Curvature refers to the degree of bending of a reflection surface and can be measured as the rate of change of the curved reflection in a given direction
This suggests that in the simplest way one could calculate curvature by computing the first and second derivatives of the x-
and y- components of the surface.
The surface computation of curvature involves fitting a quadratic surface to the mapped horizon using a least-squares regression and using nine sample points (eight neighbors around a given point). When nine sample points are used for the computation of curvature, it results in an overdetermined system when the solution is sought by the least squares regression method. Different measures of curvature can then be written in terms of the six coefficients, as was shown by Roberts (2001).
Of all the available curvature measures, Chopra and Marfurt (2007) recommend the application of the most-positive curvature and most-negative curvature attributes, for they are the easiest to understand intuitively.
Roberts (2001) has given the following expression for computation of the most-positive curvature.
where a, b and c are the coefficients of the polynomial equation.
As we note above,
Notice the clear set of faults/fractures generated from coherence and curvature attributes
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