Prediction of Mineral Reserve by a combination of Geophysical and drilling data, using the Regression, Cokriging, and ANN Algorithms in Mesgaran Copper Deposit, Iran
DOI:
https://doi.org/10.17794/rgn.2026.5.2Keywords:
Reserve Estimation , Induced Polarization (IP) , Artificial Neural Network (ANN) , GeostatisticsAbstract
During the feasibility studies of a mining project, an economic predictive model must be established to evaluate the profitability of the mineral deposit. In this research, three economic models were established using the regression, Cokriging, and ANN techniques to determine whether the mineral production in the Mesgaran copper deposit is profitable or not. The Mesgaran deposit is located in the central part of the South Khorasan province, Iran. The induced polarization (IP) and electrical resistivity tomography (ERT) data were assigned as the input parameters while the copper grade was set as the output variable. The results of those economic predictive models were compared with an actual model obtained from twelve boreholes drilled after the feasibility study phase. Based on the conducted analyses, it was found that the IP data is better suited than the ERT data for constructing the 3D economic model of the copper deposit. In addition, all three predictive models estimated the mean copper grade nearly equal to 0.15%. Nevertheless, a significant discrepancy was revealed in their predicted tonnage and profit values for the deposit. Overall, it was deduced that the usage of ANN algorithm in the economic evaluation of mineral resources provides more accurate, reliable estimations. The results of this research can be utilized in successful financial analysis of mining projects.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kamran Mostafaei, Hamidreza Ramazi, Mohammad Zamani Ahmad Mahmoudi , Mitra Khalilidermani, Dariusz Knez
This work is licensed under a Creative Commons Attribution 4.0 International License.
Creative Commons-BY
Authors who publish with this journal agree to the following terms:
In agreeing this form, you certify that:
- You read the ethical codex of the RGN zbornik available at journal web.
- You submitted work is your original work, and has not previously been published and does not include any form of plagiarism.
- You own copyright in the submitted work, and are therefore permitted to assign the licence to publish to RGN zbornik.
- Your submitted work contains no violation of any existing copyright or other third party right or any material of an obscene, libellous or otherwise unlawful nature.
- You have obtained permission for and acknowledged the source of any illustrations, diagrams or other material included in the work of which you are not the copyright owner.
- You have taken due care to ensure the accuracy of the work, and that, to the best of your knowledge, there are no false statements made within it.
- All co-authors of this submitted work are aware of, and in agreement with, the terms of this licence and that the submitted manuscript has been approved by these authors.
Publication licence
You retain copyright in your submitted work, according to journal license policy (CC-BY). By signing this form you agree that RGN zbornik may publish it under the publication licence. In summary the licence allows the following:
Anyone is free:
- To copy, distribute, display, and perform the work.
- To make derivative works.
Under the following conditions:
- The original author must always be given credit.
- The work may not be used for commercial purposes.
- If the work is altered, transformed, or built upon, the resulting work may only be distributed under a licence identical to this one.
Exceptions to the licence
In addition to publishing the work printed under the above licence, RGN zbornik will also enable the work to be visible online.
The journal editorial can change the licence rules anytime but it cannot retroactively restrict author(s) rights.
