Skip to main content

Advertisement

SpringerLink
Log in

Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration

  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Index overlay and Boolean logic are two techniques customarily applied for knowledge-driven modeling of prospectivity for mineral deposits, whereby weights of values in evidential maps and weights of every evidence map are assigned based on expert opinion. In the Boolean logic technique for mineral prospectivity modeling (MPM), threshold evidential values for creating binary maps are defined based on expert opinion as well. This practice of assigning weights based on expert opinion involves trial-and-error and introduces bias in evaluating relative importance of both evidential values and individual evidential maps. In this paper, we propose a data-driven index overlay MPM technique whereby weights of individual evidential maps are derived from data. We also propose a data-driven Boolean logic MPM technique, whereby thresholds for creating binary maps are defined based on data. For assigning weights and defining thresholds in these proposed data-driven MPM techniques, we applied a prediction-area plot from which we can estimate the predictive ability of each evidential map with respect to known mineral occurrences, and we use that predictive ability estimate to assign weights to evidential map and to select thresholds for generating binary predictor maps. To demonstrate these procedures, we applied them to an area in the Kerman province in southeast Iran as a MPM case study for porphyry-Cu deposits.

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

Similar content being viewed by others

References

  • Afzal, P., Fadakar Alghalandis, Y., Khakzad, A., Moarefvand, P., & Rashidnejad Omran, N. (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. Journal of Geochemical Exploration, 108, 220–232.

    Article  Google Scholar 

  • Agard, P., Omrani, J., Jolivet, L., & Mouthereau, F. (2005). Convergence history across Zagros (Iran): Constraints from collisional and earlier deformation. International Journal of Earth Sciences, 94, 401–419.

    Article  Google Scholar 

  • Agterberg, F. P. (2007). Mixtures of multiplicative cascade models in geochemistry. Nonlinear Processes in Geophysics, 14, 201–209.

    Article  Google Scholar 

  • Alavi, M. (1980). Tectonostratigraphic evolution of the Zagrosides of Iran. Geology, 8, 144–149.

    Article  Google Scholar 

  • Arias, M., Gumiel, P., Sanderson, D. J., & Martin-Izard, A. (2011). A multifractal simulation model for the distribution of VMS deposits in the Spanish segment of the Iberian Pyrite Belt. Computers & Geosciences, 37, 1917–1927.

    Article  Google Scholar 

  • Arribas, A. J. (1995). Contemporaneous formation of adjacent porphyry and epithermal Cu-Au deposits over 300 ka in northern Luzon, Philippines. Geology, 23, 337–340.

    Article  Google Scholar 

  • Atapour, H., & Aftabi, A. (2007). The geochemistry of gossans associated with Sarcheshmeh porphyry copper deposit, Rafsanjan, Kerman, Iran: Implications for exploration and the environment. Journal of Geochemical Exploration, 93, 47–65.

    Article  Google Scholar 

  • Bayes, T. (1764). An essay toward solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.

  • Berberian, F., & Berberian, M. (1981). Tectono-plutonic episodes in Iran. In H.K. Gupta, & F.M. Delany (Eds.), Zagroz–Hindu Kush–Himalaya Geodynamic Evolution (pp. 5–32). Washington: American Geophysical Union & Geological Society of America.

  • Berberian, F., Muir, I. D., Pankhurst, R. J., & Berberian, M. (1982). Late Cretaceous and Early Miocene Andean-type plutonic activity in northern Makran and central Iran. Journal of the Geological Society of London, 139, 605–614.

    Article  Google Scholar 

  • Billa, M., Cassard, D., Lips, A. L. W., Bouchot, V., Tourliére, B., Stein, G., & Guillou-Frottier, L. (2004). Predicting gold-rich epithermal and porphyry systems in the central Andes with a continental-scale metallogenic GIS. Ore Geology Reviews, 25, 39–67.

    Article  Google Scholar 

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

    Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. In F. P. Agterberg & G. F. Bonham-Carter (Eds.), Statistical applications in the earth sciences, Geological Survey of Canada, Paper 89-9 (pp. 171–183).

