@conference {vanleeuwen2013EAGErobustFWI,
	title = {In which domain should we measure the misfit for robust full waveform inversion?},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {Full-waveform inversion relies on minimizing the difference between observed and modeled data, as measured by some penalty function. A popular choice, of course, is the least-squares penalty. However, when outliers are present in the data, the use of robust penalties such as the Huber or Student{\textquoteright}s t may significantly improve the results since they put relatively less weight on large residuals. In order for robust penalties to be effective, the outliers must be somehow localized and distinguishable from the good data. We propose to first transform the residual into a domain where the outliers are localized before measuring the misfit with a robust penalty. This is exactly how one would normally devise filters to remove the noise before applying conventional FWI. We propose to merge the two steps and let the inversion process implicitly filter out the noise. Results on a synthetic dataset show the effectiveness of the approach.},
	keywords = {EAGE, full waveform inversion},
	doi = {10.3997/2214-4609.20130839},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/vanleeuwen2013EAGErobustFWI/vanleeuwen2013EAGErobustFWI.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/vanleeuwen2013EAGErobustFWI/vanleeuwen2013EAGErobustFWI_pres.pdf},
	author = {Tristan van Leeuwen and Aleksandr Y. Aravkin and Henri Calandra and Felix J. Herrmann}
}
@conference {aravkin2012EAGErobust,
	title = {Source estimation for frequency-domain FWI with robust penalties},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Source estimation is an essential component of full waveform inversion. In the standard frequency domain formulation, there is closed form solution for the the optimal source weights, which can thus be cheaply estimated on the fly. A growing body of work underscores the importance of robust modeling for data with large outliers or artifacts that are not captured by the forward model. Effectively, the least-squares penalty on the residual is replaced by a robust penalty, such as Huber, Hybrid {\textquoteleft}1-{\textquoteleft}2 or Student{\textquoteright}s t. As we will demonstrate, it is essential to use the same robust penalty for source estimation. In this abstract, we present a general approach to robust waveform inversion with robust source estimation. In this general formulation, there is no closed form solution for the optimal source weights so we need to solve a scalar optimization problem to obtain these weights. We can efficiently solve this optimization problem with a Newton-like method in a few iterations. The computational cost involved is of the same order as the usual least-squares source estimation procedure. We show numerical examples illustrating robust source estimation and robust waveform inversion on synthetic data with outliers.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/aravkin2012EAGErobust/aravkin2012EAGErobust.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/aravkin2012EAGErobust/aravkin2012EAGErobust_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59196},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and Henri Calandra and Felix J. Herrmann}
}
@conference {beyreuther2004EAGEcdo,
	title = {Curvelet denoising of 4-D seismic},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {With burgeoning world demand and a limited rate of discovery of new reserves, there is increasing impetus upon the industry to optimize recovery from already existing fields. 4D, or time-lapse, seismic imaging is an emerging technology that holds great promise to better monitor and optimise reservoir production. The basic idea behind 4D seismic is that when multiple 3D surveys are acquired at separate calendar times over a producing field, the reservoir geology will not change from survey to survey but the state of the reservoir fluids will change. Thus, taking the difference between two 3D surveys should remove the static geologic contribution to the data and isolate the time- varying fluid flow component. However, a major challenge in 4D seismic is that acquisition and processing differences between 3D surveys often overshadow the changes caused by fluid flow. This problem is compounded when 4D effects are sought to be derived from vintage 3D data sets that were not originally acquired with 4D in mind. The goal of this study is to remove the acquisition and imaging artefacts from a 4D seismic difference cube using Curveket processing techniques.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Beyreuther04EAGEcdo/beyreuther2004EAGEcdo_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Beyreuther04EAGEcdo/Beyreuther04EAGEcdo_pres.pdf},
	url2 = {https://circle.ubc.ca/bitstream/handle/2429/453/EAGE4D2004.pdf?sequence=1},
	author = {Moritz Beyreuther and Felix J. Herrmann and Jamin Cristall}
}
