DOI: 10.56195/20793332-2026-26-5-113-117
I.O. Panichkin
- National University of Science and Technology MISIS, Mining Institute, Moscow, Russian Federation
- Abstract:
- This study proposes a method for composite typing of geometric structures within the three-dimensional geochemical field of a porphyry copper system. The method is intended to divide an ore field into high-potential, transitional and low-potential mininggeological types while accounting not only for copper grade but also for the local shape of the geochemical field, spatial anisotropy, position relative to the ore core and peripheral halos, and lithological domain. The workflow combines geochemical indices, local quadratic field approximation, Hessian analysis and an estimate of linear anisotropy into a composite interval-scale ore-potential indicator. Validation was performed using measured copper grades, geological domains and two independent scenarios for defining mineralized intervals. Types 110 and 120 showed the most stable relationship with copper mineralization: 75.2 % of their interval length has copper grades not lower than 0.30 %, while their length-weighted grades are 0.706 % and 0.698 %, respectively. The high-potential group covers 43.1 % of mineralized intervals under the Russian State Commission on Mineral Reserves criterion scheme and 49.0 % under the Canadian-style scenario; inclusion of transitional types increases coverage to 81.9 % and 94.0 %. The novelty of the approach is the integration of geochemical intensity, local field geometry and anisotropy within a single interpretable classification framework. The method can support ore-body detailing, selection of prospective areas, planning of follow-up exploration, and subsequent mine planning while preserving geological interpretability at the interval scale. The research results may be useful for the integrated interpretation of sampling data, spatial zoning of the ore field, assessment of its internal heterogeneity, and selection of areas requiring additional geological detailing.
- Keywords:
- porphyry copper system, three-dimensional geochemical field, Hessian analysis, spatial anisotropy, composite typing, copper grade, geological domains, mineralized intervals, ore potential, mine planning
- For citation:
- Panichkin I.O. Composite typing of geometric structures of a three-dimensional geochemical field in a porphyry copper system. Mine Surveying and Subsurface Use. 2026; 26 (5): 113-117. (In Russ.). https://doi.org/10.56195/20793332-2026-265-113-117
- Information about the authors:
-
- Ilia O. Panichkin – Postgraduate Student, Department of Geology and Mine Surveying, Mining Institute, National University of Science and Technology MISIS, Moscow, Russian Federation; e-mail:
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- Ilia O. Panichkin – Postgraduate Student, Department of Geology and Mine Surveying, Mining Institute, National University of Science and Technology MISIS, Moscow, Russian Federation; e-mail:
- References:
-
- 1. Lowell J.D., Guilbert J.M. Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic Geology. 1970; 65 (4): 373-408. https://doi.org/10.2113/gsecongeo.65.4.373.
- 2. Sillitoe R.H. Porphyry copper systems. Economic Geology. 2010; 105 (1): 3-41. https://doi.org/10.2113/gsecongeo.105.1.3.
- 3. Викентьев И.В., Шатов В.В., Смирнов Д.И. и др. Медно-золотопорфировое месторождение Юбилейное (Западный Казахстан): геологическая позиция и условия образования. Геология рудных месторождений. 2023; 65 (7). https://doi.org/10.31857/S0016777023070080. Vikentiev I.V., Shatov V.V., Smirnov D.I., et al. The Yubileinoe Cu–Au Porphyry Deposit (Western Kazakhstan): Geological Setting and Formation Conditions. Geology of Ore Deposits. 2023; 65 (7). (In Russ.). https://doi.org/10.31857/S0016777023070080.
- 4. Cheng Q., Agterberg F.P., Ballantyne S.B. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration. 1994; 51 (2): 109-130. https://doi.org/10.1016/0375-6742(94)90013-2.
- 5. Zuo R., Xia Q., Wang H. Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Applied Geochemistry. 2013; 28: 202-211. https://doi.org/10.1016/j.apgeochem.2012.10.031.
