By Topic

A neural detector for seismic reflectivity sequences

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
L. -X. Wang ; Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA, USA

A commonly used routine in seismic signal processing is deconvolution, which comprises two operations: reflectivity detection and magnitude estimation. Existing statistical detectors are computationally expensive. In the paper, a Hopfield neural network is constructed to perform the reflectivity detection operation. The basic idea is to represent the reflectivity detection problem by an equivalent optimization problem and then construct a Hopfield neural network to solve this optimization problem. The neural detector is applied to a synthetic seismic trace and 30 real seismic traces. The processing results show that the accuracy of the neural detector is about the same as that of the existing detectors, but the speed of the neural detector is much faster

Published in:

IEEE Transactions on Neural Networks  (Volume:3 ,  Issue: 2 )