Abstract
The largest Au–Ag deposits in the eastern Asian region occur along the borders of cratons and superterranes. Meanwhile, intra-terranean areas host numerous small deposits, the prospecting and exploration of which are complicated by natural factors. The target areas of the Kema Terrane and the Kema Metallogenic Belt (Sikhote–Alin Superterrane, Southeast Russia) stretch along the coast of the Japan Sea to the north from latitude 45 as a part of the Sikhote–Alin Superterrane. The Kema Terrane is an Early Cretaceous back-arc basin, and it is overlain by a volcanic–sedimentary cover that is intruded by now partially eroded Late Cretaceous and Paleogene acidic plutons. This terrane includes epithermal occurrences of Au–Ag mineralization that are related to volcanic uplifts and associated with metasomatically and hydrothermally altered volcanic rocks. Field mapping in this terrane is extremely complicated by dissected relief and dense forest cover. The novelty of this research includes overcoming difficulties in delineating new prospective areas with machine learning and decreasing vegetation effects for prospecting epithermal Au–Ag deposits. The model uses fragmented and incomplete data in combination with field sampling and laboratory analysis of alteration minerals, which accompany Au–Ag mineralization. “Mineral” images derived from directed principal component (DPC) analysis of ASTER bands were used as evidence to complement information on mineral assemblages derived from field sampling. The DPC analysis helped in partial removal of the influence of sparse and moderately dense vegetation on ASTER multispectral data. A mineral occurrence probability map (MOPM) was generated by implementation of the maximum entropy (MaxEnt) method on the DPC-derived “mineral” images. This map delineates discovered deposits not used in model training and reveals previously unknown areas with high mineral occurrence probability.
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References
Abrams M, Hook S, Ramachandran B (2022) ASTER user handbook. Version 2. Jet Propulsion Laboratory / California Institute of Technology. https://lpdaac.usgs.gov/documents/262/ASTER_User_Handbook_v2.pdf. Accessed 19 Apr 2022
ASTER (2022) Level 1 precision terrain corrected registered at-sensor radiance. https://lpdaac.usgs.gov/products/ast_l1tv003/. Accessed 29 Aug 2022
Beiranvand Pour A, Park T-Y-S, Park Y, Hong JK, Zoheir B, Pradhan B, Ayoobi I, Hashim M (2018) Application of multi-sensor satellite data for exploration of Zn–Pb sulfide mineralization in the Franklinian Basin, North Greenland. Remote Sens 10(8):1186. https://doi.org/10.3390/rs10081186
Brown WM, Gedeon TD, Groves DI, Barnes RG (2000) Artificial neural networks: a new method for mineral prospectivity mapping. Aust J Earth Sci 47(4):757–770. https://doi.org/10.1046/j.1440-0952.2000.00807.x
Carranza EJM (2008) Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier 11, Netherlands
Carranza EJM, Hale M (2002) Mineral imaging with landsat thematic mapper data for hydrothermal alteration mapping in heavily vegetated terrane. Int J Remote Sensing 23(22):4827–4852. https://doi.org/10.1080/01431160110115014
Carranza EJM, Laborte AG (2016) Data-driven predictive modeling of mineral prospectivity using random forest: a case study in Catanduanes Island (Philippines). Nat Resour Res 25(1):35–50
Chashchin AA, Popov VK, Budnitskiy SYu, Chashchin SA, Nozdrachev EA (2020) New data on the age of volcanic complexes from the Cenozoic rift basins of the Eastern Sikhote-Alin. Adv Cur Nat Sc 4:169–175. https://natural-sciences.ru/article/view?id=37381. https://doi.org/10.17513/use.37381
Chen G, Huang N, Wu G, Luo L, Wang D, Cheng Q (2022) Mineral prospectivity mapping based on wavelet neural network and Monte Carlo simulations in the Nanling W-Sn metallogenic province. Ore Geol Rev 143:1–14. https://doi.org/10.1016/j.oregeorev.2022.104765
