Kriging Interpolation for Two-Dimensional Grid Points

In this article, we explore Kriging interpolation for two-dimensional grid points. Kriging interpolation is a geostatistical interpolation algorithm that has found extensive applications across various industrial sectors. Using MATLAB implementation, we can efficiently apply this interpolation method through functions like variogram estimation and kriging system solving. The fundamental principle of Kriging interpolation involves utilizing known data points to estimate values at unknown locations, incorporating spatial correlation through variogram models. This method finds applications in numerous fields including geology, meteorology, and economics. We can visualize the data by plotting points on a two-dimensional plane and then employ Kriging interpolation to estimate values between data points using spatial weighting based on distance and direction. Key MATLAB functions for implementation may include variogramfit for model fitting and kriging for spatial prediction. In summary, Kriging interpolation serves as a powerful tool that enhances our understanding of spatial data patterns and relationships through statistically optimal interpolation.