Abstract The delineation of geological domains is a crucial step in mineral resource evaluation, as it directly influences the accuracy of grade models. Instead of a single geological interpretation, geostatistical simulation generates multiple scenarios that reproduce the spatial variability and can be used to assess uncertainty in the spatial layout of the geological domains. This study compares two established geostatistical simulation frameworks–Implicit Boundary Simulation (IBS) and Hierarchical Plurigaussian Simulation (HPGS)–and proposes practical, rigorous pathways to incorporate interpreted geological models as soft data. In IBS, the interpreted model enters as an auxiliary signed–distance covariate which is co-simulated with the down-the-hole signed distance derived from sampling data. In HPGS, the interpreted model informs local domain proportions through a moving window that controls local truncation thresholds. Both methods were applied to a porphyry copper deposit in northern Chile for modeling five mineralization zones under pronounced data scarcity. One hundred conditional realizations were generated for each approach and probability maps, most-probable domain maps, and match–mismatch tables against the interpreted model and drilling data were computed. The results demonstrated that, while both approaches benefited from incorporating soft data, HPGS yielded results that are more consistent with the interpreted model, particularly for mineralization zones exhibiting small-scale variability.
Veliz et al. (Wed,) studied this question.