An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits

  • Bijan Roshanravan
  • Hamid Aghajani
  • Mahyar YousefiEmail author
  • Oliver Kreuzer
Original Paper


In this paper an improved prediction-area plot has been developed. This type of plot includes performance measures similar to other existing methods (receiver operating characteristics, success-rate curves and ordinary prediction-area plots) and, therefore, offers a reliable method for evaluating the performance of spatial evidence maps and prospectivity models. To demonstrate the reliability of the improved prediction-area plot proposed, we investigated the benefits of augmented targeting criteria through remotely sensed exploration features, compared to only geological map-derived criteria, for mineral prospectivity analysis using as an example the podiform chromite deposits of the Sabzevar Ophiolite Belt, Iran. The application of the newly developed improved prediction-area plot to the prospectivity models generated in this study indicated that the augmented targeting criteria by using remote sensing data perform better than non-updated geological map-derived criteria, and that model effectiveness can be improved by using an integrated approach that entails geologic remote sensing.


Improved prediction-area plot Geologic remote sensing Mineral prospectivity modeling Podiform chromite deposits Sabzevar Ophiolite Belt Iran 



The authors sincerely thank the Geological Survey of Iran for providing the data used in this study. In addition, we greatly appreciate the many constructive comments by Prof. John Carranza, Dr. Mark Mihalasky, Dr. Jeff Harris, and two anonymous reviewers that helped to significantly improve our paper.


  1. Abrams, M. J., Rothery, D. A., & Pontual, A. (1988). Mapping in the Oman ophiolite using enhanced Landsat Thematic Mapper images. Tectonophysics, 151, 387–401.CrossRefGoogle Scholar
  2. Agterberg, F. P., & Bonham-Carter, G. F. (2005). Measuring the performance of mineral-potential maps. Natural Resources Research, 14, 1–17.CrossRefGoogle Scholar
  3. Almasi, A., Yousefi, M., & Carranza, E. J. M. (2017). Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran. Ore Geology Reviews, 91, 1066–1080.CrossRefGoogle Scholar
  4. Almeida, T. I. R., & Souza Filho, C. R. D. (2004). Principal component analysis applied to feature-oriented band ratios of hyperspectral data: A tool for vegetation studies. International Journal of Remote Sensing, 25, 5005–5023.CrossRefGoogle Scholar
  5. Almeida, T. I. R., Souza Filho, C. R. D., & Rossetto, R. (2006). ASTER and Landsat ETM+ images applied to sugarcane yield forecast. International Journal of Remote Sensing, 27, 4057–4069.CrossRefGoogle Scholar
  6. Asadi, H. H., Sansoleimani, A., Fatehi, M., & Carranza, E. J. M. (2016). An AHP–TOPSIS predictive model for district-scale mapping of porphyry Cu–Au potential: A case study from Salafchegan Area (Central Iran). Natural Resources Research, 25, 417–429.CrossRefGoogle Scholar
  7. Asadzadeh, S., & Souza Filho, C. R. D. (2016). A review on spectral processing methods for geological remote sensing. International Journal of Applied Earth Observation and Geoinformation, 47, 69–90.CrossRefGoogle Scholar
  8. Bishop, C., Rivard, B., de Souza Filho, C., & van der Meer, F. (2018). Geological remote sensing. International Journal of Applied Earth Observation and Geoinformation, 64, 267–274.CrossRefGoogle Scholar
  9. Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS (Vol. 13). Amsterdam: Elsevier.Google Scholar
  10. Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. Statistical Applications in the Earth Sciences, 89, 171–183.Google Scholar
  11. Campos, L. D., de Souza, S. M., de Sordi, D. A., Tavares, F. M., Klein, E. L., & dos Santos Lopes, E. C. (2017). Predictive mapping of prospectivity in the Gurupi orogenic gold belt, north-northeast Brazil: An example of district-scale mineral system approach to exploration targeting. Natural Resources Research, 26, 509–534.CrossRefGoogle Scholar
  12. Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS (Vol. 11). Amsterdam: Elsevier.Google Scholar
