Comparing linear and non-linear kriging for grade prediction and ore/waste classification in mineral deposits

Hekmatnejad, Amin; Emery, Xavier; Alipour-Shahsavari, Mehrnoosh

doi:10.1080/17480930.2017.1386430

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…Further, Daya and Bejari [50] compared the performance of two kriging techniques, simple kriging and ordinary kriging, in copper deposits and found that while simple kriging produced a smoother result, the result obtained from ordinary kriging was more accurate. Studies by Hekmatnejad et al [53] showed that ordinary kriging also compares well with non-linear kriging techniques (e.g., disjunctive kriging). However, in some instances, disjunctive kriging may outperform ordinary kriging [53].…”

Section: Krigingmentioning

confidence: 99%

“…Studies by Hekmatnejad et al [53] showed that ordinary kriging also compares well with non-linear kriging techniques (e.g., disjunctive kriging). However, in some instances, disjunctive kriging may outperform ordinary kriging [53]. Other advanced kriging techniques, such as fuzzy kriging and compositional kriging, have also shown reliable results.…”

Section: Krigingmentioning

confidence: 99%

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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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Section: Krigingmentioning

confidence: 99%

Section: Krigingmentioning

confidence: 99%

Machine Learning—A Review of Applications in Mineral Resource Estimation

Dumakor-Dupey

Arya

2021

Energies

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“…[10][11][12][13][14]. These problems can be solved on the basis of a set of studies aimed at geometrization, statistical evaluation of the deposit, and modeling and monitoring of its shape, properties and volumes [15][16][17][18][19]. Geometry and subsoil modeling [20][21][22][23] is based on information about geological, geochemical, geomechanical and other properties of the deposit that characterize various features and indicators (structure, properties, state) of the mountain massif and sources of georesources [24][25][26][27], which are modeled geometrically, including topographic surfaces and different types of projections [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

The mining and geometrical methodology for estimating of mineral deposits

Pysmennyi

Peremetchyk

Chukharev

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Ensuring the correct development of the deposit is a priority production task, which is based on a scientifically grounded assessment of the geometrical characteristics of the mineral deposit and a clear understanding of the nature and quantity of the deposit’s re-serves. Aim of the study is a geological and industrial assessment of a mineral deposit, which provides for the correct determination of the quantity and quality of explored reserves, requires the collection and processing of such material, which would be sufficient to draw up a technically correct and economically feasible project for the development of the deposit. The research methodology consists in mining and geometrical modeling and monitoring of subsoil based on progressive and classical methods and techniques for geometrization of the array of minerals and host rocks. This includes a set of measures aimed at collecting and evaluating the initial information, assessing its accuracy, mathematical processing and determining the optimal and most effective methods for solving the problem of geometrization of the field. The results allow us to practically solve the problems of mining operations related to the assessment of reserves of mineral deposits, their genesis, the nature of occurrence, quality, the possibility of sorting, forecasting and industrial development. An effective set of methods has been developed based on the statistical assessment of mineral deposits, as well as the use of the latest geoinformation systems, which provide the possibility of high-quality and accurate calculation and assessment of mineral deposits.

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“…It is worth noting that the support effect is a major issue in mineral resources modeling that occurs when the desirable volume for the final analysis is not equal to the initial volume of the sampling data. Geostatistical techniques such as nonlinear kriging [10,11] and direct block simulation (DBS) [12][13][14] were introduced to assess whether or not the average grade at the block support exceeds a cut-off grade and to classify the block as ore or waste, without the need to create a fine grid and to average the grade values predicted or simulated at the points of this grid [15,16]. This considerably reduces CPU time and memory requirements and make these techniques attractive for modeling large-scale deposits.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran

et al. 2020

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Since the Gol-Gohar iron ore mine (GGIOM), which is located in southeastern Iran, is currently one of the biggest iron mines in this region, increasing the accuracy of its mineral resources model has become a challenge for geologists, metallurgists and mining engineers. Given that an accurate classification of the mining blocks into ore or waste is highly significant in strategic mine planning, three approaches for simulating the iron grades were compared against the true grades obtained from production data. The comparison was done by calculating the ratio between the total number of blocks correctly classified as ore and waste and the total number of misclassified blocks, and it was conducted for each approach in three mined benches at the GGIOM. The results reveal that the grade simulation that ignores the geological boundaries and the grade simulation based on a deterministic geological interpretation are much less accurate than the hierarchical approach, which consists of simulating both the geological boundaries and the grades.

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Comparing linear and non-linear kriging for grade prediction and ore/waste classification in mineral deposits

Cited by 7 publications

References 28 publications

Machine Learning—A Review of Applications in Mineral Resource Estimation

Machine Learning—A Review of Applications in Mineral Resource Estimation

The mining and geometrical methodology for estimating of mineral deposits

Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran

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