Clark RN, Swayze GA, Gallagher A, King TVV, Calvin WM (1993) The US Geological Survey Digital Spectral Library, Version 1:0.2 to 3.0 mm. US Geol Surv Open File Report 93–592
Deng J, Wang C, Bagas L, Carranza EJM, Lu Y (2015) Cretaceous-Cenozoic tectonic history of the Jiaojia Fault and gold mineralization in the Jiaodong Peninsula, China: constraints from zircon U-Pb, illite K–Ar, and apatite fission track thermochronometry. Miner Deposita 50:987–1006. https://doi.org/10.1007/s00126-015-0584-1
Di Tommaso IM, Rubinstein N (2007) Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geol Rev 32(1):275–290. https://doi.org/10.1016/j.oregeorev.2006.05.004
EarthExplorer USGS (2022) Web site. https://earthexplorer.usgs.gov. Date of access: 29.08.2022
ESA (2013) Optical properties of ice and snow. https://www.esa.int/SPECIALS/Eduspace_Global_EN/SEMPJ7TWLUG_2.html. Accessed 18 Nov 2022
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Hariharan S, Tirodkar S, Porwal A, Bhattacharya A, Joly A (2017) Random forest-based prospectivity modelling of greenfield terrains using sparse deposit data: an example from the Tanami region, Western Australia. Nat Resour Res 26(4):489–507. https://doi.org/10.1007/s11053-017-9335-6
Heiskanen J (2006) Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. Int J of Remote Sens 27(6):1135–1158
Hewson R, Mshiu E, Hecker C, van der Werff H, van Ruitenbeek F, Alkema D, van der Meer F (2020) The application of day and night time ASTER satellite imagery for geothermal and mineral mapping in East Africa. Int J Appl Earth Obs Geoinf 85:101991. https://doi.org/10.1016/j.jag.2019.101991
Hierarchical data format (2022) HDF NASA. https://asdc.larc.nasa.gov/documents/tools/hdf.pdf. Accessed 29 Aug 2022
Hubbard BE, Crowley JK (2005) Mineral mapping on the Chilean-Bolivian Altiplano using co-orbital ALI, ASTER and Hyperion imagery: data dimensionality issues and solutions. Remote Sens Environ 99:173–186. https://doi.org/10.1016/j.rse.2005.04.027
Gladkova VV, Nesterova AS, Gukalova AM, Lebedeva GA, Kostin AYa (2021) State geological map of Russian Federation. Scale 1:1000000. Far Eastern series. L – (52),53; (K – 52,53). http://www.vsegei.com/ru/info/pub_ggk1000-3/Dalnevostochnaya/l-52-53.php. Accessed 31 Sept 2021
Goryachev NA (2019) Gold deposits in the earth’s history. Geol Ore Deposits 61:495–511. https://doi.org/10.1134/S1075701519060023
Goryachev NA, Pirajno F (2014) Gold deposits and gold metallogeny of Far East Russia. Ore Geol Rev 59:123–151. https://doi.org/10.1016/j.oregeorev.2013.11.010
Ihlen V (2019) Landsat 8 (L8) Data Users Handbook. Version 5.0, 2019. Department of the Interior U.S. Geological Survey. https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf. Accessed 31 Sept 2021
Isozaki Y, Aoki K, Nakama T, Yanai Sh (2010) New insight into a subduction-related orogen: a reappraisal of the geotectonic framework and evolution of the Japanese Islands. Gondwana Res 18:82–105. https://doi.org/10.1016/j.gr.2010.02.015
Jia R, Lv Y, Wang G, Carranza EJM, Chen Y, Wei C, Zhang Z (2021) A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China). Comput Geosci 151:104754. https://doi.org/10.1016/j.cageo.2021.104754
Juliani C, Ellefmo SL (2019) Prospectivity mapping of mineral deposits in Northern Norway using radial basis function neural networks. Minerals 9(2):131. https://doi.org/10.3390/min9020131
Khanchuk AI (2006) Geodynamics, magmatism, and metallogeny of the Russian Far East. Dalnauka. Vladivostok
Khanchuk AI, Kemkin IV, Kruk NN (2016) The Sikhote-Alin orogenic belt, Russian South East: terranes and the formation of continental lithosphere based on geological and isotopic data. J Asian Earth Sci 120:117–138. https://doi.org/10.1016/j.jseaes.2015.10.023