  • Boomeri, M., Nakashima, K., & Lentz, D. R. (2009). The Miduk porphyry Cu deposit, Kerman, Iran: A geochemical analysis of the potassic zone including halogen element systematics related to Cu mineralization processes. Journal of Geochemical Exploration, 103, 17–29.

    Article  Google Scholar 

  • Carranza, E. J. M. (2004). Weights of evidence modeling of mineral potential: A case study using small number of prospects, Abra, Philippines. Natural Resources Research, 13, 173–187.

    Article  Google Scholar 

  • Carranza, E. J. M. (2008a). Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geology Reviews, 35, 383–400.

    Article  Google Scholar 

  • Carranza, E. J. M. (2008b). Geochemical anomaly and mineral prospectivity mapping in GIS. In Handbook of exploration and environmental geochemistry (Vol. 11, p. 351). Amsterdam: Elsevier.

  • Carranza, E. J. M. (2009). Improved wildcat modeling of mineral prospectivity. Resource Geology, 60, 129–149.

    Article  Google Scholar 

  • Carranza, E. J. M. (2010a). Catchment basin modelling of stream sediment anomalies revisited: Incorporation of EDA and fractal analysis. Geochemistry: Exploration, Environment, Analysis, 10, 365–381.

    Google Scholar 

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

    Google Scholar 

  • Carranza, E. J. M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110, 167–185.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Carranza, E. J. M., & Hale, M. (2002a). Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Natural Resources Research, 11, 45–59.

    Article  Google Scholar 

  • Carranza, E. J. M., & Hale, M. (2002b). Wildcat mapping of gold potential, Baguio district, Philippines. Transactions of the Institution of Mining and Metallurgy (Section B: Applied Earth Science), 111, 100–105.

    Google Scholar 

  • Carranza, E. J. M., Hale, M., & Faassen, C. (2008a). Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geology Reviews, 33, 536–558.

    Article  Google Scholar 

  • Carranza, E. J. M., van Ruitenbeek, F. J. A., Hecker, C., van der Meijde, M., & van der Meer, F. D. (2008b). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation, 10, 374–387.

    Article  Google Scholar 

  • Carranza, E. J. M., Owusu, E., & Hale, M. (2009). Mapping of prospectivity and estimation of number of undiscovered prospects for lode-gold, southwestern Ashanti Belt, Ghana. Mineralium Deposita, 44, 915–938.

    Article  Google Scholar 

  • Carranza, E. J. M., & Sadeghi, M. (2010). Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden). Ore Geology Reviews, 38, 219–241.

    Article  Google Scholar 

  • Carranza, E. J. M., & Sadeghi, M. (2014). Post-VMS mineralization deformations (1.88–1.82 Ga) of the Skellefte district (Sweden): Insights from the spatial pattern of VMS occurrences. Frontiers of Earth Science, 8, 319–324.

    Article  Google Scholar 

  • Carranza, E. J. M., Woldai, T., & Chikambwe, E. M. (2005). Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. Natural Resources Research, 14, 47–63.

    Article  Google Scholar 

  • Chen, Z., Zhang, L., Wan, B., Wu, H., & Cleven, N. (2011). Geochronology and geochemistry of the Wunugetushan porphyry Cu–Mo deposit in NE china, and their geological significance. Ore Geology Reviews, 43, 92–105.

    Article  Google Scholar 

  • Cheng, Q. (1995). The perimeter-area fractal model and its application to geology. Mathematical Geology, 27, 69–82.

    Article  Google Scholar 

  • Cheng, Q. (1999). Multifractality and spatial statistics. Computers & Geosciences, 25, 949–961.

    Article  Google Scholar 

  • Cheng, Q. (2007). Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32, 314–324.

    Article  Google Scholar 

  • Cheng, Q., & Agterberg, F. P. (1995). Multifractal modeling and spatial point processes. Mathematical Geology, 27, 831–845.

    Article  Google Scholar 

  • Cheng, Q., & Agterberg, F. P. (2009). Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Computers & Geosciences, 35, 34–244.

    Google Scholar 

  • Cheng, Q., Agterberg, F. P., & Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109–130.