  13. Carranza, E. J. M. (2014). Data-driven evidential belief modeling of mineral potential using few prospects and evidence with missing values. Natural Resources Research, 24, 291–304.CrossRefGoogle Scholar
  14. Carranza, E. J. M., & Hale, M. (2001). Geologically-constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research, 10, 125–136.CrossRefGoogle Scholar
  15. Carranza, E. J. M., & Laborte, A. G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: A case study in Catanduanes Island (Philippines). Natural Resources Research, 25, 35–50.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Chauhan, S., Sharma, M., Arora, M. K., & Gupta, N. K. (2010). Landslide susceptibility zonation through ratings derived from artificial neural network. International Journal of Applied Earth Observation and Geoinformation, 12, 340–350.CrossRefGoogle Scholar
  19. Chen, Y. (2015). Mineral potential mapping with a restricted Boltzmann machine. Ore Geology Reviews, 71, 749–760.CrossRefGoogle Scholar
  20. Chen, Y., & An, A. (2016). Application of ant colony algorithm to geochemical anomaly detection. Journal of Geochemical Exploration, 164, 75–85.CrossRefGoogle Scholar
  21. Chen, Y., & Wu, W. (2016). A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis. Ore Geology Reviews, 74, 26–38.CrossRefGoogle Scholar
  22. Chen, Y., & Wu, W. (2017). Mapping mineral prospectivity using an extreme learning machine regression. Ore Geology Reviews, 80, 200–213.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Chung, C. J. F., & Agterberg, F. P. (1980). Regression models for estimating mineral resources from geological map data. Journal of the International Association for Mathematical Geology, 12, 473–488.CrossRefGoogle Scholar
  25. Chung, C. J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30, 451–472.CrossRefGoogle Scholar
  26. Chung, C. J. F., & Moon, W. M. (1991). Combination rules of spatial geoscience data for mineral exploration. Geoinformatics, 2, 159–169.CrossRefGoogle Scholar
  27. Crosta, A. P., & Souza Filho, C. R. D. (2003). Searching for gold with ASTER. Earth Observation Magazine, 12, 38–41.Google Scholar
  28. Crosta, A. P., & Souza Filho, C. R. D. (2017). Hyperspectral remote sensing for mineral mapping: A case-study at alto Paraíso de Goías, central Brazil. Revista Brasileira de Geociências, 30, 551–554.CrossRefGoogle Scholar
  29. Crosta, A. P., Souza Filho, C. R. D., 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, 4233–4240.CrossRefGoogle Scholar
  30. Du, X., Zhou, K., Cui, Y., Wang, J., Zhang, N., & Sun, W. (2016). Application of fuzzy Analytical Hierarchy Process (AHP) and Prediction-Area (PA) plot for mineral prospectivity mapping: A case study from the Dananhu metallogenic belt, Xinjiang, NW China. Arabian Journal of Geosciences, 9, 298.CrossRefGoogle Scholar
  31. Eldosouky, A. M., Abdelkareem, M., & Elkhateeb, S. O. (2017). Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in Wadi Allaqi area, South Eastern Desert of Egypt. Journal of African Earth Sciences, 130, 28–37.CrossRefGoogle Scholar
  32. Fabbri, A. G., & Chung, C. J. F. (2008). On blind tests and spatial prediction models. Natural Resources Research, 17, 107–118.CrossRefGoogle Scholar
  33. Feizi, F., Karbalaei-Ramezanali, A., & Tusi, H. (2017). Mineral potential mapping via TOPSIS with hybrid AHP–Shannon entropy weighting of evidence: A case study for Porphyry-Cu, Farmahin Area, Markazi Province. Iran. Natural Resources Research, 26, 553–570.CrossRefGoogle Scholar
  34. Gabr, S., Ghulam, A., & Kusky, T. (2010). Detecting areas of high-potential gold mineralization using ASTER data. Ore Geology Reviews, 38, 59–69.CrossRefGoogle Scholar
  35. Gad, S., & Kusky, T. M. (2006). Lithological mapping in the Eastern Desert of Egypt, the Barramiya area, using Landsat thematic mapper (TM). Journal of African Earth Sciences, 44, 196–202.CrossRefGoogle Scholar
  36. Gad, S., & Kusky, T. M. (2007). ASTER spectral ratioing for lithological mapping in the Arabian-Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Research, 11, 326–335.CrossRefGoogle Scholar