Khanchuk AI, Grebennikov AV, Ivanov VV (2019) Albian-Cenomanian orogenic belt and igneous province of Pacific Asia. Russ J Pac Geol 13(3):4–37. https://doi.org/10.1134/S1819714019030035
Khanchuk AI, Mochalov AG, Rasskazov IY, Yakubovich OV, Berdnikov NV, Nevstruyev VG (2020) Isotopic age of native platinum from andesitic fluidolites of the Poperechnoye deposit (Malyi Khingan, Russia). Russ J Pac Geol 14:43–47. https://doi.org/10.1134/S1819714020010066
Khomich VG (1986) Principles of specialized typification of ore-bearing geological structures. Transactions of VI Symposium of MAGRM “Conditions of Formation of Ore Deposits”. Nauka, Moscow
Khomich VG, Boriskina NG (2007) Ore-magmatic systems with noble metals in the northern marginal sector of the Argun superterrane. Dokl Earth Sci 414:534–537. https://doi.org/10.1134/S1028334X07040101
Khomich VG, Boriskina NG, Santosh M (2014) A geodynamic perspective of world-class gold deposits in East Asia. Gondwana Res 26:816–833. https://doi.org/10.1016/j.gr.2014.05.007
Khomich VG, Nemeth K, Boriskina NG (2020) Indicators of geodynamic control of the formation of mineral resources along a convergent plate margin: Sakhalin-South Kuril areas, Russia. Int J Earth Sci 109:2759–2772. https://doi.org/10.1007/s00531-020-01923-8
Knox-Robinson CM, Wyborn LAI (1997) Towards a holistic exploration strategy: using geographic information systems as a tool to enhance exploration. Aust J Earth Sci 44:453–463
Krehbiel C (2022) Working with ASTER L1T Visible and Near Infrared (VNIR) Data in R. USGS. https://lpdaac.usgs.gov/resources/e-learning/working-aster-l1t-visible-and-near-infrared-vnir-data-r/. Accessed 1 Mar 2022
Kuhn S, Cracknell MJ, Reading AM (2018) Lithologic mapping using random forests applied to geophysical and remote-sensing data: a demonstration study from the Eastern Goldfields of Australia. Geophysics 83(4):B183–B193. https://doi.org/10.1190/geo2017-0590.1
Le Hung T, Zabloskii VR (2019) The method of detection of clay minerals and iron oxide based on Landsat multispectral images (as exemplified in the territory of Thai Nguyen Province, Vietnam). Min Sci Technol 4(1):65–75. https://doi.org/10.17073/2500-0632-2019-1-65-75
Li B, Liu B, Guo K, Li C, Wang B (2019) Application of a maximum entropy model for mineral prospectivity maps. Minerals 9(9):556. https://doi.org/10.3390/min9090556
LP DAAC (2009). Change in Status Alert for the ASTER SWIR detector. Jun 30, 2009. https://lpdaac.usgs.gov/news/change-status-alert-aster-swir-detector/. Accessed 19 Apr 2022
Malinovsky AI, Golozubov VV, Kasatkin SA (2015) Composition and formation settings of cretaceous terrigenous rocks of the West Sakhalin terrane. Dokl Earth Sci 463:672. https://doi.org/10.1134/S1028334X15070193
McCuaig TC, Beresford S, Hronsky J (2010) Translating the mineral systems approach into an effective exploration targeting system. Ore Geol Rev 38:128–138. https://doi.org/10.1016/j.oregeorev.2010.05.008
NASA (2022) Shuttle Radar Topography Mission (SRTM) Version 3.0 Global 1 arc second data released over Asia and Australia. https://earthdata.nasa.gov/learn/articles/nasa-shuttle-radar-topography-mission-srtm-version-3-0-global-1-arc-second-data-released-over-asia-and-australia. Accessed 19 Apr 2022
Nokleberg WJ, Bundtzen TK, Dawson KM, Eremin RA, Goryachev NA, Koch RD, Ratkin VV, Rozenblum IS, Shpikerman VI, Frolov YF, Gorodinsky ME, Melnikov VD, Diggles MF, Ognyanov NV, Petrachenko ED, Petrachenko RI, Pozdeev AI, Ross KV, Wood DH, Grybeck D, Khanchuk AI, Kovbas LI, Nekrasov IYa, Sidorov AA (1997) Significant metalliferous lode deposits and placer districts for the Russian Far East, Alaska, and the Canadian Cordillera. U.S. Geological Survey https://pubs.usgs.gov/of/1996/of96-513-b/. Accessed 16 Aug 2021
