P 159 COHERENT NOISE ATTENUATION USING INVERSE PROBLEMS AND PREDICTION ERROR FILTERS 1 SUMMARY ANTOINE GUITTON Department of Geophysics Stanford University Stanford CA 94305 USA Two iterative methods that remove coherent noise during the inversion of 2-D prestack data are tested. One method approximates the inverse covariance matrices with prediction error filters (PEFs) and the other introduces a coherent noise modeling operator in the objective function. This noise modelling operator is a PEF that has to be estimated either before the inversion from a noise model or directly from the data. These two methods lead to independent identically distributed (IID)


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