# Translations:Prediction distance/16/en

Predictive deconvolution is based on the convolutional model, which states that the trace within a given gate is the convolution of two components — a minimum-phase wavelet and a white-reflectivity series. Spike deconvolution is a method of finding the components of the convolutional model, and it always should be performed as the first step of predictive deconvolution. The second step involves application of a postdeconvolution filter to yield the final deconvolved trace. Let denote the prediction distance. The head is defined as the first coefficients of the minimum-phase wavelet. In the case in which the parameter is equal to one, the head is a spike. A prediction-error filter with prediction distance equal to one is called a spike (or spiking) deconvolution filter (Chapter 10). The spike-deconvolution filter is equivalent (within a constant scale factor) to the shaping filter that shapes the minimum-phase wavelet into its head. Convolution of the spike-deconvolution filter with the trace yields the spike-deconvolved trace.