Brian N. Fuller, 1999. "Principal Component Methods for Suppressing Noise and Detecting Subtle Reflection Character Variations", Covariance Analysis for Seismic Signal Processing, R. Lynn Kirlin, William J. Done
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Seismic interpreters are sometimes presented with the challenge of identifying small lateral reflection character changes that can indicate significant lithologic variations. Examples of this occur in stratigraphic trap exploration and in estimating the lateral extent of a reservoir. Standard seismic trace plots, even in color, sometimes do not have sufficient dynamic range to show a small waveform variation against a background of traces that are very similar to one another. Additionally, noise can obscure subtle waveform variations.
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Covariance Analysis for Seismic Signal Processing
This reference is intended to give the geophysical signal analyst sufficient material to understand the usefulness of data covariance matrix analysis in the processing of geophysical signals. A background of basic linear algebra, statistics, and fundamental random signal analysis is assumed. This reference is unique in that the data vector covariance matrix is used throughout. Rather than dealing with only one seismic data processing problem and presenting several methods, we will concentrate on only one fundamental methodology—analysis of the sample covariance matrix—and we present many seismic data problems to which the methodology applies.