Chapter 12: Eigenimage Processing of Seismic Sections
Tadeusz J. Ulrych, Mauricio D. Sacchi, Sergio L. M. Freire, 1999. "Eigenimage Processing of Seismic Sections", Covariance Analysis for Seismic Signal Processing, R. Lynn Kirlin, William J. Done
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This chapter briefly reviews the important theoretical aspects of eigenimage processing and demonstrates the unique properties of this approach using various examples such as the separation of up and downgoing waves, multiple attenuation, and residual static correction. In particular, we will compare the eigenimage technique to the well-known frequency-wave number f-k, method, (Treitel et al., 1967), and discuss important differences which arise especially with respect to spacial aliasing and the separation of signal and noise.
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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.