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I won't say the covariance of the model can always be ignored,
but since it must be a prior given, I would like to find some way around it.
Thus I suggest that every data space be augmented till it
has the dimensionality and completeness required to determine a solution.
It this cannot be done fully,
it should still be done to the extent feasible.
For example we might omit evanescent waves,
but not waves blocked by an aperture.
Reinaldo and I are doing just that in tomography.
The covariance matrix of the residual in data space (missing and observed)
seems a reasonable thing
to estimate--unlike the covariance matrix of the model.
I think this matrix should not be thought of as
a covariance matrix of the solution,
but as an interpolation function for plotting the solution.

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Stanford Exploration Project

1/13/1998