Next:
Multidimensional autoregression
Up:
Table of Contents
Multidimensional autoregression
Time domain versus frequency domain
SOURCE WAVEFORM, MULTIPLE REFLECTIONS
TIME-SERIES AUTOREGRESSION
PREDICTION-ERROR FILTER OUTPUT IS WHITE
The relationship between spectrum and PEF
Undoing convolution in nature
Causal with causal inverse
PEF output tends to whiteness
Spectral estimation
Short windows
Weathered layer resonance
PEF whiteness proof in 1-D
Simple dip filters
PEF whiteness proof in 2-D
Examples of modeling and deconvolving with a 2-D PEF
Seismic field data examples
PEF ESTIMATION WITH MISSING DATA
Internal boundaries to multidimensional convolution
Finding the prediction-error filter
TWO-STAGE LINEAR LEAST SQUARES
Adding noise (Geostat)
Inversions with geostat
Infill of 3-D seismic data from a quarry blast
Imposing prior knowledge of symmetry
Hexagonal coordinates
BOTH MISSING DATA AND UNKNOWN FILTER
Objections to interpolation error
Packing both missing data and filter into a vector
LEVELED INVERSE INTERPOLATION
Test results for leveled inverse interpolation
Analysis for leveled inverse interpolation
Seabeam: theory to practice
Risky ways to do nonlinear optimization
The bane of PEF estimation
MULTIVARIATE SPECTRUM
What should we optimize?
Confusing terminology for data covariance
Hermeneutics
About this document ...
Stanford Exploration Project
4/27/2004
![[*]](http://sepwww.stanford.edu/latex2html/prev_gr.gif)
