@presentation {dasilva2015SINBADFsss,
	title = {Scaling SINBAD software to 3-D on Yemoja},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present early results on the scalability of SINBAD{\textquoteright}s wavefield reconstruction and wave-equation based inversion technologies on Yemoja, a 17k core cluster made available to us by BG Group at SENAI CIMATEC Supercomputing Centre in Brazil.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.mov},
	author = {Curt Da Silva and Haneet Wason and Mathias Louboutin and Bas Peters and Shashin Sharan and Zhilong Fang and Felix J. Herrmann}
}
@conference {peters2015SEGWSsew,
	title = {Simultaneous estimation of wavefields and medium parameters - reduced-space versus full-space waveform inversion},
	booktitle = {SEG Workshop on The Limit of FWI in Subsurface Parameter Recovery; New Orleans},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	keywords = {full-space, PDE-constrained optimization, quadratic penalty method, SEG, waveform inversion, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/peters2015SEGWSsew/peters2015SEGWSsew_pres.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {peters2015SINBADswr,
	title = {Solving WRI{\textquoteright}s data-augmented wave equation in 3D},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The Wavefield Reconstruction Inversion (WRI) approach to seismic inversion is a topic of active research in the SLIM group. Several variants have been proposed, all of which rely on the solution of a linear least-squares problem with a Helmholtz discretization. While algorithms based on LU or QR factorization are very efficient for 2D problems, 3D problems required the development of a factorization-free iterative method. This talk introduces the current version of the algorithm, which requires significantly less memory and computation then a few months ago and uses existing Helmholtz solvers as an important building block.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADswr/peters2015SINBADswr.pdf},
	author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}
@presentation {peters2014SINBADsmp,
	title = {Single- and multi-parameter WRI {\textendash}- synthetic examples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We demonstrate the performance of WRI on a number of single- and multi-parameter synthetic examples. The examples shot that WRI is less reliant on accurate starting models while this formulation is also conducive to multi-parameter inversion.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADsmp/peters2014SINBADsmp.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {fang2014SINBADsew,
	title = {Source estimation for WRI and its application},
	year = {2014},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADsew/fang2014SINBADsew.pdf},
	author = {Zhilong Fang and Xiang Li and Bas Peters and Felix J. Herrmann}
}
@conference {peters2014SEGsrh,
	title = {A sparse reduced Hessian approximation for multi-parameter wavefield reconstruction inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {1206-1210},
	abstract = {Multi-Parameter full-waveform inversion is a challenging problem, because the unknown parameters appear in the same wave equation and the magnitude of the parameters can vary many orders of magnitude. This makes accurate estimation of multiple-parameters very difficult. To mitigate the problems, sequential strategies, regularization methods and scalings of gradients and quasi-Newton Hessians have been proposed. All of these require design, fine-tuning and adaptation to different waveform inversion problems. We propose to use a sparse approximation to the Hessian derived from a penalty-formulation of the objective function. Sparseness allows to have the Hessian in memory and compute update directions at very low cost. This results in decent reconstruction of the multiple parameters at very low additional memory and computational expense.},
	keywords = {Full-waveform inversion, Hessian, Optimization, penalty method, SEG},
	doi = {http://dx.doi.org/10.1190/segam2014-1667.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/peters2014SEGsrh/peters2014SEGsrh.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/peters2014SEGsrh/peters2014SEGsrh_pres.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {vanleeuwen2013SINBADsda,
	title = {Solving the data-augmented wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The recently proposed penalty method promises to mitigate some of the non-linearity inherent in full-waveform inversion by relaxing the requirement that the wave-equation needs to be solved exactly. The basic workflow of this new method is as follows; i) solve an overdetermined wave-equation (the data-augmented wave-equation), where the data serves as additional constraints for the wavefields, ii) compute the wavefield-residual by substituting this wavefield in the wave-equation, and iii) correlate the wavefield with the wavefield-residual to obtain a model-update. As opposed to the conventional workflow, no explicit adjoint solve is needed to compute the model-update. However, instead of solving a wave-equation, we need to solve a data-augmented wave-equation. In this talk we explore some of the challenges of solving this data-augmented wave-equation and review some possible solution strategies for both time and frequency-domain applications.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADsda/vanleeuwen2013SINBADsda.pdf},
	author = {Tristan van Leeuwen and Bas Peters and Felix J. Herrmann}
}
@presentation {fang2013SINBADsfwi,
	title = {Swift FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In 3D case, there is much more data and modeling is much more expensive. As a result, parallel computing is very important for 3D full-waveform inversion. Both domain decomposition and data decomposition need a large number of parallel computing resources. However, programs based on parallel MATLAB suffer from the limitation of licenses. In order to obtain a MATLAB licenses free solution, we use SWIFT, which is a fast and easy parallel scripting language. Once the original Mat file is complied to an executable file, SWIFT can run the code inside the executable file in parallel without using parallel MATLAB. We use the SWIFT to compute the object functions and gradients of different shots in parallel, and test the parallel 3D FWI code with overthrust data. This is joint work with Thomas Lai, Harsh Juneja, Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADsfwi/fang2013SINBADsfwi.pdf},
	author = {Zhilong Fang and Henryk Modzelewski and Tristan van Leeuwen and Thomas Lai and Harsh Juneja and Bas Peters and Felix J. Herrmann}
}
