Kriging Interpolation Source Code Implementation

The Kriging interpolation method serves as a fundamental geostatistical technique for estimating unknown point values through spatial correlation analysis of neighboring known data points. This sophisticated algorithm demonstrates exceptional performance across multiple domains including environmental science, agricultural planning, and mineral resource estimation. For technical implementation, the MATLAB-based EasyKrig3.0 package provides comprehensive source code featuring variogram modeling (exponential, spherical, Gaussian models) and optimal weight calculation through matrix operations. Key functions typically include covariance matrix construction using spatial distance metrics, Lagrange multiplier integration for constraint handling, and linear system resolution for Kriging weights. The implementation often incorporates nugget effect processing and range parameter optimization to enhance prediction accuracy. By examining the source code architecture, developers can understand core algorithmic components like semivariogram fitting techniques and BLUE (Best Linear Unbiased Estimator) optimization principles. This exploration enables customization of anisotropy parameters and integration of additional constraints for domain-specific applications. Code analysis may reveal optimization opportunities such as parallel computation for large datasets and adaptive neighborhood selection algorithms.