Perestoronin AN, Razvozzhaeva EP (2011) The system of Cenozoic depressions in the Amur and Primorye regions: the structure, tectonic position, and geodynamic interpretation. Russ J Pac Geol 5:139–154. https://doi.org/10.1134/S1819714011020072
Phillips SJ, Dudík M (2008) Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecogeg 31(2):161–175
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pour BA, Hashim M (2015) Hydrothermal alteration mapping from Landsat-8 data, Sar Cheshmeh copper mining district, south-eastern Islamic Republic of Iran. J Taibah Univ Sci 9(2):155–166. https://doi.org/10.1016/j.jtusci.2014.11.008
Pour BA, Park T-Y-S, Park Y, Hong JK, Zoheir B, Pradhan B, Ayoobi I, Hashim M (2018) Application of multi-sensor satellite data for exploration of Zn–Pb sulfide mineralization in the Franklinian Basin, North Greenland. Remote Sens 10(8):1186. https://doi.org/10.3390/rs10081186
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75(3):185. https://doi.org/10.1007/s12665-015-4950-1
Rowan LC, Mars JC (2003) Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sens Env 84(3):350–366. https://doi.org/10.1016/S0034-4257(02)00127-X
Salamba KE, Hede ANH, Heriawan MN (2019) Identification of alteration zones using a Landsat 8 image of densely vegetated areas of the Wayang Windu Geothermal field, West Java, Indonesia. IOP Conf Ser Earth Environ Sci 254:012004. https://doi.org/10.1088/1755-1315/254/1/012004
Saluyt deposit (2021) Web site: business portal Nedradv. https://nedradv.ru/nedradv/ru/find_place?obj=f2f5e2370b07304ef3b5b8e491394250. Accessed 13 Oct 2021
Scikit-learn (2020a) K-means clustering. Scikit-learn. Official web site. https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html. Accessed 11 Jan 2020
Scikit-learn (2020b) Principal Component Analysis. Official web site. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html. Accessed 11 Jan 2020
Scikit-learn (2020c) One Class SVM. Official web site. https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html. Accessed 11 Jan 2020
Shevyrev S (2019) Neotectonics, remote sensing and erosion cut of ore- controlling structures of the Mnogovershinnoe gold-silver deposit (Khabarovsk Krai, Russian Far East). Ore Geol Rev 108:8–22. https://doi.org/10.1016/j.oregeorev.2018.11.016
Shevyrev S (2022) DefME Software for Landsat 8 OLI data processing. GitHub Repository. https://github.com/SergeiShevyrev/DefME. Accessed 14 Oct 2022
Shevyrev S, Carranza EJM (2020) Modelling of geodynamic regimes of precious metal-bearing porphyry deposits: Lazurnoe deposit (Sikhote–Alin Belt, Far East) case study. Geol J 55:8309–8328. https://doi.org/10.1002/gj.3935
Shevyrev S, Carranza EJM (2022) Application of maximum entropy for mineral prospectivity mapping in heavily vegetated areas of Greater Kurile Chain with Landsat 8 data. Ore Geol Rev 142:104158. https://doi.org/10.1016/j.oregeorev.2022.104758
Shevyrev SL, Khomich VG, Boriskina NG (2017) System analysis of results of geological-geophysical, seismotomographic, remote sensing researches methods of the Earth for the prospecting of ore-bearing areas (on the example of East Asia). Mineral Prospectivity: current approaches and future innovations. Orléans, France
Soenen SA, Peddle DR, Coburn CA (2005) SCS+C: a modified sun-canopy-sensor topographic correction in forested terrain. IEEE Trans Geosci Remote Sens 43(9):2148–2159
Stroppiana D, Brivio PA, Zaffaroni P, Boschetti M, Mollicone D, Petrucci B (2007) Burnt area mapping within the borders of the Italian National Parks using ASTER images. GeoInformation in Europe: 27th EARSeL Symposium, pp 211–217
Syakur MA, Khotimah BK, Rohman EM, Satoto BD (2018) Integration K-means clustering method and elbow method for identification of the best customer profile Cluster. IOP Conf Ser: Mater Sci Eng 336(1):012017. https://doi.org/10.1088/1757-899X/336/1/012017
