Adaptive minimum prediction-error deconvolution and wavelet estimation using Hopfield neural networks

Three Hopfield (1984, 1985) neural networks are developed to realize a new adaptive minimum prediction-error deconvolution procedure. The first neural network is developed to detect the reflectivity sequence. The second neural network is developed to determine the magnitudes of the detected reflections. The third neural network is developed to estimate the seismic wavelet. A block-component method is proposed for simultaneous reflectivity estimation and wavelet extraction based on these three neural networks. These three neural networks and the block-component method are simulated for a narrowband wavelet. Real seismic data are processed using the block-component method, and the results are compared with those using the minimum variance deconvolution (MVD) filter and the maximum-likelihood based SMLR detector