Geological control for in-situ and recoverable resources assessment: A case study on Sarcheshmeh porphyry copper deposit, Iran

Maleki, Mohammad; Mery, Nadia; Soltani-Mohammadi, Saeed; Khorram, Farzaneh; Emery, Xavier

doi:10.1016/j.oregeorev.2022.105133

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Truncated Gaussian simulation, for instance, is employed to simulate spatial variability within specified constraints tied to geological features, physical properties, or other factors (Galli et al., 1994). Plurigaussian simulation (Armstrong et al., 2011; Veliz et al., 2023) extends traditional Gaussian techniques to handle complex geological settings, accommodating multiple Gaussian distributions where distinct statistical characteristics are exhibited by different geological units or facies. Both truncated Gaussian simulation and plurigaussian simulation can be applied to either stationary or non‐stationary fields.…”

Section: Introductionmentioning

confidence: 99%

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

Song,

Tsai

2024

Water Resources Research

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Developing a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

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Section: Introductionmentioning

confidence: 99%

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

Song,

Tsai

2024

Water Resources Research

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A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

Nasretdinova,

Madani,

Maleki

2024

Nat Resour Res

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The increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

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Uncertainty Quantification in Mineral Resource Estimation

Lindi,

Aladejare,

Ozoji

et al. 2024

Nat Resour Res

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Mineral resources are estimated to establish potential orebody with acceptable quality (grade) and quantity (tonnage) to validate investment. Estimating mineral resources is associated with uncertainty from sampling, geological heterogeneity, shortage of knowledge and application of mathematical models at sampled and unsampled locations. The uncertainty causes overestimation or underestimation of mineral deposit quality and/or quantity, affecting the anticipated value of a mining project. Therefore, uncertainty is assessed to avoid any likely risks, establish areas more prone to uncertainty and allocate resources to scale down potential consequences. Kriging, probabilistic, geostatistical simulation and machine learning methods are used to estimate mineral resources and assess uncertainty, and their applicability depends on deposit characteristics, amount of data available and expertise of technical personnel. These methods are scattered in the literature making them challenging to access when needed for uncertainty quantification. Therefore, this review aims to compile information about uncertainties in mineral resource estimation scatted in the literature and develop a knowledge base of methodologies for uncertainty quantification. In addition, mineral resource estimation comprises different interdependent steps, in and through which uncertainty accumulates and propagates toward the final estimate. Hence, this review demonstrates stepwise uncertainty propagation and assessment through various phases of the estimation process. This can broaden knowledge about mineral resource estimation and uncertainty assessment in each step and increase the accuracy of mineral resource estimates and mining project viability.

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Geological control for in-situ and recoverable resources assessment: A case study on Sarcheshmeh porphyry copper deposit, Iran

Cited by 4 publications

References 31 publications

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

Uncertainty Quantification in Mineral Resource Estimation

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