The Inverse Distance + Minimum Curvature method

The minimum curvature algorithm provides a best-fit surface to the data and draws the contours accordingly.

Used on its own, minimum curvature will give very smooth contours that attempt to minimise the ‘roughness’ of the surface being estimated. However, for the type of data that is normally contoured, the roughness of the surface may be important.

In order to stop the real surface roughness being smoothed away, first grid the data using the inverse distance function. Then apply the minimum curvature algorithm to this set of points rather than the raw data. The resulting surface will smooth out small local peaks in the data but will tend to highlight the major trends in the data.

The minimum curvature algorithm does not honour data points. It may produce a surface with a greater range than the original data, particularly if the original data range was large.

In common with most contouring algorithms, contours outside of the boundary of the raw data may be distorted.