Wang L X
Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA.
IEEE Trans Neural Netw. 1992;3(2):338-40. doi: 10.1109/72.125877.
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.
地震信号处理中常用的一个常规方法是反卷积，它包括两个操作：反射率检测和幅度估计。现有的统计检测器计算成本高昂。在本文中，构建了一个霍普菲尔德神经网络来执行反射率检测操作。基本思想是通过一个等效的优化问题来表示反射率检测问题，然后构建一个霍普菲尔德神经网络来解决这个优化问题。将该神经检测器应用于一条合成地震道和30条真实地震道。处理结果表明，神经检测器的精度与现有检测器大致相同，但神经检测器的速度要快得多。
