Improved Integration Of Secondary Data Using Self-Healing Sequential Gaussian Simulation

Secondary data are important in geostatistical simulation of continuous variables. Geophysical data and geological trends are used for modeling porosity and permeability. Multiple mineral grades must often be modeled for mining applications. Sequential Gaussian simulation (SGS) is often used because of its relative simplicity and robustness. The two most common approaches to integrate secondary data in Gaussian simulation are with (1) locally varying mean, or (2) collocated cokriging. A significant problem with both of these techniques is variance inflation, that is, the variance of the resulting simulated values is too high because of an inappropriate decision of stationarity or an artifact of choosing a single secondary data in presence of many. We introduce a self-healing procedure for dynamic correction of this problem. The dynamic correction is different for the locally varying mean approach and for collocated cokriging since there are different reasons why each of these methods causes variance inflation. The reasons for variance inflation are discussed and the self-healing is applied to a number of data sets.