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Partial stacking the data recorded with irregular
geometries within offset and azimuth ranges yields
uniformly sampled common offset/azimuth cubes.
In order to
enhance the signal and reduce the noise, the reflections should
be coherent among the traces to be stacked. Normal
Moveout (NMO) is a common method
to create this coherency among the traces.
Let's define a simple linear model that links the recorded traces
(at arbitrary midpoint locations) to the stacked volume
(defined on a regular grid). Each data trace is the result
of interpolating the stacked traces and equal to the
weighted sum of the neighboring stacked traces. In matrix notation,
this transforms to:
| |
(96) |
where is the data space, is the
model space, and is the linear interpolation operator.
Stacking can be represented as the application of the
adjoint operator to the data traces,
| |
(97) |
This simple operation does not yield satisfactory results for
an uneven fold distribution. To compensate for this uneveness,
it is common practice to normalize the stacked traces by the
inverse of the fold (), thus:
| |
(98) |
Alternatively, it is possible to apply the general theory of
inverse least-squares to the stacking normalization problem.
The formal solution of the inverse least-squares problem
takes the form:
| |
(99) |
() show that the fold normalization
() can be approximated as the inverse of .
With the knowledge of model regularization in the
least-squares inversion theory, it is possible to
introduce smoothing along offset/azimuth in the model space.
The simple least-squares problem becomes:
| |
|
| (100) |
where the roughener operator can be a leaky integration
operator. However, the use of a leaky integration operator may yield
the loss of resolution when geological dips are present. The substitution
of the identity matrix in the lower diagonal of with the
AMO operator correctly transforms a common offset-azimuth cube
into an equivalent cube with a different offset and azimuth.
This transformation also preserves
the geological dip.
The fold, which normalizes the data based on the traces distribution, is
introduced by a diagonal scaling factor. The weights, for the
regularized and preconditioned problem, are thus computed as:
| |
(101) |
where .
This fold calculation can be simplified more as:
| |
(102) |
Next: PS regularization
Up: Data Regularization
Previous: Data Regularization
Stanford Exploration Project
11/11/2002