In the first stage an inverse distortions and even structural distortions when wave equation AMO filter is computed to account for the interdependencies processes such as dip moveout, azimuth moveout, and prestack migration between data traces, then an imaging operator is applied to the filtered are applied. Data regularization before imaging becomes a processing data to invert for the final reflectivity model. In the next section we requirement to preserve amplitude information and produce a show the relationship between irregular sampling and inverse theory good quality final image. We propose a new technique to invert for reflectivity and present a formalism for the normalization filter. The computation models while correcting for the effects of irregular sampling. of each element of the filter requires the evaluation of an inner product The final reflectivity model is a two-step solution where the data is in the model space. We show that each inner product corresponds to equalized in a first stage with an inverse filter and an imaging operator an AMO transformation between two data elements. We explore the is then applied to the equalized data to invert for a model. Based on effectiveness of the method in the 2D case for the application of offset least-squares theory, the solution estimates an equalization filter that continuation and partial stacking.