Interpretive gridding by anisotropic kriging

Most interpolation algorithms perform poorly on data sampled along profiles crossing features whose length scales are small along the profiles but large transverse to them, such as lineaments. Rather than reproducing the linear features, these algorithms create a series of closures around the profiles. By introducing additional information into the algorithm, in particular by using an anisotropic covariance model for kriging that contains a priori information about the lineations, more realistic results can be obtained. An algorithm of this type produces a much more reasonable map of aeromagnetic data from the Cobb Offset zone of the Juan de Fuca Ridge than either minimum curvature gridding or isotropic kriging.