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What is the difference between kriging and cokriging?
Cokriging requires much more estimation, including estimating the autocorrelation for each variable as well as all cross-correlations. Theoretically, you can do no worse than kriging because if there is no cross-correlation, you can fall back on autocorrelation for Z1.
When to use cokriging?
Cokriging methods are used to take advantage of the covariance between two or more regionalized variables that are related, and are appropriate when the main attribute of interest (well data) is sparse, but related secondary information (seismic) is abundant.
What is cokriging?
Abstract: Cokriging is a geostatistical technique that is used for spatial prediction when realizations of a random field are available. These techniques are generalized forms of univariate and multivariate linear regression models based on georeferenced data, which often have spatial dependence.
How does cokriging work?
CoKriging is a multivariate variant of the Ordinary Kriging operation: CoKriging calculates estimates or predictions for a poorly sampled variable (the predictand) with help of a well-sampled variable (the covariable). The variables should be highly correlated (positive or negative).
How is collocated cokriging used in geostatistics?
Collocated cokriging and the Markov model I (MMI) were introduced as a simpler method of modeling multivariate geological problems in the presence of exhaustive secondary data (Almeida & Journel (1994)). The MMI uses the primary correlogram and the correlation between the primary and secondary data to infer all correlations.
Why is cokriging not extensively used in practice?
The covariance matrix requires K 2 covariance functions when K different variables are considered in a cokriging exercise. The inference becomes extremely demanding in terms of data and the subsequent joint modeling is particularly tedious. This is the main reason why cokriging has not been extensively used in practice.
Is there a constraint on the weights of cokriging?
There is no constraint on the weights. Just like simple kriging, this version of cokriging requires working on data residuals or equivalently, on variables whose means have all been standardized to zero. This is the case when applying simple cokriging in an MG approach (the normal score transforms of each variable have a stationary mean of zero).
Which is better simple cokriging or simple collocated cokkiging?
Although simple collocated cokriging is a more straightforward workflow compared to cokriging, there is a drawback. Simple cokriging results in variance inflation in the estimation, and the estimate is less accurate than cokriging.