Application of Landsat-8 Satellite Data to Predict Ore Mineralization for the Northern Territories on the Example of the Central Part of the Maloural Zone (The Polar Urals)

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Abstract

A new approach was developed during this study. It is focused on identifying probabilistic zones of increased fracturing (zones with a high density of lineaments), considered as a predictive feature for the localization of ore mineralization in the central part of the Malouralskaya zone (part the Polar Urals). This area is promising for the identification of ore occurrences of the polymetallic type (Fe, Cu, Cu–Zn, Au–Cu). Density maps of lineaments were built basis on the developed approach. In addition, predictive schemes for the distribution of highly permeable rock zones and promising areas for the polymetallic mineralization was identified, taking into account geological information, the distribution of mineral resources, and the outcome of remote sensing data processing. The last is based on identifying structures by manual and automatic approaches and their integration using the theory of fuzzy logic. Morphostructure maps were obtained from Landsat-8 data. These maps show that the known polymetallic ore occurrences in the region (Cu, Cu–Zn, Cu–Pb–Au, Fe–Ti–V, Cu–Pt) are located along the perimeter of a large morphostructure of the 1st order, or near extended tectonic structures for up to 20 km with mainly NE and less often NW trends. We identified six prospective zones by comparing remote sensing results with the geological map of the studied territory and known ore occurrences. The highlighted areas showed spatial consistency with several known polymetallic ore occurrences.

About the authors

J. N. Ivanova

Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences; Peoples’ Friendship University of Russia

Author for correspondence.
Email: jnivanova@yandex.ru
Russia, Moscow; Russia, Moscow

