Much seismic data, especially those acquired on land, are contaminated with random noise that impedes interpretation and interferes with further processing and analysis. Random noise is recognized by its dissimilarity from trace to trace. Signal, on the other hand, is recognized by its lateral continuity. Much of this continuity results from the sedimentary character of the data being considered.

The methods I consider here predict only linear events. While the human eye recognizes the continuity of nonlinear events, the mathematical tools available work best on linear problems. Even though many continuous seismic events are not linear, windowing the image into smaller areas makes most of these events at least approximately linear. Hornbostel 1991 introduced a t-x prediction technique that allowed rapidly changing data without requiring windowing, but in this chapter, I used the same windowing technique for both the f-x and t-x predictions.

- Prediction of seismic signals in the t-x domain
- Prediction of seismic signals in the f-x domain
- The relationship of f-x prediction to t-x prediction
- Comparison of two-dimensional f-x and t-x predictions
- The biasing of f-x prediction toward the output point
- Computer time requirements

2/9/2001