@conference {leeuwen2014EAGEntf,
	title = {A new take on FWI: wavefield reconstruction inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {We discuss a recently proposed novel method for waveform inversion: Wavefield Reconstruction Inversion (WRI). As opposed to conventional FWI {\textendash} which attempts to minimize the error between observed and predicted data obtained by solving a wave equation {\textendash} WRI reconstructs a wave-field from the data and extracts a model-update from this wavefield by minimizing the wave-equation residual. The method does not require explicit computation of an adjoint wavefield as all the necessary information is contained in the reconstructed wavefield. We show how the corresponding model updates can be interpreted physically analogously to the conventional imaging-condition-based approach.},
	keywords = {EAGE, Full-waveform inversion, Optimization, penalty method, Wavefield Reconstruction Inversion},
	doi = {10.3997/2214-4609.20140703},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/leeuwen2014EAGEntf/leeuwen2014EAGEntf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/leeuwen2014EAGEntf/leeuwen2014EAGEntf_pres.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann and Bas Peters}
}
@conference {aravkin2011EAGEnspf,
	title = {A nonlinear sparsity promoting formulation and algorithm for full waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Full Waveform Inversion (FWI) is a computational procedure to extract medium parameters from seismic data. FWI is typically formulated as a nonlinear least squares optimization problem, and various regularization techniques are used to guide the optimization because the problem is illposed. In this paper, we propose a novel sparse regularization which exploits the ability of curvelets to efficiently represent geophysical images. We then formulate a corresponding sparsity promoting constrained optimization problem, which we call Nonlinear Basis Pursuit Denoise (NBPDN) and present an algorithm to solve this problem to recover medium parameters. The utility of the NBPDN formulation and efficacy of the algorithm are demonstrated on a stylized cross-well experiment, where a sparse velocity perturbation is recovered with higher quality than the standard FWI formulation (solved with LBFGS). The NBPDN formulation and algorithm can recover the sparse perturbation even when the data volume is compressed to 5 \% of the original size using random superposition.},
	keywords = {EAGE, Full-waveform inversion, Optimization},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/aravkin11EAGEnspf/aravkin11EAGEnspf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/aravkin11EAGEnspf/aravkin11EAGEnspf_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50199},
	author = {Aleksandr Y. Aravkin and James V. Burke and Felix J. Herrmann and Tristan van Leeuwen}
}
@conference {yarham2007EAGEnsw,
	title = {Nonlinear surface wave prediction and separation},
	booktitle = {EAGE 2007},
	year = {2007},
	abstract = {Removal of surface waves is an integral step in seismic processing. There are many standard techniques for removal of this type of coherent noise, such as f-k filtering, but these methods are not always effective. One of the common problems with removal of surface waves is that they tend to be aliased in the frequency domain. This can make removal difficult and affect the frequency content of the reflector signals, as this signals will not be completely separated. As seen in (Hennenfent, G. and F. Herrmann, 2006, Application of stable signal recovery to seismic interpolation) interpolation can be used effectively to resample the seismic record thus dealiasing the surface waves. This separates the signals in the frequency domain allowing for a more precise and complete removal. The use of this technique with curvelet based surface wave predictions and an iterative L1 separation scheme can be used to remove surface waves from shot records more completely that with standard techniques.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/yarham2007EAGEnsw/yarham2007EAGEnsw.pdf},
	author = {Carson Yarham}
}