  37. Gao, Y., Zhang, Z., Xiong, Y., & Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews, 75, 16–28.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. Gomez, C., Delacourt, C., Allemand, P., Ledru, P., & Wackerle, R. (2005). Using ASTER remote sensing data set for geological mapping, in Namibia. Physics and Chemistry of the Earth, Parts A/B/C, 30, 97–108.CrossRefGoogle Scholar
  40. Harris, J. R., Bowie, C., Rencz, A. N., & Graham, D. (1994). Computer-enhancement techniques for the integration of remotely sensed, geophysical, and thematic data for the geosciences. Canadian Journal of Remote Sensing, 20, 210–221.Google Scholar
  41. Harris, J. R., & Grunsky, E. C. (2015). Predictive lithological mapping of Canada’s North using Random Forest classification applied to geophysical and geochemical data. Computers & Geosciences, 80, 9–25.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. Harris, J. R., Lemkow, D., Jefferson, C., Wright, D., & Falck, H. (2008). Mineral potential modelling for the greater Nahanni ecosystem using GIS based analytical methods. Natural Resources Research, 17, 51–78.CrossRefGoogle Scholar
  44. Harris, J. R., Wilkinson, L., Heather, K., Fumerton, S., Bernier, M. A., Ayer, J., et al. (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.CrossRefGoogle Scholar
  45. Hashim, M., Pournamdary, M., & Pour, A. B. (2011). Processing and interpretation of advanced space-borne thermal emission and reflection radiometer (ASTER) data for lithological mapping in ophiolite complex. International Journal of Physical Sciences, 6, 6410–6421.Google Scholar
  46. Hengl, T. (2006). Finding the right pixel size. Computers & Geosciences, 32, 1283–1298.CrossRefGoogle Scholar
  47. Inzana, J., Kusky, T., Higgs, G., & Tucker, R. (2003). Supervised classifications of Landsat TM band ratio images and Landsat TM band ratio image with radar for geological interpretations of central Madagascar. Journal of African Earth Sciences, 37, 59–72.CrossRefGoogle Scholar
  48. Jannessary, M. R., Melcher, F., Lodziak, J., & Meisel, T. C. (2012). Review of platinum-group element distribution and mineralogy in chromitite ores from southern Iran. Ore Geology Reviews, 48, 278–305.CrossRefGoogle Scholar
  49. Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (4th ed.). Upper Saddle River, NJ: Prentice Hall Press.Google Scholar
  50. Khan, S. D., & Mahmood, K. (2008). The application of remote sensing techniques to the study of ophiolites. Earth-Science Reviews, 89, 135–143.CrossRefGoogle Scholar
  51. Kreuzer, O. P., Miller, A. V., Peters, K. J., Payne, C., Wildman, C., Partington, G. A., et al. (2015). Comparing prospectivity modelling results and past exploration data: A case study of porphyry Cu–Au mineral systems in the Macquarie Arc, Lachlan Fold Belt, New South Wales. Ore Geology Reviews, 71, 516–544.CrossRefGoogle Scholar
  52. McKay, G., & Harris, J. R. (2016). Comparison of the data-driven Random Forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Natural Resources Research, 25, 125–143.CrossRefGoogle Scholar
  53. Mihalasky, M. J., & Bonham-Carter, G. F. (2001). Lithodiversity and its spatial association with metallic mineral sites, Great Basin of Nevada. Natural Resources Research, 10, 209–226.CrossRefGoogle Scholar
  54. Mohebi, A., Mirnejad, H., Lentz, D., Behzadi, M., Dolati, A., Kani, A., et al. (2015). Controls on porphyry Cu mineralization around Hanza Mountain, south-east of Iran: An analysis of structural evolution from remote sensing, geophysical, geochemical and geological data. Ore Geology Reviews, 69, 187–198.CrossRefGoogle Scholar
  55. Mosier, D. L., Singer, D. A., Moring, B. C., & Galloway, J. P. (2012). Podiform chromite deposits—Database and grade and tonnage models (No. 2012-5157, pp. i-45). United States Geological Survey.Google Scholar
  56. Mutele, L., Billay, A., & Hunt, J. P. (2017). Knowledge-driven prospectivity mapping for granite-related polymetallic Sn–F–(REE) mineralization, Bushveld Igneous Complex, South Africa. Natural Resources Research, 26, 535–552.CrossRefGoogle Scholar
  57. Nezhad, S. G., Mokhtari, A. R., & Rodsari, P. R. (2017). The true sample catchment basin approach in the analysis of stream sediment geochemical data. Ore Geology Reviews, 83, 127–134.CrossRefGoogle Scholar
