Skip to main content

Advertisement

SpringerLink
Log in

Gold Prospectivity Mapping in the Sonakhan Greenstone Belt, Central India: A Knowledge-Driven Guide for Target Delineation in a Region of Low Exploration Maturity

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

The Sonakhan greenstone belt in Central India is under-explored with respect to gold in spite of its similarity to auriferous greenstone belts in general, which prompted a prospectivity analysis. The workflow involved adoption of a conceptual mineral systems model, recognizing indicative spatial proxies, processing exploration datasets, generating evidence maps and integrating into GIS-based mineral prospectivity mapping. Available geological information such as key lithologic units and their contacts was combined with geochemical anomalies of selected pathfinder elements, geophysical data (aeromagnetic anomaly and K/Th ratio map) and satellite digital image data (ASTER and Landsat 7 ETM +), leading to generation of 17 evidential layers. The lack of a significant number of known mineral occurrences in the study area precludes the use of data-driven prospectivity modeling techniques. Therefore, knowledge-driven approaches such as binary and multiclass index overlay, fuzzy logic and fuzzy AHP (analytic hierarchy process) were adopted to integrate the evidential layers resulting in four prospectivity maps. The variation of cumulative prospectivity with respect to cumulative area in each model was used to determine threshold to produce binary prospectivity maps separating high and low prospectivity zones. An approach based on the unique conditions of the binary prospectivity maps was used to illustrate the combined results of different models. In order to quantify the intuitive uncertainty in exploration targeting that arose due to different model outputs, a modulated predictive model was generated taking the mean prospectivity values at each pixel. The pixels having mean values above 95th percentile were grouped and the area delineated as potential exploration targets for gold that comprises merely 5% of the study area. The estimated uncertainty and confidence values for each pixel were used in the risk analysis that returned 1.95% and 3.05% of the study area as low- and high-risk exploration targets, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17

Similar content being viewed by others

References

  • Abedi, M., Torabi, S. A., & Norouzi, G. H. (2013). Application of fuzzy AHP method to integrate geophysical data in a prospect scale, a case study: Seridune copper deposit. Bollettino Di GeofisicaTeorica Ed Applicata, 54(2), 145–164.

    Google Scholar 

  • Agterberg, F. (2011). A modified weights-of-evidence method for regional mineral resource estimation. Natural Resources Research, 20(2), 95–101.

    Article  Google Scholar 

  • Agterberg, F. P., Bonham-Carter, G. F., & Wright, D. F. (1990). Statistical pattern integration for mineral exploration. In Computer applications in resource estimation (pp. 1–21). Pergamon.

  • Agterberg, F. P., & Cheng, Q. (2002). Conditional independence test for weights-of-evidence modeling. Natural Resources Research, 11(4), 249–255.

    Article  Google Scholar 

  • Almasi, A., Yousefi, M., & Carranza, E. J. M. (2017). Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht goldfield, Zagros orogeny, Iran. Ore Geology Reviews, 91, 1066–1080.

    Article  Google Scholar 

  • Amer, R., El Mezayen, A., & Hasanein, M. (2016). ASTER spectral analysis for alteration minerals associated with gold mineralization. Ore Geology Reviews, 75, 239–251.

    Article  Google Scholar 

  • An, P., Moon, W. M., & Rencz, A. (1991). Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics, 27(1), 1–11.

    Google Scholar 

  • Ananaba, S. E., & Ajakaiye, D. E. (1987). Evidence of tectonic control of mineralization in Nigeria from lineament density analysis A Landsat-study. International Journal of Remote Sensing, 8(10), 1445–1453.

    Article  Google Scholar 

  • Austin, J. R., & Blenkinsop, T. G. (2009). Local to regional scale structural controls on mineralisation and the importance of a major lineament in the eastern Mount Isa Inlier, Australia: Review and analysis with autocorrelation and weights of evidence. Ore Geology Reviews, 35(3–4), 298–316.

    Article  Google Scholar 

  • Bai, H., Cao, Y., Zhang, H., Zhang, C., Hou, S., & Wang, W. (2021). Combining fuzzy analytic hierarchy process with concentration–area fractal for mineral prospectivity mapping: A case study involving Qinling orogenic belt in central China. Applied Geochemistry, 126, 104894.

