KrigingVerfahren

KrigingVerfahren, often shortened to Kriging, is a geostatistical interpolation method used to estimate the value of a variable at unsampled locations based on observed data. It is named after South African mining engineer Danie G. Krige. Kriging is a type of Gaussian process regression, assuming that the spatial variation of the variable can be modeled by a random field. The core principle of Kriging is to use a weighted average of the known data points to predict the unknown value. The weights are determined by the spatial correlation of the data, which is described by a semivariogram. The semivariogram models how the difference between values increases with distance. Kriging provides not only an estimate of the unknown value but also a measure of the uncertainty associated with that estimate, known as the kriging variance. This variance is minimized when the prediction is made at a location close to existing data points and increases with distance from the data. Different types of Kriging exist, including Ordinary Kriging, Simple Kriging, Universal Kriging, and Indicator Kriging, each making different assumptions about the data's statistical properties. Ordinary Kriging is the most commonly used, assuming the mean of the variable is unknown but constant within a local neighborhood. Kriging is widely applied in various fields such as mining, environmental science, hydrology, soil science, and meteorology for tasks like resource estimation, pollution mapping, and weather forecasting.