Interpretation of geological data at the stage of gold ore deposit exploration

Many gold mines experience worsening of composition of mineral resources and reserves recently while developing ore fields which were earlier assumed as unprofitable due to extremely complicated geological conditions and low content of useful components [1]. Mining is often very difficult and expensive therefore. The most attractive sites of such deposits are only extracted partly, which results in accumulation of mean and low quality ore reserves. Selective mining of high-grade ore exclusively is often conditioned by backwardness of available technologies and equipment. Comprehensive extraction of ore reserves requires that the maximum possible information is obtained as early as the stage of geological exploration. Aiming to reduce operating costs, to embrace both high-grade and low-grade ore sites, to optimize drilling, to scale down sampling and its analysis amount, as well as to reliably delineate ore bodies, it is necessary to study spatial variabilities using the analytical and indirect methods. The presented material and petrography analyses accomplished by the authors enable adjustment of boundaries of ore lodes and veins using the methods of interpretation, statistics and trend-analysis. Evaluation and processing of the very conflicting empirical data on the subsoil allow following up spatial distribution patterns of useful components.

Keywords: mining, gold production, data processing, exploration drilling, sampling, trendanalysis, distribution patterns, data interpretation.
For citation:

Vorotyntseva I.A., Smirnov P.A., Danilchenko A.L., Yakubov M.M. Interpretation of geological data at the stage of gold ore deposit exploration. MIAB. Mining Inf. Anal. Bull. 2021;(11):45-55. [In Russ]. DOI: 10.25018/0236_1493_2021_11_0_45.

Acknowledgements:
Issue number: 11
Year: 2021
Page number: 45-55
ISBN: 0236-1493
UDK: 550.8.053
DOI: 10.25018/0236_1493_2021_11_0_45
Article receipt date: 08.05.2021
Date of review receipt: 21.06.2021
Date of the editorial board′s decision on the article′s publishing: 10.10.2021
About authors:

I.A. Vorotyntseva1, Graduate Student, e-mail: irina.vorot@yandex.ru,
P.A. Smirnov1, Graduate Student, Technical Support Engineer, Orika CIS JSC, 125315, Moscow, Russia,
A.L. Danilchenko1, Graduate Student,
M.M. Yakubov1, Graduate Student,
1 National University of Science and Technology «MISiS», 119049, Moscow, Russia.

 

For contacts:

I.A. Vorotyntseva, e-mail: e-mail: irina.vorot@yandex.ru.

Bibliography:

1. Gosudarstvennyy doklad o sostoyanii i ispol'zovanii mineral'no-syr'evykh resursov Rossiyskoy Federatsii v 2018 godu [State report on the state and use of mineral resources of the Russian Federation in 2018], Moscow, 2019, 424 p. [In Russ].

2. Seminsky K. Zh. Special mapping of fault zones of the Earth's crust. Article 1: Theoretical foundations and principles. Geodynamics and Tectonophysics. 2014, no. 5 (2), pp. 445—467. [In Russ]. DOI: 10.5800/GT-2014-5-2-0136.

3. Khaziev R. R., Andreeva E. E., Arefyev Yu. M., Baranova A. G., Valeeva S. E., Anisimova L. Z., Goryntseva K. Yu. Litho-mineralogical features and conditions of formation of Jurassic deposits of the West Siberian oil and gas basin. Georesursy. 2017, vol. 19, no. 4, part 2, pp. 364—367. [In Russ]. DOI: 10.18599/grs.19.4.9.

4. Kahya Asuman, Kanaat Öznur Geological mineralogical and fluid inclusion characteristics of auriferous quartz veins at Güneyköy (Uşak, Eşme), Western Turkey. Neues Jahrbuch für Mineralogie — Abhandlungen. 2018, vol. 195, no. 1, pp. 11—25. DOI: 10.1127/njma/2017/0057.

5. Mraz E., Moeck I., Bissmann S., Hild S. Multiphase fossil normal faults as geothermal exploration targets in the Western Bavarian Molasse Basin: Case study Mauerstetten. Zeitschrift der Deutschen Gesellschaft für Geowissenschaften. 2018, vol. 69, no. 3, pp. 389—411.

6. Cheskidov V., Kassymkanova K. K., Lipina A., Bornman M. Modern methods of monitoring and predicting the state of slope structures. E3S Web of Conferences. 2019, vol. 105, no. 74, article 01001. DOI: 10.1051/e3sconf/201910501001.

7. Davis J. C. Statisticheskiy analiz dannykh v geologii. Kniga 2 [Statistics and data analysis in geology, book 2], Moscow, Nedra, 1990, pp. 267.

8. Nikiforov I. A. Statisticheskiy analiz geologicheskikh dannykh: Uchebnoe posobie [Statistical analysis of geological data. Educational aid], Orenburg, OGU, 2010, 170 p.

9. Babina T. O. Analysis of objective factors affecting the effectiveness of the kriging procedure. Izvestia vuzov. Geology and Exploration. 2003, no. 4, pp. 45—49. [In Russ].

10. Voroshilov V. G. Matematicheskoe modelirovanie v geologii: Uchebnoe posobie [Mathematical modeling in geology. Educational aid], Tomsk, Izd. TPU, 2001, 124 p.

11. Kondratiev M. N., Savva N. E., Gamyanin G. N., Kolova E. E., Semyshev F. I., Malinovsky M. A., Kondratieva E. A. New data on the structure, mineralogy, and geochemistry of the Karalveem gold deposit (Chukotka). Otechestvennaya geologiya. 2017, no. 3, pp. 26—44. [In Russ].

12. Guseva N. S. Sekrety zolotodobychi [Secrets of gold mining], Moscow, Izd-vo «Gornaya kniga», 2020, 208 p.

13. Makarcheva A. A. Osobennosti metodiki otsenki zapasov mestorozhdeniy shtokverkovogo tipa [Features of the methodology for assessing the reserves of stockwork-type deposits], Candidate’s thesis, Moscow, RGGU, 2016, 126 p.

14. Strimzha T. P. Prognozirovanie i poisk poleznykh iskopaemykh: uchebno-metodicheskoe posobie dlya vypolneniya kursovogo proekta [Forecasting and search for minerals: an educational and methodological guide for the implementation of a course project], Krasnoyarsk, SFU, 2014, 39 p.

15. Rupyshev M. S. Some problems of calculating mineral reserves with the use of the orebearing coefficient. Zolotodobycha. 2016, no. 209, pp. 17—22. [In Russ].

16. Emery X. Geostatistics in the presence of geological boundaries: Application to mineral resources modeling. Ore Geology Reviews. 2019, vol. 114. DOI:10.1016/j.oregeorev.2019.103124.

17. Saboor A. T., Bhanwar S. C. Ore body modelling and geostatistical analyses. Emerging Trends in Geophysical Research for Make in India, ETGRMI 2018, IIT (ISM).

18. Moseikin V. V., Galperin A. M., Cheskidov V. V., Punevsky S. A. Improvement of remote automated control of slope structures at mining enterprises. Gornyi Zhurnal. 2017, no. 12, pp. 82—86. [In Russ].

19. Yudenkov V. A. Dispersionnyy analiz [Dispersion analysis], Minsk, Biznesofset, 2013, 76 p.

20. Tolchkova E. N. Review of indicators of variability of contours of ore bodies. MIAB. Mining Inf. Anal. Bull. 2020. Special edition 40, pp. 9. [In Russ].

Подписка на рассылку

Раз в месяц Вы будете получать информацию о новом номере журнала, новых книгах издательства, а также о конференциях, форумах и других профессиональных мероприятиях.