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METHODS FOR CONTOURING IRREGULARLY SPACED DATA *
Author(s) -
BOLONDI G.,
ROCCA F.,
ZANOLETTI S.
Publication year - 1977
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/j.1365-2478.1977.tb01155.x
Subject(s) - interpolation (computer graphics) , contouring , data point , grid , surface (topology) , contour line , plane (geometry) , regular grid , transformation (genetics) , coordinate system , geometry , algorithm , mathematics , computer science , geography , computer vision , computer graphics (images) , biochemistry , chemistry , meteorology , gene , motion (physics)
A bstract The sampling theorem in two dimensions univocally defines a surface, provided that its values are known at points disposed on a regular lattice. If the data are irregularly spaced, the usual procedure is first to interpolate the surface on a regular grid and then to contour the interpolated data: however, the resulting surface will not necessarily assume the prescribed values on the irregular grid. One way to obtain this result is to introduce a transformation of the coordinates such that all the original data points are transferred into part of the nodes of a regular grid. The surface is then interpolated in the points correspondent to the other crosspoints of the regular grid; the contour lines are determined in the transformed plane and then, using the inverse coordinate transformation, are transferred back to the original plane where they will certainly be congruent with the original data points. Nonetheless, the resulting surface is very sensitive to the interpolation method used: two algorithms for that are analyzed. The first (harmonization) corresponds to the determination of the potential of an electrical field whose contour conditions are those defined by the data points. The second method consists in two dimensional statistical estimation (krigeing); in particular, the effects of different choices for the data auto‐covariance function are discussed. The solutions are compared and some practical results are shown.