The study of cokriging using a Markov model

Cokriging is an unbiased prediction method with minimum prediction variance. The main advantage of cokriging is that it takes into account the correlation and cross-correlation of information at the same time, which provides a way to consider different kinds of information simultaneously. Original cokriging cannot solve the problem of instability in matrix. However, according to the hypothesis of screening effect provided by a Markov model, cokriging can be successfully approximated. Then a colocated cokriging is used with the Markov model to realize the above approximation. The screening hypothesis indicates that the hard (primary) datum screens the influence of any other datum on the soft (secondary) colocated variable, which leads to the approximation. Experimental results show that the simulated results are associated with the coefficients of correlation between hard (primary) data and soft (secondary) data greatly. Once the coefficient of correlation is properly set, the simulated results of cokriging under the Markov model are much better than those of full cokriging and simple kriging.