Prestack land data examples

The hybrid adaptive subtraction technique has been tested on few shot records from a land data survey. My goal is to subtract multiple models from shot records. The preprocessing is described in Kelamis et al. (1999) and the goal is to attenuate surface-related multiples only. Note that for these shot records primaries and multiples are strongly correlated.

To better accomodate for the spatial variability of the data, I do not estimate one filter only but many filters. Instead of estimating filters in patches, I estimate a bank of non-stationary time domain (t, x) filters for both $\bf{f}$ and $\bf{A_s}$ Crawley (2000); Rickett et al. (2001).

Figures 3a and 4a show the land data and the multiple model respectively. Figures 3b and 4b display the estimated signal and noise respectively with the standard approach whereas Figures 3c and 4c show the estimated signal and noise with the hybrid approach. The hybrid subtraction improves the noise subtraction at far offsets. It also preserves the signal better as illustrated in Figure 3c around 1.4 seconds.

A different shot record is processed in Figure 5. The corresponding noise model and estimated multiples are displayed in Figure 6. The same conclusions hold true.

A more interesting shot record with its multiple model are shown in Figures 7a. In this example, the multiple attenuation is greatly improved with the hybrid approach.

 

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Figure 3 (a) Input data. (b) Estimated signal with the standard approach. (c) Estimated signal with the hybrid adaptive subtraction technique. The strong primary at 1.4 seconds is better preserved with the hybrid adaptive subtraction.

 

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Figure 4 (a) Multiple model for the data in Figure 3. (b) Estimated noise with the standard approach. (c) Estimated noise with the hybrid adaptive subtraction technique. Far offset events are better subtracted.

 

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Figure 5 Another shot record. (a) Input data. (b) Estimated signal with the standard approach. (c) Estimated signal with the hybrid adaptive subtraction technique.

 

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Figure 6 (a) Multiple model for the data in Figure 5a. (b) Estimated noise with the standard approach. (c) Estimated noise with the hybrid adaptive subtraction technique.

 

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Figure 7 Another shot record. (a) Input data. (b) Estimated noise with the standard approach. (c) Estimated noise with the hybrid adaptive subtraction technique.

 

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Figure 8 (a) Multiple model for the data in Figure 7a. (b) Estimated noise with the standard approach. (c) Estimated noise with the hybrid adaptive subtraction technique. The hybrid adaptive subtraction approach attenuates more multiples. Far offset events are better subtracted too.

The land data examples illustrate the efficiency of the hybrid approach when noise and signal are correlated. In the next section I discuss some limitations of the method and illustrate them with a marine data example.