Data regularization: inversion with azimuth move-out
نویسنده
چکیده
Data regularization is cast as a least-squares inversion problem. The model space is a five-dimensional (t,cmpx, cmpy, hx, hy) hypercube. The regularization minimizes the difference between various (t, cmpx, cmpy) cubes by applying a filter that acts in (hx,hy) plane. Azimuth Move-out is used transform the cubes to the same ( hx,hy) before applying the filter. The methodology is made efficient by a Fourier-domain implementation, and preconditioning the problem. The methodology, along with two approximations is demonstrated on 3-D dataset from the North Sea. The inversion result proves superior at a reasonable cost.
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AMO inversion to a common azimuth dataset
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