PS regularization

Figure  exhibits the PS portion of the data set. Again, observe the holes in the data, as well as the presence of more offset.

 

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Figure 5 PS section for the same crossline on Figure 

The data is sorted into CMP gathers. We do not present the data in common conversion point (CCP) gathers because the input of the regularization program performs the binning, and the PSAMO operator performs the lateral shift correction from the CMP point to the CCP point based on the $\gamma$ value.

We proceed with the methodology discussed in the previous section. We perform the PSAMO regularization process because it is the only one that corrects for the lateral shift displacement of the common conversion point. There are only two iterations so far.

Figure  presents two PSAMO regularization results for two iterations of our methodology. For each iteration note that the moveout of the events is not a perfect hyperbola. This characteristic corresponds to the nature of propagation of PS waves.

 

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Figure 6 PS regularization results. First (top) and second (bottom) iteration of our methodology

Observe the difference in the moveout of the events between the two iterations of our methodology (top and bottom parts of Figure ). This is due to the different velocities on each iteration. However, both results satisfactorily fit the data.

Figure  present a zoom of our results and the original data. It is easier to observe that both results fit the data. However, the second iteration is more realistic since it better follows the information of the surrounding traces.

 

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Figure 7 Zoom of Figure . From left to right, the original data, the first and the second iterations. Note how event A is more continuous in the second iteration, and event B presents a more realistic PS moveout (not a perfect hyperbola