Use of heterotopic secondary data in geostatistics using covariance tables

Mining projects often contain secondary data spatially correlated with the main variable of interest (primary). These secondary data are usually more densely sampled than the primary (heterotopic) dataset, as they are cheaper and faster to obtain. In this situation, the use of secondary data in geostatistical modelling improves the quality of the final estimated/simulated models. The main geostatistical methodology used to integrate these two types of data is cokriging, which requires the joint modelling of direct and cross variograms using the linear model of coregionalisation (LMC). This article shows a methodology for estimation/simulation with heterotopic secondary data that does not require the LMC. The spatial continuity will be described by covariance tables (direct and cross). A case study is presented to compare the proposed methodology with the estimates/simulations using the LMC. The results were satisfactory, as the estimated and simulated models with covariance tables were properly validated.