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- W2596124159 abstract "We have developed a computational framework that allows an efficient, spatially variant correlation filter for anisotropic dip filtering. The approach is based on the Laplace correlation function, for which there exists analytical expressions for the correlation kernel and its inverse kernel in the 1D case. An extension to higher dimensions by adding orthogonal 1D inverse functions provides a linear equation whose solution is identical to applying a Bessel filter. We have found that a good approximation of the Laplace filter function is obtained by applying a cascade of Bessel filters. We implement such an inverse operator on regular grids with a finite-difference stencil, giving a sparse matrix for the linear equation, which can be solved efficiently through a conjugate-gradient algorithm. Computing such a correlation operation by solving the linear system involving the inverse operator is significantly faster than applying the correlation function via convolution or windowed convolution: Solving the linear system is as fast as applying often-used tensorized 1D convolutions." @default.
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- W2596124159 date "2017-07-01" @default.
- W2596124159 modified "2023-09-25" @default.
- W2596124159 title "Efficient anisotropic dip filtering via inverse correlation functions" @default.
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- W2596124159 doi "https://doi.org/10.1190/geo2016-0552.1" @default.
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