Abstract: The Linear Model of Coregionalization（LMC） and the Markov Model 1（MM1） are proposed for Cokriging to fulfill the integration of the primary variable and the secondary one. However, when a secondary variable is defined on a much larger support than the primary variable, the MM1 is not appropriate. Then an improved Markov Model 2（MM2） for such a case is presented to meet the above condition. The MM2 screening hypothesis indicates that the secondary datum screens the influence of all further away secondary data on primary datum. Experimental results show that Cokriging under the MM2 is practical when a secondary variable is defined on a much larger support than the primary variable.

Key words: Cokriging, Markov model, screening effect, hard data, soft data

摘要： 协同区域化线性模型（LMC）和最初的Markov模型（MM1）被协同克里格用于融合软硬数据。但是当硬数据定义在较小的空间尺度时，MM1并不适合。对于上述情况，提出一种改进的Markov模型（MM2）。MM2模型的屏蔽效应假设是指某个位置的软数据可以屏蔽其他位置软数据对该位置硬数据的影响。实验结果表明，当硬数据定义在比软数据小的空间尺度时，MM2模型下的协同克里格方法有效。

关键词: 协同克里格, Markov模型, 屏蔽效应, 硬数据, 软数据