Tözün KA, Özyavaş A (2020) New logical operator algorithms for mapping of hydrothermally altered rocks using ASTER data: a case study from central Turkey. Ore Geol Rev 122:103533. https://doi.org/10.1016/j.oregeorev.2020.103533
Utkin VP (2005) Composition, geochronology and structural-dynamic regime of vertical development of East-Sikhote-Alin magma-metallogenic belt. Dokl Earth Sci 404:659–663
Utkin VP (2013) Shear structural paragenesis and its role in continental rifting of the East Asia margin. Russ J Pac Geol 7:167–188. https://doi.org/10.1134/S181971401303007X
VanderPlas J, Prettenhofer P (2020) Species distribution modeling. Electronic documents. https://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html. Accessed 11 Jan 2020
Vázquez-Jiménez R, Romero-Calcerrada R, Arrogante-Funes R, Novillo CJ (2017) Topographic correction to landsat imagery through slope classification by applying the SCS + C method in mountainous forest areas. ISPRS Int J Geo-Inf 6:287. https://doi.org/10.3390/ijgi6090287
Xiao K, Li N, Porwal A, Holden EJ, Bagas L, Lu Y (2015) GIS-based 3D prospectivity mapping: a case study of Jiama copper-polymetallic deposit in Tibet, China. Ore Geol Rev 71:611–632. https://doi.org/10.1016/j.oregeorev.2015.03.001
Xiang J, Xiao K, Carranza EJM, Chen J, Li S (2020) 3D mineral prospectivity mapping with random forests: a case study of Tongling, Anhui, China. Nat Resour Res 29:395–414. https://doi.org/10.1007/s11053-019-09578-2
Yakubchuk AS (2009) Revised mesozoic-cenozoic orogenic architecture and gold metallogeny in the northern Circum-Pacific. Ore Geol Rev 35:447–454s. https://doi.org/10.1016/j.oregeorev.2009.03.002
Yang F, Xie S, Hao Z, Carranza EJM, Song Y, Liu Q, Xu R, Nie L, Han W, Wang C (2022) Geochemical quantitative assessment of mineral resource potential in the Da Hinggan Mountains in Inner Mongolia, China. Minerals 12(4):434. https://doi.org/10.3390/min12040434
Yarmolyuk VV, Kudryashov EA, Kozlovsky AM, Savatenkov VM (2011) Late cenozoic volcanic province in Central and East Asia. Petrology 19:327–347. https://doi.org/10.1134/S0869591111040072
Yousefi M, Carranza EJM (2015) Prediction are (P-A) plot and C-A fractal analysis to classify and maps for mineral prospectivity modelling. Comput and Geosci 79:69–81. https://doi.org/10.1016/j.cageo.2015.03.007
Zhang S, Xiao K, Carranza EJM, Yang F (2019) Maximum entropy and random forest modeling of mineral potential: analysis of gold prospectivity in the Hezuo-Meiwu District, West Qinling Orogen, China. Nat Resour Res 28(3):645–664. https://doi.org/10.1007/s11053-018-9425-0
Zhang Z, Wang G, Carranza EJM, Fan J, Liu X, Zhang X, Dong Y, Chang XP, Sha D (2022) An integrated framework for data-driven mineral prospectivity mapping using bagging-based positive- unlabeled learning and bayesian cost-sensitive logistic regression. Nat Resour Res. https://doi.org/10.1007/s11053-022-10120-0
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Sergei Shevyrev and Emmanuel John M. Carranza wrote the main manuscript text and Ekaterina V. Gorobeyko and Natalia G. Boriskina prepared figures. All authors made a substantial contributions to the conception of the work. All authors participated in drafting the work and revising it critically for important intellectual content. All authors read and approved the final manuscript. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
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Shevyrev, S., Gorobeyko, E.V., Carranza, E.J.M. et al. First-Pass prospectivity mapping for Au–Ag mineralization in Sikhote–Alin Superterrane, Southeast Russia through field sampling, image enhancement on ASTER data, and MaxEnt modeling. Earth Sci Inform 16, 695–716 (2023). https://doi.org/10.1007/s12145-022-00906-4
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DOI: https://doi.org/10.1007/s12145-022-00906-4