Coherent noise attenuation using inverse problems and predictionerror filters

have a white spectrum. In this paper I show how the residual can be whitened when coherent noise is present in the data. Outliers and noiseburst problems are not addressed here. They can be winnowed out by applying, iteratively, a locally re-weighted regression (Wang, White & Pratt 2000). In the first section I review some basics of inverse theory. Then in the following section I introduce two inversion methods that yield white residuals. The first method proposes approximating the noise covariance operators with prediction–error filters (PEFs). The second method handles the coherent noise by introducing a noise modelling operator within the inversion. These methods are tested with field data.