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Minerals
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Geostatistical Modeling of the Cu Ore Deposits
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Geostatistical Modeling of the Cu Ore Deposits

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Deposits".

Deadline for manuscript submissions: closed (14 May 2021) | Viewed by 4456

Special Issue Editors

Prof. Dr. Jacek Mucha

E-Mail Website
Guest Editor
Faculty of Geology, Geophysics and Environmental Protection, Department of Geology of Mineral Deposits and Mining Geology, AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: mining geology; resource estimation; sampling; applied geostatistics; applied statistics
Dr. Monika Wasilewska-Błaszczyk

E-Mail Website
Guest Editor
Faculty of Geology, Geophysics and Environmental Protection, Department of Geology of Mineral Deposits and Mining Geology, AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: mining geology; geostatistics; resource estimation; applied statistics

Special Issue Information

Dear Colleagues,

Worldwide, there are nearly 2000 known copper-bearing mineral deposits. The purpose of this Special Issue of Minerals is to publish original research articles or review papers falling within the thematic scope of "Geostatistical Modeling of the Cu Ore Deposits". This Special Issue will focus on the current achievements of 2D/3D geochemical and lithological modeling of any genetic type of Cu deposit, both onshore and offshore (e.g., polymetallic nodule deposits), using various algorithms of "classical" geostatistics and geostatistical simulations. In particular, this includes modeling of spatial distribution of the grade (or accumulation) of Cu and valuable associated elements accompanying the main ore. Articles on the accuracy of the Cu deposits ("in situ") sampling and the modeling (1D) of variability of Cu content in ore transported on belt conveyors are also welcomed.

This Special Issue will be an opportunity to summarize previous experience in this field and to indicate new directions and research ideas. Examples of applications of the results of geostatistical modeling in mining geology and mining (e.g., assessment of resources and quality of Cu deposits, classification of resources, scenarios of short- and long-term mineral extraction) and indication of new potential applications in these disciplines will be appreciated. We invite representatives of universities, research institutes, geological surveys and the mining industry to share the results of research related to the subject of the Special Issue.

We thank you and look forward to receiving your contributions.

Prof. Dr. Jacek Mucha
Dr. Monika Wasilewska-Błaszczyk
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geostatistical models 1D/2D/3D
  • multiple-point geostatistics
  • geostatistical simulation
  • estimation domains
  • geochemical and lithological modeling
  • resource estimation
  • uncertainty estimation
  • resource classification
  • short- and long-term resource models
  • sampling accuracy
  • mining geology

Published Papers (2 papers)

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Research

25 pages, 9594 KiB  
Article
Problems of Estimating the Resources of Accompanying Elements: A Case Study from the Cu-Ag Rudna Deposit (Legnica-Głogów Copper District, Poland)
by Justyna Auguścik-Górajek, Jacek Mucha, Monika Wasilewska-Błaszczyk and Wojciech Kaczmarek
Minerals 2021, 11(12), 1431; https://0-doi-org.brum.beds.ac.uk/10.3390/min11121431 - 18 Dec 2021
Cited by 1 | Viewed by 2107
Abstract
As a result of the exploitation of ore deposits, in addition to the main elements, the accompanying elements are also partially recovered. Some of them increase the profitability of exploitation, while others reduce it because they hinder the recovery of the main elements [...] Read more.
As a result of the exploitation of ore deposits, in addition to the main elements, the accompanying elements are also partially recovered. Some of them increase the profitability of exploitation, while others reduce it because they hinder the recovery of the main elements and thus increase the costs of the recovery process. A comprehensive economic calculation to assess the profitability of ore mining depends on an appropriately accurate estimation of the resources of both the main and associated elements. This issue was analyzed with the example of the Cu-Ag Rudna ore deposit (LGCD, Poland). The subject of the assessment was the resources prediction accuracy of the main element (Cu) and four (4) accompanying elements (Co, Ni, Pb, and V) using geostatistical estimation method, in particular the ordinary kriging after the estimation of the relative variograms for describing the spatial variability structures of elements abundance. It was found that the standard kriging errors (deviations) in accompanying elements resources that are scheduled for exploitation within a one-year period in some parts of deposits are drastically greater (2 to 5 times) than the estimation errors of the main element resources. This is due to the sparse sampling pattern for their determinations and/or the high variability (among others nugget effect) of their abundance. In this situation, without additional sampling and a denser sampling pattern, the possibilities of a reliable assessment of the influence of accompanying elements on the economic consequences of exploitation are very limited. Full article
(This article belongs to the Special Issue Geostatistical Modeling of the Cu Ore Deposits)
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19 pages, 6756 KiB  
Article
Integration of Dual Border Effects in Resource Estimation: A Cokriging Practice on a Copper Porphyry Deposit
by Nasser Madani, Mohammad Maleki and Fatemeh Sepidbar
Minerals 2021, 11(7), 660; https://0-doi-org.brum.beds.ac.uk/10.3390/min11070660 - 22 Jun 2021
Cited by 5 | Viewed by 1832
Abstract
Hierarchical or cascade resource estimation is a very common practice when building a geological block model in metalliferous deposits. One option for this is to model the geological domains by indicator kriging and then to estimate (by kriging) the grade of interest within [...] Read more.
Hierarchical or cascade resource estimation is a very common practice when building a geological block model in metalliferous deposits. One option for this is to model the geological domains by indicator kriging and then to estimate (by kriging) the grade of interest within the built geodomains. There are three problems regarding this. The first is that sometimes the molded geological domains are spotty and fragmented and, thus, far from the geological interpretation. The second is that the resulting estimated grades highly suffer from a smoothing effect. The third is related to the border effect of the continuous variable across the boundary of geological domains. The latter means that the final block model of the grade shows a very abrupt transition when crossing the border of two adjacent geological domains. This characteristic of the border effect may not be always true, and it is plausible that some of the variables show smooth or soft boundaries. The case is even more complicated when there is a mixture of hard and soft boundaries. A solution is provided in this paper to employ a cokriging paradigm for jointly modeling grade and geological domains. The results of modeling the copper in an Iranian copper porphyry deposit through the proposed approach illustrates that the method is not only capable of handling the mixture of hard and soft boundaries, but it also produces models that are less influenced by the smoothing effect. These results are compared to an independent kriging, where each variable is modeled separately, irrespective of the influence of geological domains. Full article
(This article belongs to the Special Issue Geostatistical Modeling of the Cu Ore Deposits)
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