DocumentCode :
1462557
Title :
Neural networks for seismic principal components analysis
Author :
Huang, Kou-Yuan
Author_Institution :
Dept. of Comput. & Inf. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Volume :
37
Issue :
1
fYear :
1999
fDate :
1/1/1999 12:00:00 AM
Firstpage :
297
Lastpage :
311
Abstract :
The neural network, using an unsupervised generalized Hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. The authors have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The analyzed seismic data are the seismic traces with 20-, 25-, and 30-Hz Ricker wavelets, the fault, the reflection and diffraction patterns after normal moveout (NMO) correction, the bright spot pattern, and the real seismogram at Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal can be shown from the projections on the principal eigenvectors. For PCA, a theorem is proposed, which states that adding an extra point along the direction of the existing eigenvector can enhance that eigenvector. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. The PCA also provides a significant seismic data compression
Keywords :
Hebbian learning; Karhunen-Loeve transforms; feedforward neural nets; geophysical signal processing; geophysical techniques; geophysics computing; neural nets; principal component analysis; seismology; wavelet transforms; NMO; Ricker wavelet; bright spot pattern; covariance matrix; diffraction patterns; explosion seismology; geophysical measurement technique; neural net; neural network; normal moveout; polarity reversal; principal components analysis; principal eigenvector; seismic reflection layer; seismic reflection profiling; seismogram; seismology; theorem; unsupervised generalized Hebbian algorithm; Covariance matrix; Data analysis; Data mining; Diffraction; Frequency; Neural networks; Pattern analysis; Principal component analysis; Reflection; Wavelet analysis;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/36.739164
Filename :
739164
Link To Document :
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