  58. Ninomiya, Y. (2003). A stabilized vegetation index and several mineralogic indices defined for ASTER VNIR and SWIR data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 3, 1552–1554.Google Scholar
  59. Ninomiya, Y. (2004). Lithologic mapping with multispectral ASTER TIR and SWIR data. Sensors, Systems, and Next-Generation Satellites VII, 5234, 180–191.CrossRefGoogle Scholar
  60. Ninomiya, Y., & Fu, B. (2002). Quartz index, carbonate index and SiO2 content index defined for ASTER TIR data. Journal of the Remote Sensing Society of Japan, 22, 50–61.Google Scholar
  61. Ninomiya, Y., Fu, B., & Cudahy, T. J. (2005). Detecting lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral thermal infrared “radiance-at-sensor” data. Remote Sensing of Environment, 99, 127–139.CrossRefGoogle Scholar
  62. Nykänen, V. (2008). Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. Natural Resources Research, 17, 29–48.CrossRefGoogle Scholar
  63. Nykänen, V., Lahti, I., Niiranen, T., & Korhonen, K. (2015). Receiver operating characteristics (ROC) as validation tool for prospectivity models—A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland. Ore Geology Reviews, 71, 853–860.CrossRefGoogle Scholar
  64. Parsa, M., Maghsoudi, A., & Yousefi, M. (2017). A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits. Natural Resources Research, 27, 15–28.CrossRefGoogle Scholar
  65. Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2016a). Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. Journal of African Earth Sciences, 114, 228–241.CrossRefGoogle Scholar
  66. Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2016b). Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran. Journal of Geochemical Exploration, 165, 111–124.CrossRefGoogle Scholar
  67. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 6, 559–572.CrossRefGoogle Scholar
  68. Porwal, A. K., Das, R. D., Chaudhary, B., Gonzalez-Alvarez, I., & Kreuzer, O. P. (2015). Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area, Western Australia. Ore Geology Reviews, 71, 839–852.CrossRefGoogle Scholar
  69. Porwal, A. K., & Kreuzer, O. P. (2010). Introduction to the special issue: Mineral prospectivity analysis and quantitative resource estimation. Ore Geology Reviews, 38, 121–127.CrossRefGoogle Scholar
  70. Rajabzadeh, M. A., Dehkordi, T. N., & Caran, Ş. (2013). Mineralogy, geochemistry and geotectonic significance of mantle peridotites with high-Cr chromitites in the Neyriz ophiolite from the outer Zagros ophiolite belts, Iran. Journal of African Earth Sciences, 78, 1–15.CrossRefGoogle Scholar
  71. Rajabzadeh, M. A., Ghasemkhani, E., & Khosravi, A. (2015). Biogeochemical study of chromite bearing zones in Forumad area, Sabzevar ophiolite, Northeastern Iran. Journal of Geochemical Exploration, 151, 41–49.CrossRefGoogle Scholar
  72. Rajendran, S., Al-Khirbash, S., Pracejus, B., Nasir, S., Al-Abri, A. H., Kusky, T. M., et al. (2012). ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: Exploration strategy. Ore Geology Reviews, 44, 121–135.CrossRefGoogle Scholar
  73. Ranjbar, H., & Honarmand, M. (2004). Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification. International Journal of Remote Sensing, 25, 4729–4741.CrossRefGoogle Scholar
  74. Richter, R. (1998). Correction of satellite imagery over mountainous terrain. Applied Optics, 37, 4004–4015.CrossRefGoogle Scholar
  75. Roshanravan, B., Aghajani, H., Yousefi, M., & Kreuzer, O. (2018). Particle swarm optimization algorithm for neuro-fuzzy prospectivity analysis using continuously weighted spatial exploration data. Natural Resources Research. Scholar
  76. Shafaii Moghadam, H., Rahgooshay, M., & Forouzesh, V. (2010). Geochemical investigation of the nodular chromites in the Forumad ophiolite, NE of Iran. Iranian Journal of Sciences and Technology, 43, 235–245.Google Scholar
  77. Shojaat, B., Hassanipak, A. A., Mobasher, K., & Ghazi, A. M. (2003). Petrology, geochemistry and tectonics of the Sabzevar ophiolite, North Central Iran. Journal of Asian Earth Sciences, 21, 1053–1067.CrossRefGoogle Scholar
  78. Stöcklin, J. (1974). