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<p>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.</p>

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Comparing implicit boundary and hierarchical plurigaussian simulation for modeling geological domains using hard and soft data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Veronica Veliz,
  • Jordan Plaza-Carvajal,
  • Mohammad Maleki,
  • Felipe Navarro,
  • Eduardo Campos,
  • Xavier Emery

摘要

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.