Lateral Prediction for Noise Attenuation by T-x and F-x Techniques

Attenuating random noise with a prediction lter in the time-space domain generally produces results similar to those of predictions done in the frequency-space domain. However, in the presence of moderate-to high-amplitude noise, time-space, or t-x prediction passes less random noise than does frequency-space, or f-x prediction. The f-x prediction may also produce false events in the presence of strong parallel events where t-x prediction does not. These advantages of t-x prediction are the result of its ability to control the length of the prediction lter in time. An f-x prediction produces an eeective t-x domain lter that is as long in time as the input data. Gulunay's f-x domain prediction, also referred to as FXDECON, tends to bias the predictions toward the traces nearest the output trace, allowing more noise to be passed, but this bias may be overcome by modifying the system of equations used to calculate the lter. The three-dimensional extension to the two-dimensional t-x and f-x prediction techniques allows improved noise attenuation, because more samples are used in the predictions, and the requirement that events be strictly linear is relaxed.