A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit

Afeni, Thomas Busuyi; Akeju, Victor Oluwatosin; Aladejare, Adeyemi Emman

doi:10.1016/j.gsf.2020.02.019

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Due to limited sampling points in this study, effects of SK and OK interpolation are very similar when the parameter settings are the same, so the study here mainly uses OK (including spherical, exponential, gaussian and power) and several other interpolation methods (including linear, IDW, CT and NN). The contour map obtained by interpolation calculation can be seen intuitively: (1) The ore thickness is characterized by thicker central and southern areas and thinner surrounding areas. From the effects of several interpolation techniques, the following conclusions can also be drawn: (1) the linear and CT interpolation effects are similar in ore thickness, uranium grade or uranium content per square meter.…”

Section: Analysis Of the Distribution Of Reserves-related Parametersmentioning

confidence: 99%

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Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Liu

Tan

2021

Processes

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Uranium resource distribution and accurate reserve evaluation are important references for mineral investment and production. Eight kinds of interpolation methods in the Groundwater Modeling System (GMS), including ordinary kriging (OK), are used in this study to predict the spatial distribution of reserve-related parameters, such as uranium grade, ore thickness and uranium content per square meter. The present study draws the following conclusions: (1) Cross-validation found that the uranium grade value using the spherical method is the closest to the actual value. The spherical method has the best interpolation effect. (2) The relative error, which is +3.62%, between the uranium reserves that is calculated by the spherical interpolation method and that by the traditional calculation value is the smallest. (3) The setting of the number of interpolation grids is related to the actual number of boreholes. The ratio between the two will affect the accuracy of reserve estimation, and different interpolation methods have different degrees of influence on reserve estimation. This method is applicable to all in-situ leaching sandstone uranium mines. Further study needs to be carried out toward heterogeneity of three-dimensional space, which will make the estimation more accurate.

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Section: Analysis Of the Distribution Of Reserves-related Parametersmentioning

confidence: 99%

“…Economic evaluation of the mineral deposits is an important factor in deciding to open or invest in new mines [1]. Reliable reserve and grade estimation determine the economic evaluation of the mineral deposits.…”

Section: Introductionmentioning

confidence: 99%

Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Liu

Tan

2021

Processes

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“…The geometric technique includes classical polygon methods, square blocks, rectangular uniform blocks, triangular blocks, and polygonal blocks. These methods, particularly the polygonal approach, have been employed to estimate ore for a wide spectrum of mineral deposits [34,35], and they are most useful at the initial stage of exploration when collated data is relatively small [35]. The basis of this technique is that grades are allocated a definite area of influence generated by constructing the perpendicular bisectors between adjacent samples or intersections to obtain a regional estimate [36,37].…”

Section: Conventional Resource Estimation Techniquesmentioning

confidence: 99%

“…Many scholarly articles have reported kriging, particularly ordinary kriging, as a good estimator for ore reserve estimation [35,[49][50][51]. Daya [52] applied ordinary kriging to classify an iron ore deposit.…”

Section: Krigingmentioning

confidence: 99%

Machine Learning—A Review of Applications in Mineral Resource Estimation

Dumakor-Dupey

Arya

2021

Energies

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Mineral resource estimation involves the determination of the grade and tonnage of a mineral deposit based on its geological characteristics using various estimation methods. Conventional estimation methods, such as geometric and geostatistical techniques, remain the most widely used methods for resource estimation. However, recent advances in computer algorithms have allowed researchers to explore the potential of machine learning techniques in mineral resource estimation. This study presents a comprehensive review of papers that have employed machine learning to estimate mineral resources. The review covers popular machine learning techniques and their implementation and limitations. Papers that performed a comparative analysis of both conventional and machine learning techniques were also considered. The literature shows that the machine learning models can accommodate several geological parameters and effectively approximate complex nonlinear relationships among them, exhibiting superior performance over the conventional techniques.

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“…They applied semi-variograms and kriging techniques to interpolate the needed values for mineral resource estimation. Afeni et al ( 2021 ) investigated the reserve estimation methods such as triangular block and geostatistical. Then, they used the mentioned methods to estimate the Oyo-Iwa limestone deposit located in Nigeria.…”

Section: Introductionmentioning

confidence: 99%

Ore grade estimation using the imperialist competitive algorithm (ICA)

et al. 2021

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In this paper, a hybrid inverse distance weighted (IDW) and the imperialist competitive algorithm (ICA) was proposed for grade estimations in mining projects. Indeed, the coefficients of the IDW equation, power, and the influence distance were modified by applying the ICA algorithm. For this, an example was solved by using IDW, the ordinary kriging, and IDW-ICA. After successfully validating the IDW-ICA, the iron grades were estimated in the Baba Ali iron mine, the most prominent iron mine in the west of Iran. For this, after gathering data from thirty-two boreholes, the grade estimations were conducted using IDW, kriging, and IDW-ICA. Some statistical parameters, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE), and correlation coefficient, were used to compare the results. The higher value of the R 2 , 94.41%, the lowest values of MAPE, 17.4%, and RMSE, 4.19, of the proposed algorithm, revealed that the IDW-ICA could be applied as a new-fangled method for grade estimations in the mining projects.

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A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit

Cited by 15 publications

References 22 publications

Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Machine Learning—A Review of Applications in Mineral Resource Estimation

Ore grade estimation using the imperialist competitive algorithm (ICA)

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