A NEURAL DETECTOR FOR SEISMIC REFLECTIVITY SEQUENCES

A commonly used routine in seismic signal processing is deconvolution [1]-[3], which comprises two operations: reflectivity detection and magnitude estimation. Existing statistical detectors [2]-[5] are computationally expensive. In this letter, 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 [2]-[5], but the speed of the neural detector is much faster.