@conference {moldoveanu2018SEGcsbdl,
	title = {Compressive sensing based design for land and OBS surveys: The noise issue},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {102-106},
	abstract = {Two important developments in seismic acquisition in the last decade were introduction of simultaneous shooting and reconstruction via inversion of the complete seismic wavefield. As the seismic wavefield is typically under sampled in at least one of the four spatial coordinates, both developments could contribute to improve the sampling and, in addition, to increase acquisition efficiency. Herein, we look at the impact of seismic noise when simultaneous shooting and seismic wavefield reconstruction are implemented for a land or ocean bottom seismic survey.},
	keywords = {SEG},
	doi = {10.1190/segam2018-2997759.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/moldoveanu2018SEGcsbdl/moldoveanu2018SEGcsbdl.pdf},
	author = {Nick Moldoveanu and Philip Bilsby and John Quigley and Rajiv Kumar and Felix J. Herrmann}
}
@conference {bougher2016SEGava,
	title = {AVA classification as an unsupervised machine-learning problem},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {553-556},
	abstract = {Much of AVA analysis relies on characterizing background trends and anomalies in pre-stack seismic data. Analysts reduce a seismic section into a small number of these trends and anomalies, suggesting that a low-dimensional structure can be inferred from the data. We describe AVA-attribute characterization as an unsupervised-learning problem, where AVA classes are learned directly from the data without any prior assumptions on physics and geological settings. The method is demonstrated on the Marmousi II elastic model, where a gas reservoir was successfully delineated from a background trend in a depth migrated image.},
	keywords = {AVA, machine learning, SEG},
	doi = {10.1190/segam2016-13874419.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/bougher2016SEGava/bougher2016SEGava.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/bougher2016SEGava/bougher2016SEGava_pres.pdf},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@conference {aravkin2012SEGST,
	title = {On non-uniqueness of the Student{\textquoteright}s t-formulation for linear inverse problems},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {31},
	year = {2012},
	month = {11},
	pages = {1-5},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We review the statistical interpretation of inverse problem formulations, and the motivations for selecting non-convex penalties for robust behaviour with respect to measurement outliers or artifacts in the data. An important downside of using non-convex formulations such as the Student{\textquoteright}s t is the potential for non-uniqueness, and we present a simple example where the Student{\textquoteright}s t penalty can be made to have many local minima by appropriately selecting the degrees of freedom parameter. On the other hand, the non-convexity of the Student{\textquoteright}s t is precisely what gives it the ability to ignore artifacts in the data. We explain this idea, and present a stylized imaging experiment, where the Student{\textquoteright}s t is able to recover a velocity perturbation from data contaminated by a very peculiar artifact {\textendash}- data from a different velocity perturbation. The performance of Student{\textquoteright}s t inversion is investigated empirically for different values of the degrees of freedom parameter, and different initial conditions.},
	keywords = {non-convex, robust, SEG, Student{\textquoteright}s t, uniqueness},
	doi = {10.1190/segam2012-1558.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2012/aravkin2012SEGST/aravkin2012SEGST.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and Kenneth Bube and Felix J. Herrmann}
}
@conference {moghaddam2008SEGcbm,
	title = {Curvelet-based migration preconditioning},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2211-2215},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we introduce a preconditioner for seismic imaging{\textendash}-i.e., the inversion of the linearized Born scattering operator. This preconditioner approximately corrects for the {\textquoteleft}{\textquoteleft}square root{\textquoteright}{\textquoteright} of the normal{\textendash}-i.e., the demigration-migration operator. This approach consists of three parts, namely (i) a left preconditoner, defined by a fractional time integration designed to make the migration operator zero order, and two right preconditioners that apply (ii) a scaling in the physical domain accounting for a spherical spreading, and (iii) a curvelet-domain scaling that corrects for spatial and reflector-dip dependent amplitude errors. We show that a combination of these preconditioners lead to a significant improvement of the convergence for iterative least-squares solutions to the seismic imaging problem based on reverse-time migration operators.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059325},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm_pres.pdf},
	author = {Peyman P. Moghaddam and Cody R. Brown and Felix J. Herrmann}
}
@conference {yarham2006SEGcgrr,
	title = {Curvelet-based ground roll removal},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {25},
	year = {2006},
	pages = {2777-2782},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We have effectively identified and removed ground roll through a two-step process. The first step is to identify the major components of the ground roll through various methods including multiscale separation, directional or frequency filtering or by any other method that identifies the ground roll. Given this estimate for ground roll, the recorded signal is separated during the second step through a block-coordinate relaxation method that seeks the sparsest set for weighted curvelet coefficients of the ground roll and the sought-after reflectivity. The combination of these two methods allows us to separate out the ground roll signal while preserving the reflector information. Since our method is iterative, we have control of the separation process. We successfully tested our algorithm on a real data set with a complex ground roll and reflector structure.},
	keywords = {SEG},
	doi = {10.1190/1.2370101},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/yarham06SEGcgrr/yarham06SEGcgrr.pdf},
	author = {Carson Yarham and Urs Boeniger and Felix J. Herrmann}
}
@conference {beyreuther2005SEGcot,
	title = {Computation of time-lapse differences with 3-D directional frames},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {24},
	year = {2005},
	pages = {2488-2491},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present an alternative method of extracting production related differences from time-lapse seismic data sets. Our method is not based on the actual subtraction of the two data sets, risking the enhancement of noise and introduction of artifacts due to local phase rotation and slightly misaligned events. Rather, it mutes events of the monitor survey with respect to the baseline survey based on the magnitudes of coefficients in a sparse and local atomic decomposition. Our technique is demonstrated to be an effective tool for enhancing the time-lapse signal from surveys which have been cross-equalized. \copyright2005 Society of Exploration Geophysicists},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.2148227},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2005/Beyreuther05SEGcot/Beyreuther05SEGcot.pdf},
	author = {Moritz Beyreuther and Jamin Cristall and Felix J. Herrmann}
}
