next up previous print clean
Next: Are field arrays really Up: STEEP-DIP DECON Previous: Dip rejecting known-velocity waves

Tests of steep-dip decon on field data

Low-velocity noises on shot records are often not fully suppressed by stacking because the noises are spatially aliased. Routine field arrays are not perfect and the noise is often extremely strong. An interesting, recently-arrived data set worth testing is shown in Figure [*].

Figure 9
Gravel plain ground roll (Middle East) Worth testing.

view burn build edit restore

I scanned the forty Yilmaz and Cumro shot profiles for strong low-velocity noises and I selected six examples. To each I applied an AGC that is a slow function of time and space (triangle smoothing windows with triangle half-widths of 200 time points and 4 channels). Because my process simultaneously does both low-velocity rejection and deconvolution, I prepared more traditional 1-D deconvolutions for comparison. This is done in windows of 250 time points and 25 channels, the same filter being used for each of the 25 channels in the window. In practice, of course, considerably more thought would be given to optimal window sizes as a function of the regional nature of the data. The windows were overlapped by about 50%. The same windows are used on the steep-dip deconvolution.

It turned out to be much easier than expected and on the first try I got good results on all all six field profiles tested. I have not yet tweaked the many adjustable parameters. As you inspect these deconvolved profiles from different areas of the world with different recording methods, land and marine, think about how the stacks should be improved by the deconvolution. Stanford Exploration Project report 77 (SEP-77) shows the full suite of results. Figure [*] is a sample of them.

Figure 10
Top is a North African vibrator shot profile (Y&C #10) after AGC. Middle is gapped 1-D decon. Bottom is steep-dip decon.

[*] view burn build edit restore

Unexpectedly, results showed that 1-D deconvolution also suppresses low-velocity noises. An explanation can be that these noises are often either low-frequency or quasimonochromatic.

As a minor matter, fundamentally, my code cannot work ideally along the side boundaries because there is no output (so I replaced it by the variance scaled input). With a little extra coding, better outputs could be produced along the sides if we used spatially one-sided filters like
 x& x& x& x& x \\  .& x& x& x& x \\  ....
 ... .& .& .& .& . \\  .& .& .& .& . \\  .& .& .& .& 1
 \end{array}\end{displaymath} (6)

These would be applied on one side of the shot and the opposite orientation would be applied on the other side. With many kinds of data sets, such as off-end marine recording in which a ship tows a hydrophone streamer, the above filter might be better in the interior too.

next up previous print clean
Next: Are field arrays really Up: STEEP-DIP DECON Previous: Dip rejecting known-velocity waves
Stanford Exploration Project