A Deterministic Evaluation of eismic Fidelity using Velocity Modeling ...
A Deterministic Evaluation of eismic Fidelity using Velocity Modeling ...
A Deterministic Evaluation of eismic Fidelity using Velocity Modeling ...
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3.3 Processing<br />
My primary objective <strong>of</strong> the pre-migration processing phase <strong>of</strong> this study was noise<br />
suppression with minimal processing. I assessed field data to determine the appropriate<br />
processing steps that would limit the impact <strong>of</strong> processing on the migration as described<br />
by Yilmaz, 1987. The basic steps were: 1) to QC the geometry, 2) static corrections, 3)<br />
deconvolution, 4) velocity analysis, 5) normal moveout (NMO) correction, and 6) stack.<br />
I tested the impact <strong>of</strong> minimal processing by QCing the geometry, applying a datum<br />
static correction, running deconvolution, applying NMO and stacking the data. An<br />
automatic gain control (AGC) applied to the migrated volume revealed that noise<br />
manifested as migration swirls (Figure 34) was an issue that would require more<br />
aggressive processing.<br />
One <strong>of</strong> my major concerns with the data was noise that is common with land data.<br />
Sources <strong>of</strong> noise in land data can result from poor receiver coupling and outside noise<br />
sources such as traffic and power lines. An additional noise source common to land data<br />
is ground roll (Figure 35). Low-frequency high-amplitude ground roll can have a<br />
significantly adverse effect on the migration. Ground roll consists <strong>of</strong> Rayleigh waves, aircoupled<br />
waves, and other guided waves propagating within the low velocity surface<br />
layers resulting in coherent noise (Al Husseini et al., 1981). I believed ground round roll<br />
was a contributing factor to the noise present in the initial migration test. Examination <strong>of</strong><br />
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