Although t-x prediction gives results similar to those of f-x prediction in low noise cases, t-x prediction provides better random noise reduction than the older f-x prediction technique in the presence of moderate- to high-amplitude noise. The t-x prediction also avoids the generation of false events in the presence of strong parallel events when using f-x prediction. These advantages of t-x prediction are the result of its ability to control the length of the prediction filters in time. Because t-x prediction has a shorter effective filter length in time than f-x prediction, t-x prediction passes significantly less random noise than f-x prediction. While Gulunay's f-x prediction biases the prediction toward the traces nearest to the output point, allowing more noise to be passed, this bias appears unimportant when compared to the problem with the length of the effective filter in time.
Three-dimensional prediction allows improved noise attenuation because more samples are used to make predictions. Three-dimensional prediction also relaxes the requirement that events be linear. Comparisons of one- and two-pass predictions on a land data set show that the one-pass results show significantly less noise than the two-pass results. In both one- and two-pass predictions, the f-x prediction passes more undesirable random noise than the t-x prediction.