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Next: Conclusions Up: Sava and Biondi: WEMVA Previous: Synthetic example

Field data example

We also exemplify our method with a field data example. The data corresponds to a complicated North Sea salt environment Vaillant and Sava (1999), although the region we have selected for our initial analysis is away from the main salt body. We have selected a 2-D line from the 3-D data, although our 2-D assumption for this region is only partially correct Clapp (2001).

Figure [*] depicts the migrated image obtained using our benchmark velocity model. We use this image to relate all our velocity analysis results. Since this velocity model is not perfect, the migrated image is not perfect, either. Various gathers show substantial moveout, particularly inside the ``bowl'' (around depth 2500-3000 m).

 
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Figure 9
Benchmark model. Migrated image (left) and slowness model (right).
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Since our benchmark velocity model is not perfect anyway, and given the intrinsic non-linearity of migration velocity analysis, we decided to back away from this model and use a heavily smoothed version of it as our background model. Figure [*] shows the smoothed model and the corresponding migrated image. The angle-gathers clearly indicate slowness inaccuracies which we try to correct using our WEMVA with differential image perturbations method.

 
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Figure 10
Background model. Migrated image (left) and slowness model (right).
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Following the strategy used for our synthetic model, we run residual migration for a wide range of velocity ratios, and then pick at every location the value which corresponds to the flattest gathers. We compute the image perturbation using the differential equation (12), and scale it with the picked residual migration ratio. We use the image perturbation in Figure [*] to invert for the slowness perturbation. We run 1 non-linear iteration and 9 linear iterations to obtain the slowness perturbation presented in the right panel of Figure [*].

 
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Figure 11
Perturbation image (left) and perturbation slowness (right).
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Finally, we take the inverted image perturbation in Figure [*] and update the background slowness in Figure [*] to obtain the slowness model in Figure [*]. This figure also shows the image obtained by migrating the data using this updated slowness model.

 
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Figure 12
Updated model. Migrated image (left) and slowness model (right).
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Comparing the background and improved slowness models in Figures [*] and [*], we observe improved flatness in the upper part of the image (around depth 1500-2500 m). We also observe better definition of the ``bowl'' (around depth 2500-3000 m). However, the bottom-right corner of the image degrades slightly after inversion, possibly as a result of boundary effects or of poor picking during the residual migration step.

Comparing our improved slowness model and the benchmark model in Figures [*] and [*], we also observe a few interesting differences. Again, the ``bowl'' (around depth 2500-3000 m) is better defined using our improved slowness model, although the upper part of the model is still flatter in the image obtained with the benchmark model.


next up previous print clean
Next: Conclusions Up: Sava and Biondi: WEMVA Previous: Synthetic example
Stanford Exploration Project
7/8/2003