation {lopez2015SINBADFumc,
	title = {Universal matrix completion: applications to seismic data acquisition},
	year = {2015},
	publisher = {SINBAD},
	abstract = {This talk discusses the potential applications of universal matrix completion for infill management and acquisition design. We consider recent developments in the theory of matrix completion that produce practical tools to quantify how successful a given sub sampling mask will be for seismic trace interpolation via nuclear norm minimization. This approach provides an on the fly infill management system as well as instruments to design acquisition schemes that favor interpolation via rank penalization techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.mov},
	author = {Oscar Lopez and Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {fang2014SINBADuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses the uncertainty quantification for the wavefield reconstruction inversion. The Hessian and gradient of the WRI can be easily obtained once the wave field is generated. As a result, we use a Newton type McMC method to sample the posterior distribution. Statistical property such as mean, standard deviation, confidence interval are calculated based on the sampled distribution.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADuqw/fang2014SINBADuqw.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@presentation {oghenekohwo2014SINBADuwc,
	title = {Use what{\textquoteleft}s in common: time-lapse FWI with distributed Compressive Sensing},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will extend our joint recovery formulation to FWI in time-lapse seismic, using the modified Gauss-Newton approach. Leveraging ideas from distributed compressive sensing, we exploit shared information in the sparse model perturbations of the baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADuwc/oghenekohwo2014SINBADuwc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@presentation {fang2013SINBADuafwi,
	title = {Uncertainty analysis for FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Uncertainty analysis is important for seismic interpretation. Based on the framework of Bayesian, we can analyse different statistic parameters of our FWI result. However, directly sampling the posterior probability density function (pdf) is computationally intractable. In order to make this problem computationally tractable, in this work, we use Gaussian distribution approximation and low rank approximation to generate the posterior pdf. Simultaneous shots are also used to reduce the computational costs.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADuafwi/fang2013SINBADuafwi.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {ghadermarzy2013SINBADups,
	title = {Using prior support information in approximate message passing algorithms},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Consider the standard compressed sensing problem. We want to recover sparse compressible aorsignal from few linear measurements. In this talk we investigate recovery performance when we have prior information about the support, i.e., the indices of the non-zero entries, of the signal to be recovered. First we briefly review the results of "weighted $\ell_p$ minimization algorithm with p = 1 and 0 < p < 1". Then we derive a weighted approximate message passing (AMP) algorithm which incorporates prior support information into the AMP algorithm. We empirically show that this algorithm recovers sparse signals significantly faster than weighted $\ell_1$ minimization. We also introduce a reweighting scheme for AMP and weighted AMP which, we observe, substantially improves the recovery conditions of these algorithms. We illustrate our results with extensive numerical experiments on synthetic data and seismic data reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/ghadermarzy2013SINBADups/ghadermarzy2013SINBADups.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
