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In a previous report Guitton (2000), I presented two
methods specifically designed to obtain independent and identically
distributed (*iid*) residual components.
When this requirement is met, the inversion of the data becomes
stable with fast convergence toward a minimum and
attenuation of the coherent noise present in the data.
These two inversion strategies are (1) use of a
prediction error filter (PEF) to approximate the noise covariance
matrix and (2) addition of a coherent noise modeling operator in the
fitting goal. The first method filters
the coherent noise while the second separates
the coherent noise by subtraction. The subtraction method
has the advantage to mitigate the correlation between signal
and noise by introducing a regularization term into the inverse problem.
This paper is divided into two parts. Firstly, I use the filtering method
to attenuate coherent noise with both synthetic and real data.
I show that the signal/noise separation is efficient as long as
the PEF used to approximate the noise covariance matrix renders
the noise spectrum accurately. Secondly, I use the subtraction method
on the same data to separate noise from signal. Although both methods
yield similar attenuation of the coherent noise, I anticipate that the
subtraction method has the potential to handle the correlation between
the noise and signal.

** Next:** Approximating the noise covariance
** Up:** Guitton: Coherent noise attenuation
** Previous:** Guitton: Coherent noise attenuation
Stanford Exploration Project

4/29/2001