The number of filter coefficients controls the computer time required by both of these techniques. Since the applications being considered here are post-stack and generally fast, at least for 2-dimensional cases, the computer time required is generally insignificant. It is interesting to note that while the effective t-x prediction time-domain filter has more coefficients than the individual filters used in f-x prediction, because f-x prediction produces a different filter for each frequency, the total number of filter coefficients is much larger than the number for t-x prediction. Nevertheless, the computer time needed to apply these two processes in the 2-dimensional case is comparable, since each filter calculated by f-x prediction is smaller than the single filter calculated by t-x prediction. Since the time to calculate a filter is proportional to n3 with the routine I used, where n is the number of filter coefficients, the calculation times used by the two approaches become nearly equal. For the 3-dimensional case, the application of t-x prediction tends to be more costly than f-x prediction, but not tremendously so, since the filter sizes used by t-x prediction can be smaller than those for a comparable f-x prediction. However, as the size of the t-x prediction filter increases, the processing time increases rapidly. On the other hand, should cost be an issue, Claerbout 1992a pointed out that the number of iterations in the conjugate-gradient routine used by my t-x prediction can be significantly reduced from its theoretical limit with good results.