MATLAB Implementation of Kriging Interpolation Algorithm

In scientific computing, Kriging interpolation serves as a fundamental geostatistical method that predicts unknown data values through analysis of known data points. Specifically, Kriging interpolation utilizes the spatial positions and values of known points to estimate values at unknown locations, making it widely applicable in fields such as geology, petroleum industry, meteorology, and environmental science. This spatial data interpolation technique also finds extensive application in satellite image processing, geographic information systems (GIS), and remote sensing technologies. From international technical websites, numerous resources on Kriging interpolation are available, covering basic principles, advantages, and practical application examples. These materials facilitate better understanding and implementation of Kriging methodology, thereby enhancing scientific computational capabilities and research proficiency. For MATLAB implementation, key functions typically involve calculating variograms to model spatial correlation, solving kriging systems using linear algebra operations, and generating prediction surfaces through grid-based interpolation. The algorithm commonly employs ordinary kriging with matrix operations like (G'*G)\G'*Z for weight calculation, where G represents the spatial covariance matrix and Z contains known sample values.