Contents
What are the different types of kriging?
The Geostatistical Wizard offers several types of kriging, which are suitable for different types of data and have different underlying assumptions:
- Ordinary Kriging.
- Simple Kriging.
- Universal Kriging.
- Indicator Kriging.
- Probability Kriging.
- Disjunctive Kriging.
- Empirical Bayesian Kriging.
- Areal Interpolation.
What is the difference between ordinary and universal kriging?
Regression kriging assumes that the fitted trend surface is the correct polynomial (not only the correct degree but also the correct coefficients) whereas Universal kriging only assumes that the trend surface determines the maximum degree of the polynomial but does not use the coefficients from the Trend surface.
What are geostatistical techniques?
Geostatistical analysis techniques are used to predict the values of parameters between data points. Geostatistical methods are only valid for spatially dependent (i.e., nonrandom) data. Conditioning is the incorporation of hard or soft data into a model to reduce uncertainty.
Why do we use ordinary kriging in ArcMap?
It also looks like the data is more variable on the left and becomes smoother on the right. In fact, this data was simulated from the ordinary kriging model with a constant mean µ. The true but unknown mean is given by the dashed line. Thus, ordinary kriging can be used for data that seems to have a trend.
What does data look like in ordinary kriging?
It looks like the data is elevation values collected from a line transect through a valley and over a mountain. It also looks like the data is more variable on the left and becomes smoother on the right. In fact, this data was simulated from the ordinary kriging model with a constant mean µ. The true but unknown mean is given by the dashed line.
Which is an example of a universal kriging concept?
For example, in the following figure, which uses the same data as for ordinary kriging and universal kriging concepts, the observed data is given by the solid circles: The known constant, represented by the dotted line, is µ. This can be compared to ordinary kriging.
Do You estimate ε or ε ( s ) in kriging?
For ordinary kriging, you estimated µ, so you also estimated ε ( s ). If you know ε ( s ), you can do a better job of estimating the autocorrelation than if you are estimating ε ( s ).