Coherent noise attenuation using Inverse Problems and Prediction Error Filters

Antoine Guitton

antoine@sep.stanford.edu

ABSTRACT

Two iterative methods that handle coherent noise effects during the inversion of 2-D prestack data are tested. One method approximates the inverse covariance matrices with PEFs, and the other introduces a coherent noise modeling operator in the objective function. This noise modeling operator is a PEF that has to be estimated before the inversion from a noise model or directly from the data. These two methods lead to Independent, Identically Distributed (IID) residual variables, thus guaranteeing a stable convergence of the inversion schemes and permitting coherent noise filtering/separation.