next up previous print clean
Next: About this document ... Up: Vlad: Velocity estimation for Previous: APPENDIX B - THE

APPENDIX C - preprocessing details

I first applied better tuned f-k filters, then shifted the data 9 meters across offset using a frequency-domain operator. Why? The migration program Phase requires data to be regularly sampled to contain the zero offset. The minimum offset of the data was 241m and the offset sampling was 50m (interpolated to 25), so there was no way of having both the zero offset and regularly sampled data. Worse, Phase requires split-spread data, so half of the offsets would have been off by 9 meters. I then performed f-x decon to eliminate random noise. I interpolated the offsets from a sampling rate of 50m (visible aliasing) to 25m in the wavenumber domain. I performed deconvolution using Pef and Helicon. I had to apply again f-k filters with new parameters to eliminate some of the effects of former aliasing, which turned into spurious events after interpolation.

Figures 11 and 12 show the smallest non-extrapolated offset before and after the new preprocessing, respectively. The railroad-track reflections above 1.5 seconds, which is actually water-velocity noise, is eliminated and the geology beneath is uncovered (due to the dip filters). The strong ringing which multiplied reflectors most visibly in the high-amplitude region is gone (due to deconvolution). The signal/noise ratio between 3 and 5 seconds is highly improved (due to the f-x decon). After the new preprocessing, the stratigraphy looks much more interpretable and new, subtler FEAVO anomalies are brought to light. The V-shaped anomalies were not destroyed; on the contrary, they are clearer than ever (Figure 13).

 
zofbef
Figure 11
Smallest offset (241m) before reprocessing
zofbef
view burn build edit restore

 
zofaft
Figure 12
Smallest offset (250m) after reprocessing. Railroad-track false reflections above 1.5 sec, ringing all over the section and high noise in the lower part are eliminated.
zofaft
view burn build edit restore

 
kshq
kshq
Figure 13
Preprocessing enhanced the V-shaped anomalies
view burn build edit restore

The previous velocity model, which is already existing in the data library, is shown in the upper left panel of Figure 14. The geological setting of the Grand Isle survey in the Mississippi Delta shows that the Grand Isle deposits are very young and the velocity is most likely determined by compaction, making such large lateral velocity variations as pictured in the initial model implausible. The previous velocity had also been picked at only ten midpoints.

I eliminated random noise from the data with an enhanced noise attenuation method. I then transformed each CMP to velocity space, automatically picked the highest semblance values, and transformed them to interval velocity using the ``SuperDix'' inversion described by Clapp et al. (1998) (Figure 15). The result of the inversion was then smoothed along midpoint into a more geologically plausible almost-v(z).

 
veloplot
veloplot
Figure 14
Upper left: previous interval velocity model. Upper right: v(z) model constructed by smoothing it many times. Lower left: new interval velocity model for migration. Lower right: ``v(z)'' profile constructed by smoothing the new velocity model across midpoint
view burn build edit restore

I migrated with the velocity shown in the lower left panel of 14. I also used more frequencies than in the previous migration. The new migration stack is shown in Figure 16. Some reflectors stack better in the newer result, and amplitude anomalies are also more consistent.

 
phw
phw
Figure 15
Illustration of the velocity analysis for one midpoint: autopicker fairway, automatic picks, and inversion weights.
view burn build edit restore

 
kaer_new
Figure 16
New migrated stack
kaer_new
view burn build edit restore

jgb_lecture

 


next up previous print clean
Next: About this document ... Up: Vlad: Velocity estimation for Previous: APPENDIX B - THE
Stanford Exploration Project
11/11/2002