    Article  Google Scholar 

  • Behera, S., & Panigrahi, M. K. (2021). Mineral prospectivity modelling using singularity mapping and multifractal analysis of stream sediment geochemical data from the auriferous Hutti-Maski schist belt, S. India. Ore Geology Reviews, 131, 104029.

    Article  Google Scholar 

  • Behera, S., Panigrahi, M. K., & Pradhan, A. (2019). Identification of geochemical anomaly and gold potential mapping in the Sonakhan Greenstone belt, Central India: An integrated concentration-area fractal and fuzzy AHP approach. Applied Geochemistry, 102, 45–57.

    Article  Google Scholar 

  • Bhatta, B. (2008). Remote sensing and GIS. Oxford University Press.

    Google Scholar 

  • Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: modeling with GIS. Pergamon.

    Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1988). Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetric Engineering and Remote Sensing, 54(11), 1585–1592.

    Google Scholar 

  • Bonham-Carter, G.F. (1989). Weights of evidence modeling: a new approach to mapping mineral potential. Statistical Applications in the Earth Sciences, 171–183.

  • Brunelli, M. (2014). Introduction to the analytic hierarchy process. Springer.

    Google Scholar 

  • Burkin, J. N., Lindsay, M. D., Occhipinti, S. A., & Holden, E. J. (2019). Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling. Geoscience Frontiers, 10(4), 1383–1396.

    Article  Google Scholar 

  • Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.

    Google Scholar 

  • Carranza, E. J. M. (2010). Mapping of anomalies in continuous and discrete fields of stream sediment geochemical landscapes. Geochemistry: Exploration, Environment, Analysis, 10(2), 171–187.

    Google Scholar 

  • Carranza, E. J. M., & Hale, M. (2001). Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research, 10(2), 125–136.

    Article  Google Scholar 

  • Carranza, E. J. M., & Hale, M. (2002). Mineral imaging with Landsat Thematic Mapper data for hydrothermal alteration mapping in heavily vegetated terrane. International Journal of Remote Sensing, 23(22), 4827–4852.

    Article  Google Scholar 

  • Carranza, E. J. M., Sadeghi, M., & Billay, A. (2015). Predictive mapping of prospectivity for orogenic gold, Giyani greenstone belt (South Africa). Ore Geology Reviews, 71, 703–718.

    Article  Google Scholar 

  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.

    Article  Google Scholar 

  • Cheng, Q., & Agterberg, F. P. (1999). Fuzzy weights of evidence method and its application in mineral potential mapping. Natural Resources Research, 8(1), 27–35.

    Article  Google Scholar 

  • Chiozzi, P., Pasquale, V., & Verdoya, M. (2007). Radiometric survey for exploration of hydrothermal alteration in a volcanic area. Journal of Geochemical Exploration, 93(1), 13–20.

    Article  Google Scholar 

  • Chudasama, B., Porwal, A., Kreuzer, O. P., & Butera, K. (2016). Geology, geodynamics and orogenic gold prospectivity modelling of the Paleoproterozoic Kumasi Basin, Ghana, West Africa. Ore Geology Reviews, 78, 692–711.

    Article  Google Scholar 

  • Cox, D. R., & Snell, E. J. (1989). Analysis of binary data. Monographs on statistics and applied probability 32.

  • Crosta, A. P., De Souza Filho, C. R., Azevedo, F., & Brodie, C. (2003). Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. International Journal of Remote Sensing, 24(21), 4233–4240.

    Article  Google Scholar 

  • Das, D. K., Razdan, P. N., & Sinha, N. K. (2011). A manual on chemical analysis of National Geochemical Mapping (NGCM) samples: Guidelines and package-wise analytical methods. Eastern Region Publication.

    Google Scholar 

  • Das, N., Royburman, K. J., Vatsa, U. S., Mahurkar, Y. V., & Dhoundial, D. P. (1990). Sonakhan schist belt, a precambrian granite-greenstone complex. Geological Survey of India, Special Publication, 28, 118–132.

    Google Scholar 

  • Davies, R. S., Groves, D. I., Trench, A., & Dentith, M. (2020). Towards producing mineral resource-potential maps within a mineral systems framework, with emphasis on Australian orogenic gold systems. Ore Geology Reviews, 119, 103369.

    Article  Google Scholar 

  • D’ercole, C., Groves, D. I., & Knox-Robinson, C. M. (2000). Using fuzzy logic in a geographic information system environment to enhance conceptually based prospectivity analysis of Mississippi Valley-type mineralisation. Australian Journal of Earth Sciences, 47(5), 913–927.

    Article  Google Scholar 

  • Deshmukh, S. D., Hari, K. R., Diwan, P., & Prasanth, M. M. (2018). Geochemical constraints on the tectonic setting of the sonakhan greenstone belt, Bastar craton, Central India. Acta Geochimica, 37(3), 489–499.

    Article  Google Scholar 

  • Ercan, Ö. A., Şeren, A., & Elmas, A. (2014). Gold and silver prospection using magnetic, radiometry and microgravity methods in the Kışladağ Province of Western Turkey. Resource Geology, 64(1), 25–34.

    Article  Google Scholar 

  • Fu, C., Chen, K., Yang, Q., Chen, J., Wang, J., Liu, J., Xiang, Y., Li, Y., & Rajesh, H. M. (2021). Mapping gold mineral prospectivity based on weights of evidence method in southeast Asmara. Eritrea. Journal of African Earth Sciences, 176, 104143.

    Article  Google Scholar 

  • Gabr, S., Ghulam, A., & Kusky, T. (2010). Detecting areas of high-potential gold mineralization using ASTER data. Ore Geology Reviews, 38(1–2), 59–69.

    Article  Google Scholar 

  • Geological Survey of India (2012). Geology and Mineral Resources of India. Miscellaneous Publication No. 30, Part-XXII.

  • Ghosh, S., Rajajaiya, V., & Ashiya, I. D. (1995). Rb-Sr dating of components from the Sonakhan granite-greenstone belt, Raipur district. MP Records Geological Survey of India, 128, 11–13.

    Google Scholar 

  • Groves, D. I., Santosh, M., Deng, J., Wang, Q., Yang, L., & Zhang, L. (2020). A holistic model for the origin of orogenic gold deposits and its implications for exploration. Mineralium Deposita, 55(2), 275–292.

    Article  Google Scholar 

  • Hagemann, S. G., Lisitsin, V. A., & Huston, D. L. (2016). Mineral system analysis: Quo vadis. Ore Geology Reviews, 76, 504–522.

    Article  Google Scholar 

  • Haidarian Shahri, M. R., Karimpour, M. H., & Malekzadeh, A. (2010). The exploration of gold by magnetic method in Hired Area, South Khorasan, a case study. Journal of the Earth & Space Physics, 35(4), 33–44.

    Google Scholar 

  • Haldar, S. K. (2018). Mineral exploration: Principles and applications. Elsevier.

    Google Scholar 

  • Harris, J. R., Grunsky, E., Behnia, P., & Corrigan, D. (2015). Data-and knowledge-driven mineral prospectivity maps for Canada’s North. Ore Geology Reviews, 71, 788–803.

    Article  Google Scholar 

  • Hengl, T. (2006). Finding the right pixel size. Computers & Geosciences., 32, 1283–1298.

    Article  Google Scholar 

  • Hronsky, J. M. A., & Groves, D. I. (2008). The science of targeting: Definition, strategies, targeting and performance measurement. Australian Journal of Earth Sciences, 55(1), 3–12.

    Article  Google Scholar 

  • Hronsky, J. M., Groves, D. I., Loucks, R. R., & Begg, G. C. (2012). A unified model for gold mineralisation in accretionary orogens and implications for regional-scale exploration targeting methods. Mineralium Deposita, 47(4), 339–358.

    Article  Google Scholar 

  • Hronsky, J. M., & Kreuzer, O. P. (2019). Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future. Ore Geology Reviews, 107, 647–653.

    Article  Google Scholar 

  • Indian Minerals Yearbook (2019) Gold (Advance release) In: Part-II: Metals and alloys. 58th Edition. Government of India Ministry of Mines, Indian Bureau of Mines. (https://ibm.gov.in/writereaddata/files/10142020121942Gold_2019_AR.pdf)

  • Jain, A., & Biswal, P. C. (2019). Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India. Resources Policy, 61, 501–507.

    Article  Google Scholar 

  • Joly, A., Porwal, A., & McCuaig, T. C. (2012). Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: Mineral system analysis, targeting model and prospectivity analysis. Ore Geology Reviews, 48, 349–383.

    Article  Google Scholar 

  • Kabir, G., & Hasin, M. A. A. (2011). Comparative analysis of AHP and fuzzy AHP models for multicriteria inventory classification. International Journal of Fuzzy Logic Systems, 1(1), 1–16.

    Google Scholar 

  • Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87(2), 171–184.

    Article  Google Scholar 

  • Karimi, A. R., Mehrdadi, N., Hashemian, S. J., Nabi-Bidhendi, G. R., & Tavakkoli-Moghaddam, R. (2011). Using of the fuzzy TOPSIS and fuzzy AHP methods for wastewater treatment process selection. International Journal of Academic Research, 3(1), 780–786.

    Google Scholar 

  • Kesler, S. E., Simon, A. C., & Simon, A. F. (2015). Mineral resources, economics and the environment. Cambridge University Press.

    Book  Google Scholar 

  • Knox-Robinson, C. M., & Wyborn, L. A. I. (1997). Towards a holistic exploration strategy: Using geographic information systems as a tool to enhance exploration. Australian Journal of Earth Sciences, 44(4), 453–463.

    Article  Google Scholar 

  • Koçal, A., Duzgun, H. S., & Karpuz, C. (2004). Discontinuity mapping with automatic lineament extraction from high resolution satellite imagery. ISPRS XX, Istanbul, 12–23.

  • Kohanpour, F., Occhipinti, S., Lindsay, M., Gorczyk, W., Jourdan, F., & Poujol, M. (2020). Mineral systems prospectivity modelling for gold and nickel in the Halls Creek Orogen, Western Australia. Ore Geology Reviews, 103809.

  • Laznicka, P. (1999). Quantitative relationship among giant deposits of metals. Economic Geology, 94, 455–473.

    Article  Google Scholar 

  • Leung, L. C., & Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, 124(1), 102–113.

    Article  Google Scholar 

  • Lima, A., De Vivo, B., Cicchella, D., Cortini, M., & Albanese, S. (2003). Multifractal IDW interpolation and fractal filtering method in environmental studies: An application on regional stream sediments of (Italy), Campania Region. Applied Geochemistry, 18(12), 1853–1865.

    Article  Google Scholar 

  • Lima, A., Plant, J. A., De Vivo, B., Tarvainen, T., Albanese, S., & Cicchella, D. (2008). Interpolation methods for geochemical maps: a comparative study using arsenic data from European stream waters. Geochemistry: Exploration, Environment, Analysis, 8(1), 41–48.

    Google Scholar 

  • Lindsay, M. D., Betts, P. G., & Ailleres, L. (2014). Data fusion and porphyry copper prospectivity models, southeastern Arizona. Ore Geology Reviews, 61, 120–140.

    Article  Google Scholar 

  • Lisitsin, V. A., González-Álvarez, I., & Porwal, A. (2013). Regional prospectivity analysis for hydrothermal-remobilised nickel mineral systems in western Victoria, Australia. Ore Geology Reviews, 52, 100–112.

    Article  Google Scholar 

  • Lisitsin, V. A., Porwal, A., & McCuaig, T. C. (2014). Probabilistic fuzzy logic modeling: Quantifying uncertainty of mineral prospectivity models using Monte Carlo simulations. Mathematical Geosciences, 46(6), 747–769.

    Article  Google Scholar 

  • Loughlin, W. P. (1991). Principal component analysis for alteration mapping. Photogrammetric Engineering and Remote Sensing, 57(9), 1163–1169.

    Google Scholar 

  • Lord, D., Etheridge, M., Willson, M., Hall, G., & Uttley, P. (2001). Measuring exploration success: An alternate to the discovery cost-per-ounce method of quantifying exploration effectiveness. SEG Newsletter, 45(1), 10–16.

    Google Scholar 

  • Manu Prasanth, M. P., Hari, K. R., Chalapathi Rao, N. V., Santosh, M., Hou, G., Tsunogae, T., & Pandit, D. (2018). Neoarchean suprasubduction zone magmatism in the Sonakhan greenstone belt, Bastar Craton, India: Implications for subduction initiation and melt extraction. Geological Journal, 53(4), 1526–1542.

    Article  Google Scholar 

  • Mars, J. C., & Rowan, L. C. (2006). Regional mapping of phyllic- and argillic-altered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere, 2(3), 161–186.

    Article  Google Scholar 

  • Masoud, A. A., & Koike, K. (2011). Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data. ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), 818–832.

    Article  Google Scholar 

  • McCuaig, T. C., Beresford, S., & Hronsky, J. (2010). Translating the mineral systems approach into an effective exploration targeting system. Ore Geology Reviews, 38(3), 128–138.

    Article  Google Scholar 

  • McCuaig, T. C., & Hronsky, J. M. (2014). The mineral system concept: The key to exploration targeting. Society of Economic Geologists Special Publication, 18, 153–175.

    Google Scholar 

  • McCuaig, T. C., & Kerrich, R. (1998). P—T—t—deformation—fluid characteristics of lode gold deposits: Evidence from alteration systematics. Ore Geology Reviews, 12(6), 381–453.

    Article  Google Scholar 

  • Ministry of Mines, Government of India (2015). Exploration and Mining Opportunities in India. Retrieved July 3, 2021, from https://mines.gov.in/writereaddata/UploadFile/ExplorationandMiningEbook.pdf

  • Mishra, B., Pruseth, K. L., Hazarika, P., & Chinnasamy, S. S. (2018). Nature and source of the ore-forming fluids associated with orogenic gold deposits in the Dharwar Craton. Geoscience Frontiers, 9(3), 715–726.

    Article  Google Scholar 

  • Mishra, V.K. (1996). Status report of gold deposits of Sonakhan, Raipur, Madhya Pradesh. In: Gold'96 Held at NGRI, Hyderabad, Sem. Vol, pp. 196–204.

  • Mondal, M., & Raza, M. (2009). Tectonomagmatic evolution of the Bastar craton of Indian shield through plume-arc interaction: evidence from geochemistry of the mafic and felsic volcanic rocks of Sonakhan greenstone belt. Eds.) Talat Ahmad, Francis Hirsch, and Punya Charusiri, Journal of the Virtual Explorer, 32, 3.

  • Mu, E., & Pereyra-Rojas, M. (2017). Practical decision making using super decisions v3: An introduction to the analytic hierarchy process. Berlin: Springer.

    Book  Google Scholar 

  • Najafi, A., Karimpour, M. H., & Ghaderi, M. (2014). Application of fuzzy AHP method to IOCG prospectivity mapping: A case study in Taherabad prospecting area, eastern Iran. International Journal of Applied Earth Observation and Geoinformation, 33, 142–154.

    Article  Google Scholar 

  • Occhipinti, S. A., Metelka, V., Lindsay, M. D., Hollis, J. A., Aitken, A. R., Tyler, I. M., Miller, J. M., & McCuaig, T. C. (2016). Multicommodity mineral systems analysis highlighting mineral prospectivity in the Halls Creek Orogen. Ore Geology Reviews, 72, 86–113.

    Article  Google Scholar 

  • Parsa, M. (2021). A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted Zn-Pb mineral systems of Western Iran. Journal of Geochemical Exploration, 228, 106811.

  • Parsa, M., & Carranza, E. J. M. (2021). Modulating the Impacts of Stochastic Uncertainties Linked to Deposit Locations in Data-Driven Predictive Mapping of Mineral Prospectivity. Natural Resources Research, 1–17.

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2017a). An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets. International Journal of Applied Earth Observation and Geoinformation, 58, 157–167.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Carranza, E. J. M. (2017b). Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: Implications for mapping exploration targets. Journal of African Earth Sciences, 128, 5–15.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2016). Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. Journal of African Earth Sciences, 114, 228–241.

    Article  Google Scholar 

  • Parsa, M., & Pour, A. B. (2021). A simulation-based framework for modulating the effects of subjectivity in greenfield mineral prospectivity mapping with geochemical and geological data. Journal of Geochemical Exploration, 229, 106838.

    Article  Google Scholar 

  • Porwal, A., & Carranza, E. J. M. (2015). Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration. Ore Geology Review, 71, 477–483.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, 12(1), 1–25.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2004). A hybrid neuro-fuzzy model for mineral potential mapping. Mathematical Geology, 36(7), 803–826.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2006). A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Natural Resources Research, 15(1), 1–14.

    Article  Google Scholar 

  • Porwal, A., & González-Álvarez, I. (2019). Reprint of: Introduction to special issue on geologic remote sensing. Ore Geology Reviews, 108, 1–7.

    Article  Google Scholar 

  • Porwal, A., Gonzalez-Alvarez, I., Markwitz, V., McCuaig, T. C., & Mamuse, A. (2010). Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geology Reviews, 38(3), 184–196.

    Article  Google Scholar 

  • Pour, A. B., & Hashim, M. (2012). The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geology Reviews, 44, 1–9.

    Article  Google Scholar 

  • Ramachandra, H.M., & Vasudev, V.N. (2019). February 15–16. Gold mining in India: the way forward. Final Report on the proceedings of the national seminar organised by Geological Society of India. Retrieved July 3, 2021, from http://www.geosocindia.org/public/journals/253/docs/FinalReportSeminar_website.pdf

  • Ramakrishnan, M., & Vaidyanadhan, R. (2008). Geology of India (Vol. 1). Geological Society of India.

    Google Scholar 

  • Ray, R. K., & Rai, K. L. (2004). Geological setting and petrogenesis of the auriferous metavolcanic complex of Sonakhan, Raipur district, Chhattisgarh. SAAEG J Ecol Geol, 1, 45–60.

    Google Scholar 

  • Ray, R. K., Pandey, H. K., & Rai, K. L. (2000). Geochemistry of Mafic volcanics associated with sulphide mineralization in Sonakhan, Raipur district, Madhya Pradesh. Proc nat Sem on ‘Tectonomagmatism, Geochemistry and Metamorphism of Precambrian terrain’. Univ Dept of Geology Udaipur, 381–393.

  • Roberts, R. (1987). Ore deposit models# 11. Archean Lode gold deposits. Geoscience Canada, 14(1), 37–52.

    Google Scholar 

  • Rowan, L. C., & Bowers, T. L. (1995). Analysis of Linear features mapped in landsat thematic mapper and side-looking airborne radar lmages of the Reno 1˚ by 2˚ quadrangle, nevada and california: Lmplications for mineral resource studies. Photogrammetric Engineering & Remote Sensing, 61(6), 749–759.

    Google Scholar 

  • Rowan, L. C., & Wetlaufer, P. H. (1981). Relation between regional lineament systems and structural zones in Nevada. AAPG Bulletin, 65(8), 1414–1432.

    Google Scholar 

  • Rowan, L. C., & Mars, J. C. (2003). Lithologic mapping in the Mountain Pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sensing of Environment, 84(3), 350–366.

    Article  Google Scholar 

  • Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281.

    Article  Google Scholar 

  • Sabins, F. F. (1999). Remote sensing for mineral exploration. Ore Geology Reviews, 14(3–4), 157–183.

    Article  Google Scholar 

  • Sadeghi, B., & Khalajmasoumi, M. (2015). A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment. Renewable and Sustainable Energy Reviews, 43, 818–831.

    Article  Google Scholar 

  • Sahoo, H. B., Gandre, D. K., Das, P. K., Karim, M. A., & Bhuyan, G. C. (2018). Geochemical mapping of heavy metals around Sukinda-Bhuban area in Jajpur and Dhenkanal districts of Odisha, India. Environmental Earth Sciences, 77(2), 1–17.

    Article  Google Scholar 

  • Sanusi, S. O., & Amigun, J. O. (2020). Logistic-based translation of orogenic gold forming processes into Mappable exploration criteria for fuzzy logic mineral exploration targeting in the Kushaka Schist Belt, North-Central Nigeria. Natural Resources Research, 29, 3505–3526.

    Article  Google Scholar 

  • Sedrette, S., & Rebaï, N. (2016). Automatic extraction of lineaments from Landsat ETM+ images and their structural interpretation: Case study in Nefza region (North West of Tunisia). Journal of Research in Environmental and Earth Sciences, 4, 139–145.

    Google Scholar 

  • Sheikhrahimi, A., Pour, A. B., Pradhan, B., & Zoheir, B. (2019). Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran. Advances in Space Research, 63(10), 3315–3332.

    Article  Google Scholar 

  • Schoenberger, E. (2011). Why is gold valuable? Nature, social power and the value of things. Cultural Geographies, 18(1), 3–24.

    Article  Google Scholar 

  • Shellnutt, J. G., Hari, K. R., Liao, A. Y., Denyszyn, S. W., Vishwakarma, N., & Deshmukh, S. D. (2019). Petrogenesis of the 1.85 Ga Sonakhan mafic dyke swarm, Bastar Craton, India. Lithos, 334, 88–101.

    Article  Google Scholar 

  • Sridhar, M., Babu, V. R., Chaturvedi, A. K., & Roy, M. K. (2015). Predictive GIS modeling from Landsat, AGRS, aeromagnetic and ground surveys for uranium exploration—A case study from Sonakhan Block, Chhattisgarh, India. Journal of the Indian Society of Remote Sensing, 43(2), 347–362.

    Article  Google Scholar 

  • Sun, T., Chen, F., Zhong, L., Liu, W., & Wang, Y. (2019). GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China. Ore Geology Reviews, 109, 26–49.

    Article  Google Scholar 

  • Thannoun, R. G. (2013). Automatic extraction and geospatial analysis of lineaments and their tectonic significance in some areas of Northern Iraq using remote sensing techniques and GIS. International Journal of Enhanced Research in Science Technology & Engineering Bulletin, 2(2), 1–11.

    Google Scholar 

  • Venkatesh, A. S. (2001). Geochemical signatures and auriferous implications in Sonakhan greenstone belt, Chhattisgarh. Geological Survey of India Special Publication, 55, 219–228.

    Google Scholar 

  • Wemegah, D. D., Preko, K., Noye, R. M., Boadi, B., Menyeh, A., Danuor, S. K., & Amenyoh, T. (2015). Geophysical interpretation of possible gold mineralization zones in Kyerano, south-western Ghana using aeromagnetic and radiometric datasets. Journal of Geoscience and Environment Protection, 3(4), 67–82.

    Article  Google Scholar 

  • Wilburn, D. R., & Karl, N. A. (2017). Exploration review. Mining Engineering, 70(5), 28–50.

    Google Scholar 

  • Williams, P. R., & Currie, K. L. (1993). Character and regional implications of the sheared Archaean granite-greenstone contact near Leonora, Western Australia. Precambrian Research, 62(3), 343–365.

    Article  Google Scholar 

  • Woodall, R. (1984). Success in mineral exploration: Confidence in prospectivity. Geoscience Canada, 11(2), 83–90.

    Google Scholar 

  • Woodall, R. (1988). The role of mineral exploration toward the year 2000. Geophysics, 7(2), 35–37.

    Google Scholar 

  • Woodall, R. (1992). Challenge of minerals exploration in the 1990s. Mining Engineering, 44, 679–683.

    Google Scholar 

  • Woodall, R. (1994). Empiricism and concept in successful mineral exploration. Australian Journal of Earth Sciences, 41, 1–10.

    Article  Google Scholar 

  • World Gold Council (2017). India’s gold market: evolution and innovation. Retrieved July 3, 2021, from https://www.gold.org/goldhub/research/india-gold-market

  • World Gold Council (2019). Gold Demand Trends Full Year and Q4 2018. Retrieved July 3, 2021, https://www.gold.org/download/file/13554/gdt-fy-2018.pdf

  • Wyborn, L. A. I., Heinrich, C. A., & Jaques, A. L. (1994, August). Australian Proterozoic mineral systems: essential ingredients and mappable criteria. In The AusIMM Annual Conference (Vol. 1994, pp. 109–115). AusIMM Darwin.

  • Wyman, D. A., Cassidy, K. F., & Hollings, P. (2016). Orogenic gold and the mineral systems approach: Resolving fact, fiction and fantasy. Ore Geology Reviews, 78, 322–335.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015). Prediction–area (P–A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69–81.

    Article  Google Scholar 

  • Yousefi, M., Carranza, E. J. M., & Kamkar-Rouhani, A. (2013). Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling. Journal of Geochemical Exploration, 128, 88–96.

    Article  Google Scholar 

  • Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2012). Geochemical mineralization probability index (GMPI): A new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24–35.

    Article  Google Scholar 

  • Yousefi, M., & Nykänen, V. (2016). Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. Journal of Geochemical Exploration, 164, 94–106.

    Article  Google Scholar 

  • Yousefi, M., & Nykänen, V. (2017). Introduction to the special issue: GIS-based mineral potential targeting. Journal of African Earth Sciences, 12, 1–4.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  Google Scholar 

  • Zimmerman, H. J. (1996). Fuzzy set theory and its applications. Allied Publishers.

    Book  Google Scholar 

  • Zuo, R., Cheng, Q., & Agterberg, F. P. (2009). Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity. Ore Geology Reviews, 35(1), 101–108.

    Article  Google Scholar 

  • Zhang, X., Pazner, M., & Duke, N. (2007). Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California). ISPRS Journal of Photogrammetry and Remote Sensing, 62(4), 271–282.

    Article  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the Geological Survey of India (GSI) for providing access to the stream sediment geochemical data required for this research. We also acknowledge the USGS EROS data center for providing the ASTER and Landsat ETM+ data used in this research. The authors are also thankful to the Atomic Minerals Directorate for Exploration and Research of the Department of Atomic Energy, Government of India, and Sridhar et al. (2015) whose airborne geophysical survey results have been used in this study. ESRI India is also acknowledged for providing the geospatial software such as ArcGIS, ENVI that are utilized during the preparation of this manuscript. We gratefully acknowledge Mohammad Parsa and an anonymous reviewer for their constructive comments which have helped us in improving the quality of our work and presentation significantly.

Author information

Authors and Affiliations

  1. Department of Geology and Geophysics, Indian Institute of Technology, Kharagpur, WB, 721302, India

    Satyabrata Behera & Mruganka K. Panigrahi

Authors
  1. Satyabrata Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mruganka K. Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mruganka K. Panigrahi.

Appendix A: Pairwise Comparison Matrices for Fuzzy AHP

Appendix A: Pairwise Comparison Matrices for Fuzzy AHP

A detailed discussion on the steps for applying fuzzy AHP method along with the associated equations can be found in Behera et al. (2019). Interested readers are referred to Saaty (1977), Chang (1996), Brunnelli (2014) and Enrique Mu (2017). In the following section, the pairwise comparison matrices (PCMs) have been given for geological, geochemical and geophysical and satellite image processing results (Table 8). Three different sets of judgements on the relative importance of each input evidence layers were incorporated in the analysis. Saaty's nine-point pairwise comparison scale was used to quantify the relative preferences of the alternatives in pairwise comparison matrices (PCMs). The consistency ratios (CRs) for the PCMs were calculated using Super Decisions V3, a freely available software developed by the Creative Decisions Foundation and it was ensured that all the CRs are less than 0.10 which approves the plausibility of the quantified subjective judgements used in this study. The triangular fuzzy numbers (TFN), fuzzy synthetic extents, priority vectors and normalized weight vectors were calculated using the respective equations (Behera et al., 2019). Finally, the corresponding weights for criteria and alternatives were derived (Table 7 in the manuscript) and used for gold prospectivity mapping.

See Table

Table 9 Pairwise comparison matrices (PCMs) for criteria and alternatives used in this study (refer to the acronyms given at the end of the table)
Full size table

9.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behera, S., Panigrahi, M.K. Gold Prospectivity Mapping in the Sonakhan Greenstone Belt, Central India: A Knowledge-Driven Guide for Target Delineation in a Region of Low Exploration Maturity. Nat Resour Res 30, 4009–4045 (2021). https://doi.org/10.1007/s11053-021-09962-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-021-09962-x

Keywords

Search

Navigation