Multi-point geostatistics for ore grade estimation

Song, Yushou; Liu, Zhan-Ning; Xin, Yu

doi:10.4154/gc.2019.23

Geol Cro

2019

DOI: 10.4154/gc.2019.23

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Multi-point geostatistics for ore grade estimation

Yushou Song

Zhan-Ning Liu

Yu Xin

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2024

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Cited by 2 publications

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The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

Chimunhu,

Topal,

Asad

et al. 2024

Mining Technology: Transactions of the Institutions of Mining a

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Machine learning (ML) applications are increasing their footprint in underground mine planning, enabled by the gradual enrichment of research methods. Indeed, improvements in prediction results have been accelerated in areas such as mining dilution, stope stability, ore grade, and equipment availability, among others. In addition, the increasing deployment of equipment with digital technologies and rapid information retrieval sensor networks is resulting in the production of immense quantities of operational data. However, despite these favourable developments, optimisation studies on key input activities are still siloed, with minimal or no synergies towards the primary objective of optimising the production schedule. As such, the full potential of ML benefits is not realised. To explore the potential benefits, this study outlines primary input areas in production scheduling for reference and limits the scope to six key areas, covering dilution prediction, ore grade variability, geotechnical stability, ventilation, mineral commodity prices and data management. The study then delves into the literature of each before examining the limitations of existing common applications, including ML. Finally, conclusions with recommendations/solutions to enhance resilience, global optimality, and reliability of the production schedule through synergistic nexus with function-specific optimised input models are presented.

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The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

Chimunhu,

Topal,

Asad

et al. 2024

Mining Technology: Transactions of the Institutions of Mining a

View full text Add to dashboard Cite

show abstract

Evaluation method of ore grade estimation effectiveness

Liu,

Lu,

Tian

et al. 2024

PLoS ONE

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This study proposes a new method to evaluate the effectiveness of orebody grade estimations, drawing upon the analysis of existing evaluation methods for grade estimation. This new approach addresses factors such as uneven sampling and asymmetric estimation range, which are challenging to overcome with existing evaluation techniques. The core principle of this method involves documenting how frequently individual samples are used during grade estimation and calculating the total distance weights for each sample. Subsequently, the usage frequency and total weight of the samples are standardized, and these standardized values are weighted based on the sample grades. A comparison is made between the weighted sample grades and the estimated grades, with the closeness between the two serving as a metric for assessing the effectiveness of the estimation. This study compares the new evaluation method to the direct comparison and cross-validation methods, examining the effectiveness of grade estimation using the inverse distance weighting (IDW) method. The findings revealed that: (1) The new evaluation method theoretically accounts for the systematic deviation between the statistical measures of estimated and sample grades resulting from uneven sample distribution, offering a fresh approach for enhancing the effectiveness of orebody grade estimation. (2) In the grade estimation of experimental Fe samples, the frequency of usage and the sum of distance weights were unequal. This inequality significantly contributes to the systematic deviation between the estimated and sample grades. (3) Comparing the new evaluation method to others confirms the stability and reliability of the new approach for evaluating the effectiveness of orebody grade estimation. This novel method demonstrates theoretical advantages and practical utility. (4) The deviation between the estimated grades and the statistical results of sample grades is influenced by the distribution pattern of sample grades, the spatial relationship between samples and estimation blocks, and the inherent systematic error associated with the IDW method. This systematic error cannot be overlooked.

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Multi-point geostatistics for ore grade estimation

Cited by 2 publications

References 11 publications

The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

Evaluation method of ore grade estimation effectiveness

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