What is indicator kriging?
Indicator kriging (IK) is a spatial interpolation technique devised for estimating a conditional cumulative distribution function at an unsampled location. The result is a discrete approximation, and its corresponding estimated probability density function can be viewed as a composition in the simplex.
What is multiple indicator kriging?
Multiple Indicator Kriging (MIK) is a non-parametric estimation of uncertainty at point scale that makes no explicit assumption about the distribution. This method could be used for complex mineralization, with mixed grade populations that cannot be easily separated in different domains.
What is the difference between simple and ordinary kriging?
Simple kriging produced a result that is “smoother,” and results show that simple kriging can be less accurate than ordinary kriging. The model obtained by ordinary kriging is more accurate, and future economic decision by ordinary kriging results was more reliable.
Why do we need geostatistics?
Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. In the environmental sciences, geostatistics is used to estimate pollutant levels in order to decide if they pose a threat to environmental or human health and warrant remediation.
What are the 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 kriging in geostatistics?
In statistics, originally in geostatistics, kriging or Kriging, also known as Gaussian process regression, is a method of interpolation based on Gaussian process governed by prior covariances. Under suitable assumptions of the prior, kriging gives the best linear unbiased prediction (BLUP) at unsampled locations.
What is kriging method?
Kriging predicts the value of a function at a given point by computing a weighted average of the known values of the function in the neighborhood of the point. The method is closely related to regression analysis.
How is geostatistics different from statistics?
As nouns the difference between geostatistics and statistics is that geostatistics is (geology|mathematics) the application of statistics to geological observations while statistics is (singular in construction) a mathematical science concerned with data collection, presentation, analysis, and interpretation.
What is Kriging method?
How do you calculate Kriging?
In Ordinary Kriging the number of points used (n <= N) and hence the size of the Kriging matrix (n+1) will change from pixel to pixel while calculating the output map(s)….Algorithm.
| is the estimate or predicted value for one output pixel to be calculated | |
|---|---|
| wi | is the weight factor for input point i |
| Zi | is the value of input point i |
What is the kriging method?
Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data.
What is the purpose of kriging?
Kriging is a type of regression that gives a least squares estimate of data (Remy et. al, 2011). It uses z-scores to generate an estimated surface model from the spatial description of a scattered set of data points. It originated in mining geology, and is now an important part of the geostatistics toolbox.
What is indicator kriging (IK)?
The non-parametric geostatistical method Indicator Kriging (IK), proposed by Journel (1983), solves this non-linear spatial estimation problem satisfactorily. Therefore, its implementation in environmental studies is more extended ( Antunes and Albuquerque, 2013, Jang, 2013, Jang et al., 2013, Piccini et al., 2012 ).
How to estimate categorical variables?
The estimation of categorical variables implies the spatial variability analysis of the experimental indicators for each class. Experimental variograms of indicators have been adjusted with exponential isotropic models, with ranges of 1800 m, 2500 m and 3750 m for I CL. 1, I CL. 2 and I CL. 3, respectively ( Table 2 and Fig. 3 ).
What are the values for the indicators of the classes Pik⁎?
For each cell, the values for the indicators of the classes pIK⁎ ( u0, ck) have been estimated, and interpreted as probabilities of belonging to the respective classes ck, k = 1, 2 and 3.
How do you interpret the variogram of the indicator class?
The variogram of the indicator class must be interpreted as a frequency of variation or transition of a given class to any of the other classes depending on the h distance vector. According this, the class indicator variogram shows the spatial probability of the class changes into any of the other two. Table 2.