Adaptive multiple subtraction using regularized nonstationary regression

Pluto test

ref,sig
Figure 15. Adaptive multiple subtraction in the Pluto synthetic dataset. (a) Input data. (b) Extracted Signal. Surface-related multiples are successfully subtracted.

mod,pre
Figure 16. Multiple model from surface-related prediction (a) and estimated multiples (b) for the Pluto synthetic dataset.

zero,csum
Figure 17. Variability of non-stationary match filter coefficients for the Pluto test. (a) Zero-lag coefficient. (b) Mean coefficient.

Finally, Figure 15 shows an application of the nonstationary matching technique to the Pluto synthetic dataset, a well-known benchmark for adaptive multiple subtraction. Matching and subtracting an imperfect model of the multiples created by the surface-related multiple elimination approach of Verschuur et al. (1992) leaves a clean estimate of primary reflections. Figure 16 shows a comparison between the multiple model obtained by surface-related prediction and the multiple model generated by nonstationary matching. The matching filter non-stationarity is depicted in Figure 17, which shows the variability of filter coefficients with time and space.

Adaptive multiple subtraction using regularized nonstationary regression