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AMO Inversion to a Common Azimuth Dataset

I cast 3-D data regularization as a least-squares inversion problem. I form a linear operator that maps from irregular dataspace 5-D data space to a regular 4-D common azimuth volume using a cascade of linear interpolation and Azimuth Move-out (AMO) binning. I regularize the inversion by adding minimizes the difference between various (t , cmpx , cmpy) cubes by applying a filter that acts along offset. AMO is used to transform the cubes to the same hx before applying the filter. Further efficiency is gained by inverting each frequency independently. I apply the methodology on marine dataset and compare the results with two more conventional approaches.