A neural detector for seismic reflectivity sequences.
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.
Wang
L X
LX
Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA.