AVA classification as an unsupervised machine-learning problem

TitleAVA classification as an unsupervised machine-learning problem
Publication TypeConference
Year of Publication2016
AuthorsBen B. Bougher, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Page553-556
Month10
KeywordsAVA, machine learning, SEG
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.

Notes

(SEG, Dallas)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2016/bougher2016SEGava/bougher2016SEGava.html
DOI10.1190/segam2016-13874419.1
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2016/bou...

Citation Keybougher2016SEGava