Abstract
For the first time, we apply a new approach to processing earth remote sensing data obtained by the Landsat-8 spacecraft to the central part of the Maloyralskaya zone, the Polar Urals. It consists of integrating metasomatic alteration distribution maps and lineament density patterns that were created based on the results of statistical processing of remote sensing data and the Shuttle Radar Topography Mission (SRTM) digital elevation model. This study is aimed at identifying morphological features and patterns and features of the deep structure and at identifying areas promising for the gold-polymetallic type of mineralization in the studied area. As a result of the study, it is established that areas promising for the gold–polymetallic type of mineralization in the central part of the Maloyralskaya zone are localized along transregional fault zones that intersect favorable horizons and structures and control ore mineralization and within large morphostructures complicated by radial faults of the first-order NE and NW direction with a length of up to 30 km, as well as areas with increased indices of iron oxides (II and III) and, less often, hydroxide (Al–OH and Mg–OH) and carbonate-containing minerals.
REFERENCES
Abdullah, A., Akhir, J.M., and 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. Eng., 2010, vol. 15, no. 6, pp. 949–958. https://doi.org/10.1039/CS9962500401
Alonso-Contes, C.A., Lineament mapping for groundwater exploration using remotely sensed imagery in a karst terrain: Rio Tanama and Rio de Arecibo basins in the northern karst of Puerto Rico, Master’s Thesis, Michigan Technological University, 2011.
Amer, R., Kusky, T., and El Mezayen, A., Remote sensing detection of gold related alteration zones of Um Rus area, central Eastern Desert of Egypt, Adv. Space Res., 2012, vol. 49, pp. 121–134.
Cheng, Q., Jing, L., and Panahi, A., Principal component analysis with optimum order sample correlation coefficient for image enhancement, Int. J. Remote Sens., 2006, vol. 27, no. 16, pp. 3387–3401. https://doi.org/10.1080/01431160600606882
Chernyaev, E.V., Chernyaeva, E.I., and Sedel’nikova, A.Yu., Geology of the Novogodnee-Monto gold-skarn deposit (Polar Urals), in Skarny, ikh genezis i rudonosnost' (Fe, Cu, Au, W, Sn, …). Mat. konf. XI Chtenii A.N. Zavaritskogo (Genesis and Ore Content of Skarns (Fe, Cu, Au, W, Sn, …): Proceedings of the XI Conf. A. N. Zavaritskii Readings), Ekaterinburg: IGiG UrO RAN, 2005, pp. 131–137.
Ekneligoda, T.C. and Henkel, H., Interactive spatial analysis of lineaments, Comput. Geosci., 2010, vol. 36, no. 8, pp. 1081–1090.
Estrada, S., Henjes-Kunst, F., Burgath, K.P., et al., Insights into the magmatic and geotectonic history of the Voikar Massif, Polar Urals, Z. Dtsch. Geol. Ges., 2012, vol. 163, no. 1, pp. 9–41. https://doi.org/10.1127/1860-1804/2012/0163-0009
Farr, T.G., Rosen, P.A., Caro, E., et al., The shuttle radar topography mission, Rev. Geophys., 2007, vol. 45, no. 2, p. RG2004. https://doi.org/10.1029/2005RG000183
Gornyi, V.I., Kritsuk, S.G., Latypov, I.Sh., and Tronin, A.A., Features of the mineralogical zoning of ore-magmatic systems containing quartz-vein gold deposits (based on satellite spectrometry), Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2014, vol. 11, no. 3, pp. 140–156.
Gupta, R.P., Remote Sensing Geology, Berlin: Springer, 2017, pp. 180–190, 235–240, and 332–336.
Hubbard, B.E., Mack, T.J., and Thompson, A.L., Lineament analysis of mineral areas of interest in Afghanistan, USGS Open-File Report, Reston, Va.: U.S. Geological Survey, 2012. http://pubs.usgs.gov/of/2012/1048.
Ivanova, Yu.N. and Nafigin, I.O., Development of an approach for constructing a prognostic map of the probabilistic distribution of high-permeability rock zones for the polymetallic mineralization type according to Landsat-8 spacecraft data, Issled. Zemli Kosmosa, 2023, no. 1. https://doi.org/10.31857/S0205961423010062
Ivanova, Yu.N. and Tyukova, E.E., Decay structures in the Amfibolitovoe (the polar Urals) ore occurrence, in II Nauchn. Konf. “Geologiya na okraine kontinentov” (The II Scientific Conference “Continental Margin Geology,”) 2022, pp. 143–145.
Ivanova, Yu.N., Vykhristenko, R.I., and Vikent’ev, I.V., Structural control of gold mineralization in the central part of the Malouralskii volcano-plutonic belt (the polar Urals) from multispectral images of the Landsat 8 spacecraft, Issled. Zemli Kosmosa, 2020, no. 4, pp. 51–62.
Jensen, J.R., Introductory Digital Image Processing: A Remote Sensing Perspective, Upper Saddle River, N.J.: Prentice Hall, 1996, pp. 276–287 and 296–301.
Jolliffe, I.T., Principal Component Analysis, New York: Springer, 2002.
Kosmicheskaya informatsiya v geologii (Satellite Data in Geology), Peive, A.V, Ed., Moscow: Nauka, 1983.
Kremenetskii, A.A., Obosnovanie poiskovykh i poiskovo-revizionnykh rabot na rudnoe zoloto v predelakh Manyukuyu-Varchatinskogo rudnogo uzla (rudoproyavleniya: Polyarnaya Nadezhda, Geokhimicheskoe i Blagodarnoe). Masshtab 1 : 10 000 (Justification of Prospecting and Checking Activities for Gold Ore within the Manyukuyu-Varchatinsky Ore Cluster (the Polyarnaya Nadezhda, Geokhimicheskoe, and Blagodarnoe Ore Occurrences), Scale 1 : 10 000), Moscow: IMGRE, 2012.
Kumar, C., Chatterjee, S., and Oommen, T., Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India, Int. J. Remote Sens., 2020, vol. 41, no. 2, pp. 794–812. https://doi.org/10.1080/01431161.2019.1648906
Loughlin, W.P., Principal component analysis for alteration mapping, Photogramm. Eng. Remote Sens., 1991, vol. 57, pp. 1163–1169.
Lyapustin, A., Martonchik, J., Wang, Y., et al., Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables, J. Geophys. Res.: Atmos., 2003, no. D17, vol. 108, no. D17. https://doi.org/10.1029/2002JD002903
Masoud, A. and 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, vol. 45, pp. 467–477.
Mather, P.M., Computer Processing of Remotely Sensed Images: An Introduction, Chichester: UK: John Wiley and Sons, 1999.
Maurer, T., How to pan-sharpen images using the gram-Schmidt pan-sharpen method: A recipe, in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Hannover Workshop, Hannover, Germany, 2013, pp. 239–244.
Sadek,·M.S., El-Kalioubi, B.A.,·Ali-Bik, M.W., et al., Utilizing Landsat-8 and ASTER data in geologic mapping of hyper-arid mountainous region: Case of Gabal Batoga area, South Eastern Desert of Egypt, Environ. Earth Sci., 2020, vol. 79, p. 101. https://doi.org/10.1007/s12665-020-8845-4
Pour, A.B., Park, Y., Park, T.S., et al., Regional geology mapping using satellite-based remote sensing approach in Northern Victoria Land, Antarctica, Polar Sci., 2018, no. 16, pp. 23–46.
Pour, A.B., Zoheir, B., Pradhan, B., et al., Editorial for the special issue: Multispectral and hyperspectral remote sing data for mineral exploration and environmental monitoring of mined areas, Remote Sens., 2021, vol. 13, no. 3, p. 519. https://doi.org/10.3390/rs13030519
Remizov, D.N., Shishkin, M.A., Grigor’ev, S.I., Stepunin, A.V., et al., Gosudarstvennaya geologicheskaya karta Rossiiskoi Federatsii. Masshtab 1 : 200 000 (2-e izd., tsif.). Seriya Polyarno-Ural’skaya. List Q-41-XVI (g. Khord’’yus). Ob’’yas. zap. (State Geological Map of the Russian Federation, Scale 1 : 200 000 (2nd Ed., Dig.), Ser. Polar Urals, Sheet Q-41-XVI (Khord’’yus), Explanatory Notice), St. Petersburg: VSEGEI, 2014.