I. O. Nafigin

Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences

Email: jnivanova@yandex.ru
Russia, Moscow

References

  1. Бортников Н.С., Лобанов К.В., Волков А.В., Галямов А.Л., Мурашов К.Ю. Арктические ресурсы золота в глобальной перспективе // Арктика: экол. и экон. 2014. № 4(16). С. 28–37.
  2. Викентьев И.В., Мансуров Р.Х., Иванова Ю.Н. и др. Золото-порфировое Петропавловское месторождение (Полярный Урал): геологическая позиция, минералогия и условия образования // Геол. руд. местор. 2017. Т. 59. № 6. С. 501–541.
  3. Гессе В.Н., Дембовский У.С., Монастырь У.С. и др. Карта четвертичных образований. Мас. 1 : 200 000 (1-е изд.). Уральская сер. Лист Q-41 – Воркута. ВСЕГЕИ. 1975.
  4. Геоинформационные технологии в проектировании и создании корпоративных информационных систем. Межвузовский научный сборник. Уфа. 2012. 193 с.
  5. Государственная геологическая карта Российской Федерации. Мас. 1 : 1 000 000 (3-е поколение). Уральская серия. Лист Q-41 – Воркута. Объясн. зап. СПб: ВСЕГЕИ, 2007. 541 с.
  6. Коновалов А.Л, Зылёва Л.И., Казак А.П. и др. Государственная геологическая карта Российской Федерации. Масштаб 1 : 1 000 000 (3-е покол.). Серия Западно-Сибирская. Лист Q-42 – Салехард: Объясн. зап. СПб.: ВСЕГЕИ, 2014. 396 с.
  7. Кременецкий А.А. Обоснование поисковых и поисково-ревизионных работ на рудное золото в пределах Манюкую-Варчатинского рудного узла (рудопроявления: Полярная Надежда, Геохимическое и Благодарное). Мас. 1 : 10 000. М.: ФГУП ИМГРЭ, 2012. 45 с.
  8. Ремизов Д.Н., Шишкин М.А., Григорьев С.И. и др. Государственная геологическая карта Российской Федерации. Масштаб 1 : 200 000 (2-е изд., цифровое). Серия Полярно-Уральский. Лист Q-41-XVI (Хордюс). Объясн. зап. СПб.: ВСЕГЕИ, 2014. 256 с.
  9. Шишкин В.А, Астапов А.П., Кабатови Н.В. и др. Государственная геологическая карта Российской Федерации. Масштаб 1 : 1 000 000 3-е покол.). Уральская сер. Лист Q-41 – Воркута. Объясн. зап. СПб.: ВСЕГЕИ, 2007. 541 с.
  10. Abdullah A., Akhir J. M., Abdullah I. Automatic Mapping of Lineaments Using Shaded Relief Images Derived from Digital Elevation Model (DEMs) in the Maran – Sungai Lembing Area, Malaysia // Electr. J. Geotech. Engin. 2010. V. 15(6). P. 949–958. https://doi.org/10.1039/CS9962500401
  11. Beygi S., Talovina I., Tadayon M., et al. Alteration and structural features mapping in Kacho Mesqal zone Central Iran using ASTER remote sensing data for porphyry copper exploration // Intern. Jour. of Image and Data Fusion. 2020. 12(1). https://doi.org/10.1080/19479832.2020.1838628
  12. Bolouki S.M., Ramazi H.R., Maghsoudi A., et al. Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran // Remote Sens. 2020. 12. 105. https://doi.org/10.3390/rs12010105
  13. Bonham-Carter G.F. Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists, 1994. 416 p.
  14. Carranza E.J.M. Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook of Exploration and Environmental Geochemistry, 2008. V. 11. 351 p.
  15. Cheng Q., Jing, L., Panahi A. Principal component analysis with optimum order sample correlation coefficient for image enhancement // Intern. J. Rem. Sen. 2006. V. 27(16). P. 3387–3401. https://doi.org/10.1080/01431160600606882
  16. Duuring P., Hagemann S.G., Novikova, Y., et al. Targeting iron Ore in banded iron formations using ASTER data: Weld Range Greenstone Belt, Yilgarn Craton, Western Australia // Econ. Geol. 2012. V. 107. P. 585–597.
  17. Ekneligoda T.C., Henkel H. Interactive spatial analysis of lineaments // J. Comp. and Geos. 2010. V. 36. № 8. P. 1081–1090.
  18. Estrada S., Henjes-Kunst F., Burgath K.-P., et al. Insights into the magmatic and geotectonic history of the Voikar Massif, Polar Urals // Z. Deutschen Ges. Geowissenschaften. Bd 2012. V. 163. № 1. P. 9–41. https://doi.org/10.1127/1860-1804/2012/0163-0009
  19. Gupta R.P. Remote Sensing Geology, 3rd edn. Springer, Berlin, Germany, 2017. P. 180–190, 235–240, and 332–336.
  20. Hubbard B.E., Mack T.J., Thompson A.L. Lineament Analysis of Mineral Areas of Interest in Afghanistan. USGS Open. Reston, Virginia: U.S. Geological Survey. 2012. Available at: http://pubs.usgs.gov/of/2012/1048.
  21. Hung L., Batelaan O., Dinh N.Q., et al. Remote sensing and GIS-based analysis of cave development in the Suoimuoi catchment (Son La-NW Vietnam) // J. Cave Karst Stud. 2002. V. 64. P. 23–33.
  22. Hung Q., Batelaan O., De Smedt F. Lineament extraction and analysis, comparison of Landsat ETM and Aster imagery // Case study: Suoimuoi tropical karst catchment, Vietnam, Proc. of SPIE. 2005. 5983, 5983. P. 1–12.
  23. Jensen J.R. Introductory Digital Image Processing: A remote sensing perspective // Pearson Prentice Hall, Upper Saddle River NJ 07458, 3-rd edn., 2005. P. 276–287 and 296–301.
  24. Jolliffe I.T. Principal component analysis. Department of Mathematical Sciences King’s College University of Aberdeen, Uk, 2-d edition., 2002. 487 p.
  25. Kim Y.H., Choe K.U., Ri R.K. Application of fuzzy logic and geometric average: A Cu sulfide deposits potential mapping case study from Kapsan Basin, DPR Korea // Ore Geol. Rev. 2019. V. 107. P. 239–247.