    Article  Google Scholar 

  • Cheng, Q., Agterberg, F. P., & Bonham-Carter, G. F. (1996). A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration, 56, 183–195.

    Article  Google Scholar 

  • Cheng, Q., Xia, Q., Li, W., Zhang, S., Chen, Z., Zuo, R., & Wang, W. (2010). Density/area power law models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China. Biogeosciences, 7, 3019–3025.

    Article  Google Scholar 

  • Chico-Olmo, M., Abarca, F., & Rigol, J. P. (2002). Development of a decision support system based on remote sensing and GIS techniques for gold-rich area identification in SE Spain. International Journal of Remote Sensing, 23, 4801–4814.

    Article  Google Scholar 

  • De Araújo, C. C., & Macedo, A. B. (2002). Multicriteria geologic data analysis for mineral favorability mapping: Application to a metal sulphide mineralized area, Ribeira Valley metallogenic province, Brazil. Natural Resources Research, 11, 29–43.

    Article  Google Scholar 

  • Deng, J., Wang, Q. F., Wan, L., Liu, H., Yang, L. Q., & Zhang, J. (2011). A multifractal analysis of mineralization characteristics of the Dayingezhuang disseminated-veinlet gold deposit in the Jiaodong gold province of China. Ore Geology Reviews, 40, 54–64.

    Article  Google Scholar 

  • Deng, J., Wang, Q., Wan, L., Yang, L., Gong, Q., Zhao, J., & Liu, H. (2009). Self-similar fractal analysis of gold mineralization of Dayingezhuang disseminated-veinlet deposit in Jiaodong gold province, China. Journal of Geochemical Exploration, 102, 95–102.

    Article  Google Scholar 

  • Deng, J., Wang, Q., Yang, L., Wang, Y., Gong, Q., & Liu, H. (2010). Delineation and explanation of geochemical anomalies using fractal models in the Heqing area, Yunnan Province, China. Journal of Geochemical Exploration, 105, 95–105.

    Article  Google Scholar 

  • Ford, A., & Blenkinsop, T. G. (2008). Evaluating geological complexity and complexity gradients as controls on copper mineralization, Mt Isa Inlier. Australian Journal of Earth Sciences, 55, 13–23.

    Article  Google Scholar 

  • Ford, A., & Blenkinsop, T. G. (2009). An expanded de Wijs model for multifractal analysis of mineral production data. Mineralium Deposita, 44, 233–240.

    Article  Google Scholar 

  • Ghasemi, A., & Talbot, C. J. (2006). A new tectonic scenario for the Sanandaj–Sirjan Zone (Iran). Journal of Asian Earth Sciences, 26, 683–693.

    Article  Google Scholar 

  • Gholami, R., Moradzadeh, A., & Yousefi, M. (2012). Assessing the performance of independent component analysis in remote sensing data processing. Journal of the Indian Society of Remote Sensing, 40, 577–588.

    Article  Google Scholar 

  • Good, I. J. (1950). Probability and the weighting of evidence. London: Griffin. 119p.

    Google Scholar 

  • Grabeljsek, V. (1956). Geological Map of Sabzevaran (1:100,000), Geological Survey of Iran publication.

  • Guillou-Frottier, L., & Burov, E. (2003). The development and fracturing of plutonic apexes: Implications for porphyry ore deposits. Earth and Planetary Science Letters, 214, 341–356.

    Article  Google Scholar 

  • Gumiel, P., Sanderson, D. J., Arias, M., Roberts, S., & Martin-Izard, A. (2010). Analysis of the fractal clustering of ore deposits in the Spanish Iberian Pyrite Belt. Ore Geology Reviews, 38, 307–318.

    Article  Google Scholar 

  • Hanuš, V., Vaněk, J., & Špičák, A. (2000). Seismically active fracture zones and distribution of large accumulations of metals in the central part of Andean South America. Mineralium Deposita, 35, 2–20.

