Interpolation is to use known samples to predict or estimate the values of unknown samples, including interpolation and extrapolation. Application provides three interpolation methods: Inverse Distance Weighting (IDW), Kriging and Radial Basis Function (RBF). It includes the following contents:
About interpolation
The basic concepts and principles of various interpolation methods are introduced in detail.
Inverse Distance Weighting interpolation
Inverse Distance Weighting interpolation is based on the similarity of the sample points in the interpolation region, and calculates the Weighted Mean value of the sample points in the adjacent region to estimate the value of the cell, and then interpolates to get a surface.
Spline interpolation
The spline interpolation method uses the mathematical expression of the minimum surface curvature to simulate and generate a smooth surface through a series of sample points.
Or dinary Kriging interpolation
Ordinary Kriging interpolation is a linear estimator of the regionalized variable, assuming that the observed data are normally distributed and that the expected value of the regionalized variable is unknown.
Simple Kriging interpolation
Simple Kriging interpolation is a linear estimator of the regionalized variable, assuming that the observed data are normally distributed and that the expected value of the regionalized variable is a fixed constant.
Universal Kriging interpolation
Universal Kriging interpolation can be used when there is a trend in the observed data, and the trend can be fitted by a definite function or polynomial.