  26. Kocal A., Duzgun H., Karpuz C. Discontinuity Mapping with Automatic Lineament Extraction from High Resolution Satellite Imagery // In Proc. of the XXth ISPRS Congress, 2004, Istanbul, Turkey. P. 2–6.
  27. Kumar C., Chatterjee S., Oommen T. Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India. // Intern. J. Rem. Sen. 2020. V. 41. № 2. P. 794–812.https://doi.org/10.1080/01431161.2019.1648906
  28. Mallast U., Gloaguen R., Geyer S. et al. Derivation of groundwater flow-paths based on semi-automatic extraction of lineaments from remote sensing data // Hydrol. Earth Syst. Sci. 2012. V. 15. P. 2665–2678.
  29. Marion A. Introduction aux techniques de traitement d’images. Eyrolles, Paris, 1987. P. 127–167.
  30. Masoud A., Koike K. Tectonic architecture through Landsat-7 ETM+/SRTM DEM-derived lineaments and relationship to the hydrogeologic setting in Siwa region, NW Egypt // J. Afr. Earth Sci. 2006. V. 45. P. 467–477.
  31. Masoud A.A., Koike K. Morphotectonics inferred from the analysis of topographic lineaments auto-detected from DEMs: application and validation for the Sinai Peninsula, Egypt // Tectonophysics. 2011. 510(3). P. 291–308. https://doi.org/10.1016/j.tecto.2011.07.010
  32. Moradpour H., Paydar G.R., Feizizadeh B. et al. Fusion of ASTER satellite imagery, geochemicaland geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran // Intern. J. Image and Data Fusion. 2021. https://doi.org/10.1080/19479832.2021.1915395
  33. Nawaz A., Magiera J. Remote sensing based geological mapping and mineral exploration of the area of North Waziristan // 15th SGA Meeting. Glasgow. UK. 2019. V. 3. P. 1378–1381.
  34. Nykänen V., Groves D.I., Ojala V.J. et al. Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper – Gold deposits in the northern Fennoscandian Shield, Finland // Aust. J. Earth Sci. 2008. V. 55. P. 25–38.
  35. Pour A.B., Park Tae-Yoon S., Park Y. et al. Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and WorldView-3 Multispectral Satellite Imagery for Prospecting Copper-Gold Mineralization in the Northeastern Inglefield Mobile Belt (IMB), Northwest Greenland // Rem. Sens. 2019. 11(20), 2430. https://doi.org/10.3390/rs11202430
  36. Pour A.B., Sekandari M., Rahmani O. et al. Identification of Phyllosilicates in the Antarctic Environment Using ASTER Satellite Data: Case Study from the Mesa Range, Campbell and Priestley Glaciers, Northern Victoria Land // Rem. Sens. 2021. 13(1). 38. doi.org/.https://doi.org/10.3390/rs13010038
  37. Pour A.B., Zoheir B., Pradhan B. et al. Editorial for the Special Issue: Multispeal and Hyperspectral Remote Sing Data for Mineral Exploration and Environmental Monitoring of Mined Areas // Rem. Sens. 2021. 13, 13, 519. doi.org/https://doi.org/10.3390/rs13030519.
  38. Rahnama M., Gloaguen R. A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction // Rem. Sens. 2014. 6. P. 5938–5958. https://doi.org/10.3390/rs6075938
  39. Ramli M.F., Yusof N., Yusoff M.K. et al. Lineament mapping and its application in landslide hazard assessment: A review // Bull. Eng. Geol. Environ. 2010. 69. P. 215–233.
  40. Sarp G. Lineament Analysis From Satellite Images, North-West Of Ankara. Msc thesis, Middle East Technical University, 2005. 76 p.
  41. Schowengerdt R.A. Remote sensing: models and methods for image processing, sample correlation coefficient for image enhancement // Int. J. Remote Sens. 2007. V. 27(16). Sets Syst. 4 (1). P. 37–51.
  42. Süzen M.L., Toprak V. Filtering of satellite images in geological lineament analyses: an application to a fault zone in Central Turkey // Intern. J. Rem. Sen. 1998. 19(6). P. 1101–1114.
  43. Thannoun R.G. Automatic Extraction and Geospatial Analysis of Lineaments and their Tectonic Significance in some areas of Northern Iraq using Remote Sensing Techniques and GIS // Intern. J. enhanced Res. in Scien. Techn. & Engin. 2013. 2, 2. ISSN NO: 2319-7463.
  44. Verdiansyah O. A Desktop Study to Determine Mineralization Using Lineament Density Analysis at Kulon Progo Mountains, Yogyakarta and Central Java Province. Indonesia // Indonesian J. Geography. 2019. 51, 1. P. 31–41.https://doi.org/10.22146/ijg.37442.
  45. Verdiansyah O. Aplikasi Lineament Density Analysis Untuk Membatasi Pola Kaldera Purba Godean // J. Teknologi Technoscienti, 2017. 9(2).
  46. Zadeh L.A. Fuzzy sets // Inf. Control. 1965. V. 8(3). P. 338–353. https://doi.org/10.1016/s0019-9958(65)90241
  47. Zhang N., Zhou K., Du X. Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China // J. Afr. Earth Sc. 2017. 128. P. 84–96.
  48. Zhumabek Z., Bibossinov A.A. Fremd Automated lineament analysis to assess the geodynamic activity areas // Procedia Computer Science. 2017. V. 121. P. 699–706. https://doi.org/10.1016/j.procs.2017.11.091
  49. Zoheir B., Emam A., Abdel-Wahed M. et al. Multispectral and Radar Data for the Setting of Gold Mineralization in the South Eastern Desert, Egypt // Remote Sens. 2019. V. 11. 1450.

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Copyright (c) 2023 Ю.Н. Иванова, И.О. Нафигин

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