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A feasibility study for long-path multiple detection using a neural networkLeast-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.
Document ID
19960022633
Acquisition Source
Headquarters
Document Type
Conference Paper
Authors
Feuerbacher, G. A.
(Texaco, Inc. Houston, TX United States)
Moebes, T. A.
(Science Applications International Corp. Houston, TX United States)
Date Acquired
August 17, 2013
Publication Date
May 1, 1994
Publication Information
Publication: Dual-Use Space Technology Transfer Conference and Exhibition, Volume 2
Subject Category
Cybernetics
Accession Number
96N25577
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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