A comparison of surface fitting algorithms for geophysical data

Many treatments in geophysics require regular grids of data. Since the data are generally recorded irregularly (e.g. gravity measurements along roads) or even on tracks (e.g. satellite measurements), it is necessary to grid the observed data. We present the result of a comparison of various surface fitting algorithms carried out in order to check their reliability. Two different types of sampling have been verified: (i) clouds of points and (ii) points along tracks. Five algorithms have been extensively tested: (1) polynomial fit, (2) algorithm using a combination of spline-laplace, (3) krigeage (4) least-squares fitting method, and (5) finite element method. The suitability of each method for different sets of data and the limitations (in terms of amplitude and gradient) are discussed.