Amplitude vs. angle analysis as an unsupervised learning problem

TitleAmplitude vs. angle analysis as an unsupervised learning problem
Publication TypeSINBAD Presentation
AuthorsBen B. Bougher, Felix J. Herrmann
PublisherSINBAD
Year of Publication2016
Abstract

Amplitude vs. angle analysis (AVA) of pre-stack seismic data is a commonly used method for inferring petrophysical information from seismic data. Conventionally, a two-term linearized rock physics model (Shuey equation) is used to invert angle-domain common-image gathers. Multivariate analysis of the inverted terms leads to a background of siliciclastic interfaces, where outlying points are associated with hydrocarbon saturated sands. The acquisition and processing of seismic data does not result in highly-calibrated measurements that adhere to the rock physics model, which often inhibits the success of AVA analysis. We offer an alternative approach that uses PCA-based methods to learn projections directly from the data without the need of a physical model. Results on synthetic and field data show that PCA-based projections can improve segmentation of potential reservoirs in seismic data.

KeywordsPresentation, private, SINBAD, SINBADFALL2016, SLIM
URLhttps://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.pdf
URL1

https://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2016...

Citation Keybougher2016SINBADFaaa