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Introduction

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 up previous print clean
Next: Approximating the noise covariance Up: Guitton: Coherent noise attenuation Previous: Guitton: Coherent noise attenuation
Stanford Exploration Project
4/29/2001