Discrimination of signal and noise events on seismic recordings by linear threshold estimation theory
"The object of this study is the investigation of a linear threshold element technique for identifying surface multiples on a single seismic trace. Traces of seismic events were generated which contained primaries, surface multiples, and various levels of Gaussian random noise. Since it was necessary to separate the events as much as possible, the traces were subjected to pulse-compression deconvolution processing prior to LTE analysis. Mean frequency, peak frequency, amplitude spectrum variance, periodicity, and polarity were employed as pattern parameters. A set of weights was found that would maximize the moment of inertia of the S line distribution of the patterns subject to the constraint that the sum of the squared values of the weights was minimized. It is shown that the problem of the maximization of the moment of inertia reduces to the solution of a simple eigenvalue problem. Furthermore, the optimum set of weights is the eigenvector corresponding to the largest eigenvalue of a matrix proportional to the autocovariance matrix of the pattern vectors. The classes of patterns representing primaries and multiples on traces with high signal-to-noise ratios were clustered and separated, making identification by inspection a simple procedure. Clustering and separation of classes on traces with low signal-to-noise ratios was less than optimum"--Abstract, page ii.
Zenor, Hughes M., 1908-2001
Betten, J. Robert
Carlile, Robert E.
Bain, Lee J.
Rupert, Gerald B., 1930-2016
Robinson, John C.
Geosciences and Geological and Petroleum Engineering
Ph. D. in Geophysical Engineering
University of Missouri--Rolla
viii, 93 pages
Note about bibliography
Includes bibliographical references (pages 87-92).
© 1970 David Nuse Peacock, All rights reserved.
Dissertation - Open Access
Seismic reflection method
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Peacock, David Nuse, "Discrimination of signal and noise events on seismic recordings by linear threshold estimation theory" (1970). Doctoral Dissertations. 2157.