    Article  Google Scholar 

  • Harris, J. R., Wilkinson, L., Heather, K., Fumerton, S., Bernier, M. A., Ayer, J., & Dahn, R. (2001). Application of GIS processing techniques for producing mineral prospectivity maps—A case study: Mesothermal Au in the Swayze Greenstone Belt, Ontario, Canada. Natural Resources Research, 10, 91–124.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hezarkhani, A. (2006a). Mineralogy and fluid inclusion investigations in the Reagan Porphyry System, Iran, the path to an uneconomic porphyry copper deposit. Journal of Asian Earth Sciences, 27, 598–612.

    Article  Google Scholar 

  • Hezarkhani, A. (2006b). Petrology of the intrusive rocks within the Sungun Porphyry Copper Deposit, Azerbaijan, Iran. Journal of Asian Earth Sciences, 27, 326–340.

    Article  Google Scholar 

  • Hezarkhani, A. (2009). Hydrothermal fluid geochemistry at the Chah-Firuzeh porphyry copper deposit, Iran: Evidence from fluid inclusions. Journal of Geochemical Exploration, 101, 254–264.

    Article  Google Scholar 

  • Hosseinali, F., & Alesheikh, A. A. (2008). Weighting spatial information in GIS for copper mining exploration. American Journal of Applied Sciences, 5, 1187–1198.

    Article  Google Scholar 

  • Jaccard, P. (1908). Nouvelles recherché sur la distribution florale. Bulletin Societe Vaudoise des Sciences Naturelles, 44, 223–270.

    Google Scholar 

  • Lindsay, M., 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 

  • Lusty, P. A. J., Scheib, C., Gunn, A. G., & Walker, A. S. D. (2012). Reconnaissance-scale prospectivity analysis for gold mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland. Natural Resources Research, 21, 359–382.

    Article  Google Scholar 

  • Mandelbrot, B. B. (1977). Fractals: Form, chance, and dimension. San Francisco: Freeman. 365.

    Google Scholar 

  • Mandelbrot, B. B. (1983). The fractal geometry of nature, (updated and augmented edition). New York: Freeman. 468.

    Google Scholar 

  • Mandelbrot, B. B., Passoja, D. E., & Paullay, A. J. (1984). Fractal character of fracture surfaces of metals. Nature, 308(5961), 721–722.

    Article  Google Scholar 

  • Meshkani, S. A., Mehrabi, B., Yaghubpur, A., & Sadeghi, M. (2013). Recognition of the regional lineaments of Iran: Using geospatial data and their implications for exploration of metallic ore deposits. Ore Geology Reviews, 55, 48–63.

    Article  Google Scholar 

  • Moreira, F. R. S., Almeida-Filho, R., & Câmara, G. (2003). Spatial analysis techniques applied to mineral prospecting: An evaluation in the Poços de Caldas Plateau. Revista Brasileira de Geosciências, 33(2-Suppl.), 183–190.

  • 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 

  • Nykänen, V., Groves, D. I., Ojala, V. J., Eilu, P., & Gardoll, S. J. (2008). Reconnaissance scale conceptual fuzzy-logic prospectivity modeling for iron oxide copper—Gold deposits in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences, 55, 25–38.

    Article  Google Scholar 

  • Omrani, J., Agard, P., Whitechurch, H., Benoit, M., Prouteau, G., & Jolivet, L. (2008). Arc-magmatism and subduction history beneath the Zagros Mountains, Iran: A new report of adakites and geodynamic consequences. Lithos, 106, 380–398.

    Article  Google Scholar 

  • Panahi, A., & Cheng, Q. (2004). Multifractality as a measure of spatial distribution of geochemical patterns. Mathematical Geology, 36, 827–846.

    Article  Google Scholar 

  • Pazand, K., Hezarkhani, A., Ataei, M., & Ghanbari, Y. (2011). Combining AHP with GIS for predictive Cu porphyry potential mapping: A case study in Ahar area (NW, Iran). Natural Resources Research, 20, 251–262.

    Article  Google Scholar 

  • Pirajno, F. (2010). Intracontinental strike-slip faults, associated magmatism, mineral systems and mantle dynamics: Examples from NW China and Altay-Sayan (Siberia). Journal of Geodynamics, 50, 325–346.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003a). Extended weights-of-evidence modelling for predictive mapping of base metal deposit potential in Aravalli province, western India. Exploration and Mining Geology, 10, 155–163.

    Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003b). Artificial neural networks for mineral potential mapping: A case study from Aravalli Province, Western India. Natural Resources Research, 12, 155–171.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003c). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, 12, 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, 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, 1–14.

    Article  Google Scholar 

  • Qu, X., Hou, Z., Zaw, K., & Youguo, L. (2007). Characteristics and genesis of Gangdese porphyry copper deposits in the southern Tibetan Plateau: Preliminary geochemical and geochronological results. Ore Geology Reviews, 31, 205–223.

    Article  Google Scholar 

  • Raines, G. L. (2008). Are fractal dimensions of the spatial distribution of mineral deposits meaningful? Natural Resource Research, 17, 87–97.

    Article  Google Scholar 

  • Rogge, D. M., Halden, N. M., & Beaumont-Smith, C. (2006). Application of data integration for shear hosted Au potential modeling: Lynn Lake Greenstone Belt, Northwestern Manitoba, Canada. In J. R. Harris (Ed.), GIS for the earth sciences: Geological Association of Canada Special Publication 44 (pp. 191–210). St. John’s: Geological Association of Canada.

    Google Scholar 

  • Roy, R., Cassard, D., Cobbold, P. R., Rossello, E. A., Billa, M., Bailly, L., & Lips, A. L. W. (2006). Predictive mapping for copper–gold magmatic-hydrothermal systems in NW Argentina: Use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS. Ore Geology Reviews, 29, 260–286.

    Article  Google Scholar 

  • Sengor, A. M. C., Altiner, D., Cin, A., Ustomer, T., & Hsu, K. J. (1988). The origin and assembly of the Tethyside orogenic collage at the expense of Gondwana land. In M. G. Audley-Charles & A. Hallam (Eds.), Gondwana and Tethys. Geological Society, (pp. 119–181). London: Special Publication, Geological Society.

  • Sillitoe, R. H. (1997). Characteristics and controls of the largest porphyry copper–gold and epithermal gold deposits in the circum-Pacific region. Australian Journal of Earth Sciences, 44, 373–388.

    Article  Google Scholar 

  • Sillitoe, R. H. (2010). Porphyry copper systems. Economic Geology, 105, 3–41.

    Article  Google Scholar 

  • Singer, D. A., Berger, V. I., & Moring, B. C. (2005). Porphyry copper deposits of the world: Database, map, grade and tonnage models. U.S. Geological Survey. Open-File Report (pp. 1005–1060).

  • Storti, F., Holdsworth, R. E., & Salvini, F. (2003). Intraplate strike-slip deformation belts. Geological Society, London, Special Publication, 210, 1–14.

    Article  Google Scholar 

  • Takin, M. (1972). Iranian geology and continental drift in the Middle East. Nature, 235, 147–150.

    Article  Google Scholar 

  • Tangestani, M. H., & Moore, F. (2002a). Porphyry copper alteration mapping in the Meiduk area, Iran. International Journal of Remote Sensing, 23, 4815–4825.

    Article  Google Scholar 

  • Tangestani, M. H., & Moore, F. (2002b). The use of Dempster-Shafer model and GIS in integration of geoscientific data for porphyry copper potential mapping, north of Shahr-e-Babak, Iran. International Journal of Applied Earth Observation and Geoinformation, 4, 65–74.

    Article  Google Scholar 

  • Thiart, C., & De Wit, M. (2000). Linking spatial statistics to GIS: Exploring potential gold and tin models of Africa. South African Journal of Geology, 103, 215–230.

    Article  Google Scholar 

  • Wang, Z., Cheng, Q., Cao, L., Xia, Q., & Chen, Z. (2006). Fractal modelling of the microstructure property of quartz mylonite during deformation process. Mathematical Geology, 39, 53–68.

    Article  Google Scholar 

  • Wang, Z., Cheng, Q., Xu, D., & Dong, Y. (2008). Fractal modeling of sphalerite banding in Jinding Pb–Zn deposit, Yunnan, Southwestern China. Journal of China University Geosciences, 19, 77–84.

