Amplitude vs. angle analysis as an unsupervised learning problem

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.

https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall...