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<p>This study focuses on the Molaoi area in Laconia, Greece, aiming to evaluate mineral resources using geostatistical simulation, taking into account the spatial distribution of geological facies and metal grades. The project’s relevance is heightened by the fact that over 15 strategic and critical raw materials listed in the European Union’s catalog are found within Greece’s public mining areas, including zinc, silver, gallium, and germanium, elements crucial for the EU’s energy transition industry. The Molaoi area, part of southeastern Peloponnese, contains significant polymetallic deposits, mainly sphalerite, with previously estimated proven reserves of 2.655 million tonnes and high concentrations of Zn and Pb. This study utilizes core data from over 60 boreholes, encompassing geological descriptions and metal grades. The geological formations of the area play a pivotal role in the spatial estimation and evaluation of mineral resources, influencing the distribution and concentration of metals and guiding the efficient and accurate identification of mineral resources. For the purpose of this study, we co-simulate geological facies and metal concentrations for accurate grade evaluation. In a plurigaussian modeling framework, the geological facies are modeled through underlying Gaussian random fields, which are conditionally simulated using a Gibbs sampler based on the observed facies data and a geological rule. Then, a linear coregionalization model is established to account for the dependencies between facies and the normal scores of grades. This approach is particularly well suited to addressing the complexities inherent in the Molaoi area geology, where the spatial distribution and correlation of metal grades vary significantly across different geological domains. By simulating both categorical (geological domain) and continuous (ore grades) variables, our goal is to enhance confidence in resource evaluation, ensuring accurate consideration of geological uncertainty. This enables a more nuanced and realistic joint distribution modeling. The proposed geostatistical methodology aims to refine mineral resource evaluation, which is essential for the sustainable exploitation of deposits, reduction in licensing times, and attracting investments. The outcome of this research provides critical insights into resource evaluation techniques, aligning with contemporary environmental, economic, and strategic objectives within the European Union and Greece, thus contributing to a more self-reliant and sustainable future in critical raw materials.</p>

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Co-simulation of Geological Facies and Ore Grades in the Molaoi Polymetallic Orebody, Greece: A Geostatistical Application Using Advanced Modeling Techniques

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • George Valakas,
  • Konstantinos Modis

摘要

This study focuses on the Molaoi area in Laconia, Greece, aiming to evaluate mineral resources using geostatistical simulation, taking into account the spatial distribution of geological facies and metal grades. The project’s relevance is heightened by the fact that over 15 strategic and critical raw materials listed in the European Union’s catalog are found within Greece’s public mining areas, including zinc, silver, gallium, and germanium, elements crucial for the EU’s energy transition industry. The Molaoi area, part of southeastern Peloponnese, contains significant polymetallic deposits, mainly sphalerite, with previously estimated proven reserves of 2.655 million tonnes and high concentrations of Zn and Pb. This study utilizes core data from over 60 boreholes, encompassing geological descriptions and metal grades. The geological formations of the area play a pivotal role in the spatial estimation and evaluation of mineral resources, influencing the distribution and concentration of metals and guiding the efficient and accurate identification of mineral resources. For the purpose of this study, we co-simulate geological facies and metal concentrations for accurate grade evaluation. In a plurigaussian modeling framework, the geological facies are modeled through underlying Gaussian random fields, which are conditionally simulated using a Gibbs sampler based on the observed facies data and a geological rule. Then, a linear coregionalization model is established to account for the dependencies between facies and the normal scores of grades. This approach is particularly well suited to addressing the complexities inherent in the Molaoi area geology, where the spatial distribution and correlation of metal grades vary significantly across different geological domains. By simulating both categorical (geological domain) and continuous (ore grades) variables, our goal is to enhance confidence in resource evaluation, ensuring accurate consideration of geological uncertainty. This enables a more nuanced and realistic joint distribution modeling. The proposed geostatistical methodology aims to refine mineral resource evaluation, which is essential for the sustainable exploitation of deposits, reduction in licensing times, and attracting investments. The outcome of this research provides critical insights into resource evaluation techniques, aligning with contemporary environmental, economic, and strategic objectives within the European Union and Greece, thus contributing to a more self-reliant and sustainable future in critical raw materials.