    Article  Google Scholar 

  • Wang, Q., Deng, J., Wan, L., & Zhang, Z. (2011a). Fractal analysis of the ore-forming process in a skarn deposit: A case study in the Shizishan area, China. In A. N. Sial, Bettencourt, J. S., De Campos, C. P., Ferreira, V. P. (Eds.), Granite-related ore deposits: Geological Society, London, Special Publications (Vol. 350, pp. 89–104).

  • Wang, G., Zhang, S., Yan, C., Song, Y., Sun, Y., Li, D., & Xu, F. (2011b). Mineral potential targeting and resource assessment based on 3D geological modeling in Luanchuan region, China. Computers & Geosciences, 37, 1976–1988.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences, 74, 97–109.

    Article  Google Scholar 

  • Yousefi, M., Carranza, E. J. M., & Kamkar-Rouhani, A. (2013). Weighted drainage catchment basin mapping of stream sediment geochemical anomalies for mineral potential mapping. 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., Kamkar-Rouhani, A., & Carranza, E. J. M. (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochemistry: Exploration, Environmental, Analysis, 14, 45–58.

    Google Scholar 

  • Yule, G. U. (1912). On the methods of measuring association between two attributes. Journal of the Royal Statistical Society, 75, 579–642.

    Article  Google Scholar 

  • Zhao, J., Chen, S., Zuo, R., & Carranza, E. J. M. (2011). Mapping complexity of spatial distribution of faults using fractal and multifractal models: Vectoring towards exploration targets. Computers & Geosciences, 37, 1958–1966.

    Article  Google Scholar 

  • Ziaii, M., Carranza, E. J. M., & Ziaei, M. (2011). Application of geochemical zonality coefficients in weights-of-evidence modeling of mineral prospectivity. Computers & Geosciences, 37, 1935–1945.

    Article  Google Scholar 

  • Zuo, R. (2011a). Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Applied Geochemistry, 26, S271–S273.

    Article  Google Scholar 

  • Zuo, R. (2011b). Identifying geochemical anomalies associated with Cu and Pb\Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration, 111, 13–22.

    Article  Google Scholar 

  • Zuo, R. (2011c). Regional exploration targeting model for Gangdese porphyry copper deposits. Resource Geology, 61, 296–303.

    Article  Google Scholar 

  • Zuo, R., Agterberg, F. P., Cheng, Q., & Yao, L. (2009a). Fractal characterization of the spatial distribution of geological point processes. International Journal of Applied Earth Observation and Geoinformation, 11, 394–402.

    Article  Google Scholar 

  • Zuo, R., & Cheng, Q. (2008). Mapping singularities: A technique to identify potential Cu mineral deposits using sediment geochemical data, an example for Tibet, west China. Mineralogical Magazine, 72, 531–534.

    Article  Google Scholar 

  • Zuo, R., Cheng, Q., Agterberg, F. P., & Xia, Q. (2009b). Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration, 101, 225–235.

    Article  Google Scholar 

  • Zuo, R., & Xia, Q. (2009). Application fractal and multifractal methods to mapping prospectivity for metamorphosed sedimentary iron deposits using stream sediment geochemical data in eastern Hebei province, China. Geochimica et Cosmochimica Acta, 73, A1540–A1540.

    Google Scholar 

Download references

Acknowledgments

The authors express special thanks to Mr. Sahebzamani and Mr. Babaie, head of exploration department of the National Iranian Copper Industries Company (NICICO), for some supports.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Malayer University, 65719-95863, Malayer, Iran

    Mahyar Yousefi

  2. School of Earth and Oceans, James Cook University, Townsville, QLD, Australia

    Emmanuel John M. Carranza

Authors
  1. Mahyar Yousefi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel John M. Carranza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahyar Yousefi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousefi, M., Carranza, E.J.M. Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration. Nat Resour Res 25, 3–18 (2016). https://doi.org/10.1007/s11053-014-9261-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-014-9261-9

Keywords

Search

Navigation