A New Semi-greedy Approach to Enhance Drillhole Planning

Dutaut, Raphaël; Marcotte, Denis

doi:10.1007/s11053-020-09674-8

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…In the past decades, many other geostatistical objective functions have been proposed as an alternative to the kriging variance for optimal sampling design, such as the weighted kriging variance [26][27][28], interpolation variance [29][30][31], combined variance [31][32][33], conditional variance [34], information entropy [31,35], value of information [36], efficacy of information [37], GET (grade-estimation error-thickness) function [10,38], cross-validation error [39], interquartile range [40], probability interval widths [34,41], probability of classification error [42], probability of threshold exceedance [43], expected ore value [44], expected cost of classification errors [45], or increase of indicated and measured mineral resource categories [46]. These objective functions account for local ore grade variability, expected grade, expected productivity, expected profit, and/or reduction of uncertainty, unlike the kriging variance that only depends on the spatial correlation (variogram) and geometric configuration of the data.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

Hossein-Morshedy,

Khorram,

Emery

2023

Minerals

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Accurate subsurface exploration requires an optimal network of boreholes. This paper proposes a multi-objective approach to optimize the layout of additional exploratory boreholes. In order to illustrate this approach, geochemical analyses of core samples at the eastern part of the Kahang copper deposit, Central Iran, were used. A measure of the grade uncertainty (kriging standard deviation) and a confidence measure on the ore/waste classification were first calculated by implementing ordinary and indicator kriging. An ore value function was then determined to measure the total value of each block by considering the grades of all the effective variables and their ore membership degree derived from a fuzzy treatment of the grades. Finally, a misclassification cost is defined for each block based on the expected economic effects of ore loss and waste dilution. As a result, an index for the selection of additional boreholes was introduced in order to maximize the kriging standard deviation, the ore misclassification cost, and the ore value and to minimize the confidence measure. Applied to the Kahang copper deposit, this index allowed the prioritization of areas for infill sampling, leading to the recommendation for eight vertical and two directional additional boreholes.

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Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

Hossein-Morshedy,

Khorram,

Emery

2023

Minerals

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“…Geostatistical techniques assume stationarity assumptions and normalised data before conditional simulations [2,[9][10][11]. Data for spatial estimation in the geoscience domain is obtained mostly through scarce and costly drilling methods [12][13][14][15]; therefore, robust MLAs must be explored. Real-time decision-making, particularly the demand for automation in various industries, requires efficient and less time-consuming spatial estimation models [16,17].…”

Section: Introductionmentioning

confidence: 99%

Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK

Ahmad

Muhammad

Glass

et al. 2022

IJGI

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Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can incorporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p < 10 × 10−5), with correlation coefficients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency.

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“…Several studies were made considering the kriging variance as a guide to locate infill sampling. Examples of those studies are: Szidarovszky (1983); Gershon (1987); Groenigen et alli (1999); Delmelle & Goovaerts (2009); Wilde (2009); Soltani et alli (2011);Mohammadi et alli (2012); Silva & Boisvert (2013); Soltani & Hezarkhani (2013); Dutaut & Marcotte (2020). Those works differ from each other by the optimization algorithm utilized or the application of the kriging variance as the objective function, that can be considered alone, be averaged in the domain, weighted, with the estimated value, and others.…”

Section: -Introductionmentioning

confidence: 99%

Hybrid objective function applied to optimize infill sampling location

Ramos¹

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A New Semi-greedy Approach to Enhance Drillhole Planning

Cited by 5 publications

References 20 publications

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK

Hybrid objective function applied to optimize infill sampling location

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