@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}
}
