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Mining and geological models of virtual complex ore blocks of the bench

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Category: Content №4 2023

Last Updated on 28 August 2023

Published on 30 November -0001

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Authors:

B.R.Rakishev*, orcid.org/0000-0001-5445-070X, Satbayev University, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (4): 011 - 017

https://doi.org/10.33271/nvngu/2023-4/011

Abstract:

Purpose. Creation of mining and geological models of virtual complex ore blocks of a bench to develop a basic methodology for determining the geological structure of complex structural sections of mineral deposits.

Methodology. In the scientific and technical substantiation of mining and geological, mining and technical indicators of complex structural blocks in terms of ore saturation and complexity of the morphological structure of blocks of ledges, methods of complex and abstract-logical analysis, synthesis, systematization, the method of theoretical generalization, generalization of information sources and world experience in the field of geoinformation of complex structural deposits, statistical analysis, mathematical modeling, mining and geological modeling of mineral deposits were applied.

Findings. Mining and geological models of virtual complex structure ore blocks (CSOB) of the bench have been created. The mining and geological characteristics of the blocks are analytically interconnected with the geometric parameters of the scattered ore bodies and the dimensions of the layer of admixed rock or lost ore. They determine the degree of complexity of the geological structure of the CSOB. According to the given sizes and location of disparate solid and dispersed ore bodies of virtual complex structural blocks, the numerical values of the mining and geological characteristics of ore blocks were calculated using the developed method. CSOB are subdivided into more ore-saturated, moderately ore-saturated, less ore-saturated as well as complex structural and more complex structural ones.

Originality. For the first time in mining, the concepts of “virtual complex-structural ore blocks of a bench” and “mining and geological models of virtual complex-structural ore blocks of a bench” have been introduced. The set of geometrical parameters of the scattered ore bodies of the block and their mining and geological characteristics are presented as mining and geological models of the virtual CSOB of the bench. The developed models make it possible to establish patterns of changes in the mining and geological characteristics of complex ore blocks.

Practical value. The developed mining and geological models of virtual complex structural blocks serve as the basis for creating mining and geological models of real complex ore blocks, models of CSOB in a blasted state. They will make it possible to develop a methodology for rationing losses and impoverishment of ores for real complex-structural blocks, to choose rational parameters for mining technologies for disparate ore bodies in specific mining and geological conditions, and to expand the use of waste-free, low-waste technologies in the development of complex-structural mineral deposits.

Keywords: complex-structural ore blocks, ore saturation factor, mining and geological characteristics, models of virtual blocks

References.

1. Wellmann, F., & Caumon, G. (2018). 3-D Structural geological models: Concepts, methods, and uncertainties. Advances in geophysics 59, 1-121, Elsevier. https://doi.org/10.1016/bs.agph.2018.09.001.

2. Dagasan, Y., Renard, P., Straubhaar, J., Erten, O., & Topal, E. (2019). Pilot point optimization of mining boundaries for lateritic metal deposits: Finding the trade-off between dilution and ore loss. Natural Resources Research, 28, 153-171. https://doi.org/10.1007/s11053-018-9380-9.

3. Masoumi, I., Kamali, G., Asghari, O., & Emery, X. (2020). Assessing the impact of geologic contact dilution in ore/waste classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran. Minerals, 10(4), 336. https://doi.org/10.3390/min10040336.

4. Vasiukhina, D. (2020). 3D geological modeling for mineral resource assessment of the Galeshchynske iron ore deposit, Ukraine. Geoinformatics: Theoretical and Applied Aspects, 2020(1), 1-5.

5. Remezova, O. O., Khrushchov, D. P., Vasylenko, S. P., & Yaremenko, O. V. (2021). Innovative approaches to information modeling of placer deposits. Geoinformatics, 2021(1), 1-6.

6. Rakishev, B. R. (2017). Technological resources for improving the quality and completeness of use of the mineral raw materials. News of the National Academy of Sciences of the Republic of Kazakhstan. Series of geology and technical sciences, 2(422), 116-124.

7. Bala, G., Kurylo, M., & Virshylo, I. (2022, November). Modeling and Estimation of Iron Ore Reserves in the Leapfrog on the Example of the Azovsky Block Deposit. In 16 ^th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, 2022(1), 1-5. EAGE Publications BV.

8. Rakishev, B.R. (2019). Development of the Bozshakol and Aktogay copper ore deposits in Kazakhstan. Gornyi Zhurnal, 2019(1), 89-92. https://doi.org/10.17580/gzh.2019.01.18.

9. Zeqiri, R. (2021). Nickel discretization and quality review in Gllavica mine, Kosovo. Mining of Mineral Deposits, 15(1), 35-41. https://doi.org/10.33271/mining15.01.035.

10. Morales, N., Seguel, S., Cáceres, A., Jélvez, E., & Alarcón, M. (2019). Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning. Minerals, 9(2), 108. https://doi.org/10.3390/min9020108.

11. Osterholt, V., & Dimitrakopoulos, R. (2018). Simulation of orebody geology with multiple-point geostatistics – application at Yandi channel iron ore deposit, WA, and implications for resource uncertainty. Advances in applied strategic mine planning, 335-352. https://doi.org/10.1007/978-3-319-69320-0_22.

12. Cowan, E. J. (2020). Deposit-scale structural architecture of the Sigma-Lamaque gold deposit, Canada – insights from a newly proposed 3D method for assessing structural controls from drill hole data. Mineralium Deposita, 55(2), 217-240. https://doi.org/10.1007/s00126-019-00949-6.

13. Jin, X., Wang, G., Tang, P., Hu, C., Liu, Y., & Zhang, S. (2020). 3D geological modelling and uncertainty analysis for 3D targeting in Shanggong gold deposit (China). Journal of Geochemical Exploration, 210, 106442. https://doi.org/10.1016/j.gexplo.2019.106442.

14. Zuo, R., Kreuzer, O. P., Wang, J., Xiong, Y., Zhang, Z., & Wang, Z. (2021). Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions. Natural Resources Research, 30, 3059-3079. https://doi.org/10.1007/s11053-021-09871-z.

15. Dowd, P. A., & Dare-Bryan, P. C. (2018). Planning, designing and optimising production using geostatistical simulation. Advances in applied strategic mine planning, 421-449. https://doi.org/10.1007/978-3-319-69320-0_26.

16. Rakishev, B., Rakisheva, Z., Auezova, A., & Orynbay, A. (2022). Automated determination of rock crushing zones in the collapse. Mining of Mineral Deposits, 16(3), 109-114. https://doi.org/10.33271/mining16.03.109.

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