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Next: Conclusions Up: Adaptive multiple subtraction Previous: Spitz test

Pluto test

ref sig
ref,sig
Figure 15.
Adaptive multiple subtraction in the Pluto synthetic dataset. (a) Input data. (b) Extracted Signal. Surface-related multiples are successfully subtracted.
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mod pre
mod,pre
Figure 16.
Multiple model from surface-related prediction (a) and estimated multiples (b) for the Pluto synthetic dataset.
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zero csum
zero,csum
Figure 17.
Variability of non-stationary match filter coefficients for the Pluto test. (a) Zero-lag coefficient. (b) Mean coefficient.
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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.


next up previous [pdf]

Next: Conclusions Up: Adaptive multiple subtraction Previous: Spitz test

2013-03-02