Five-Dimensional Interpolation: exploring different Fourier operators

Daniel Trad

ABSTRACT

Five-Dimensional interpolation has become a very popular method to pre-condition data for migration. Many different implementations have been developed in the last decade, most of them sharing a similar dataflow and principles, which is applying sparseness to a transform and mapping back to a new seismic geometry. In this report, I explore three different ways to implement the mapping between data and model in the context of Fourier transforms. These three methods are multidimensional Fast Fourier transform, Discrete Fourier Transforms, and Non-Equidistant Fast Fourier transforms. I incorporate the three operators inside the same inversion algorithm, to be able to perform a fair comparison between them. By keeping all the same, we can focus on the actual performance differences. In addition, I discuss some the implementation details, similarities and differences.

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