Roy, D.P., Wulder, M., Lovelandet, T.R., et al., Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 2014, vol. 145, pp. 154–172. https://doi.org/10.1016/j.rse.2014.02.001
Shishkin, V.A., Astapov, A.P., Kabatovi, N.V., et al., Gosudarstvennaya geologicheskaya karta Rossiiskoi Federatsii. Masshtab 1 : 100 000 (3-e pokol.). Ural’skaya Seriya. List Q-41 (Vorkuta). Ob’’yasn. zap. (State Geological Map of the Russian Federation, Scale 1 : 1 000 000 (Third Generation), Ser. Urals, Sheet Q-41: Vorkuta, Explanatory Note), St. Petersburg: VSEGEI, 2007.
Singh, A.K. and Mondal, G.C., Remote sensing for mineral exploration, in Geological Methods in Mineral Exploration and Mining, Springer, 2016, pp. 139–185. https://doi.org/10.1007/978-3-319-39292-7_7.
Sobolev, I.D., Soboleva, A.A., and Udoratina, O.V., et al., Devonian island-arc magmatism of the Voikar zone in the Polar Urals, Geotectonics, 2018, vol. 52, no. 5, pp. 531–563.
Teillet, P.M., et al., Radiometric normalization of surface reflectance data in the visible and near-infrared domains from EO-1 Hyperion, IEEE Trans. Geosci. Remote Sens., 1982, no. 3, pp. 354–366.
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, Int. J. Enhanced Res. Sci. Technol. Eng., 2013, vol. 2, no. 2. https://doi.org/10.13140/RG.2.2.20851.99363
Verdiansyah, O., Aplikasi lineament density analysis untuk membatasi pola kaldera purba godean (Lineament density analysis to limit the ancient Godean caldera pattern), J. Teknol. Technosci., 2017, vol. 9, no. 2.
Verdiansyah, O., A desktop study to determine mineralization using lineament density analysis at Kulon Progo Mountains, Yogyakarta and Central Java Province, Indonesia, Indones. J. Geogr., 2019, vol. 51, no. 1, pp. 31–41. https://doi.org/10.22146/ijg.37442
Vikentyev, I.V., Mansurov, R.Kh., Ivanova, Y.N., Tyukova, E.E., et al., Porphyry-style Petropavlovskoe gold deposit, the Polar Urals: Geological position, mineralogy, and formation conditions, Geol. Ore Deposits, 2017, vol. 59, no. 6, pp. 482–520. https://doi.org/10.22146/ijg.37442
Wilson, J.P. and Gallant, J.C., Terrain Analysis: Principles and Applications, John Wiley and Sons, 2000.
Zhang, Y., et al., Comparison of four atmospheric correction algorithms for Landsat-8 OLI imagery in varying landscapes, Remote Sens., 2017, vol. 9, no. 3, p. 233. https://doi.org/10.3390/rs9030233
Zyleva, L.I., Konovalov, A.L., Kazak, A.P., et al., Gosudarstvennaya geologicheskaya karta Rossiiskoi Federatsii. Masshtab 1 : 1 000 000 (3-e pokolenie). Seriya Zapadno-Sibirskaya. List Q-42 – Salekhard: Ob’’yasn. zap. (State Geological Map of the Russian Federation, Scale 1 : 1 000 000 (3rd Generation), Ser. South Siberia, Sheet Q-42 – Salekhard, Explanatory Notice), St. Petersburg: VSEGEI, 2014.
Funding
This work was supported by the State Task of the Institute of Geology of Ore Deposits, Petrography, Mineralogy, and Geochemistry, Russian Academy of Sciences.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors of this work declare that they have no conflicts of interest.
Additional information
Translated by A. Ivanov
Publisher’s Note.
Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ivanova, Y.N., Nafigin, I.O. Using the Landsat-8 Data Set and Shuttle Radar Topography Mission Digital Terrain Model for Gold–Polymetallic Mineralization Prediction on the Territory of the Central Part of the Malouralskaya Zone, the Polar Urals. Izv. Atmos. Ocean. Phys. 59, 1397–1408 (2023). https://doi.org/10.1134/S0001433823120125
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0001433823120125