- 6. Zhang S., Carranza E.J.M., Fu C., et al. Interpretable machine learning for geochemical anomaly delineation in the Yuanbo Nang District, Gansu Province, China. Minerals. 2024; 14 (5): 500. https://doi.org/10.3390/min14050500.
- 7. Min Y., Zhao J., Sui Y., et al. Comparison of manifold learning algorithms for identifying geochemical anomalies associated with copper mineralization. Scientific Reports. 2025; 15: 39628. https://doi.org/10.1038/s41598-025-23301-7.
- 8. Nathwani C.L., Wilkinson J.J., Fry G., et al. Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation. Mineralium Deposita. 2022; 57: 1143-1166. https://doi.org/10.1007/s00126-021-01086-9.
- 9. Frangi A.F., Niessen W.J., Vincken K.L., et al. Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention – MICCAI’98. Lecture Notes in Computer Science. 1998; 1496: 130-137. https://doi.org/10.1007/BFb0056195.
- 10. Bolsinov A.V., Konyaev A.Yu., Matveev V.S. Nijenhuis geometry. Advances in Mathematics. 2022; 394: 108001. https://doi.org/10.1016/j. aim.2021.108001.
- 11. Кантемиров В.Д., Яковлев А.М., Титов Р.С. Оценка качественных показателей полезных ископаемых с использованием геоинформаци- онных технологий блочного моделирования. Геоинформатика. 2020; 3: 29-37. https://doi.org/10.47148/1609-364X-2020-3-29-37. Kantemirov V.D., Yakovlev A.M., Titov R.S. Assessment of mineral quality indicators using geoinformation technologies for block modeling. Geoinformatika. 2020; 3: 29-37. (In Russ.). https://doi.org/10.47148/1609-364X-2020-3-29-37.
- 12. Кантемиров В.Д., Яковлев А.М., Титов Р.С. Применение геоинформационных технологий блочного моделирования для совершенство- вания методов оценки качественных показателей полезных ископаемых. Известия высших учебных заведений. Горный журнал. 2021; 1:63-73. https://doi.org/10.21440/0536-1028-2021-1-63-73. Kantemirov V.D., Yakovlev A.M., Titov R.S. Application of geoinformation block modeling technologies to improve methods for assessing mineral quality indicators. News of the Higher Institutions. Mining Journal. 2021; 1: 63-73. (In Russ.). https://doi.org/10.21440/0536-1028-2021-1-63-73.
- 13. Мельниченко И.А., Кожухов А.А., Омельченко Д.Р. и др. Построение трехмерной модели месторождения с использованием принципов блочного моделирования и искусственных нейронных сетей. Горный информационно-аналитический бюллетень. 2022; 10: 5-19. https:// doi.org/10.25018/0236_1493_2022_10_0_5. Melnichenko I.A., Kozhukhov A.A., Omelchenko D.R., et al. Construction of a 3D deposit model using block modeling principles and artificial neural networks. Mining Informational and Analytical Bulletin. 2022; 10: 5-19. (In Russ.). https://doi.org/10.25018/0236_1493_2022_10_0_5.
- 14. Кантемиров В.Д., Титов Р.С., Тимохин А.В. и др. Совершенствование методов учета повышенных потерь и разубоживания полезного ископаемого при добыче. Горный информационно-аналитический бюллетень. 2020; 3-1: 453-464. https://doi.org/10.25018/0236-1493-2020-31-0-453-464. Kantemirov V.D., Titov R.S., Timokhin A.V., et al. Improving methods for accounting for increased mineral losses and dilution during extraction. Mining Informational and Analytical Bulletin. 2020; 3-1: 453-464. (In Russ.). https://doi.org/10.25018/0236-1493-2020-31-0-453-464.
- 15. Mou N., Wang G., Sun X. Identification of geochemical anomalies related to mineralization: a case study from porphyry copper deposits in the Qulong- Jiama mining district of Tibet, China. Journal of Geochemical Exploration. 2023; 244: 107126. https://doi.org/10.1016/j.gexplo.2022.107126.
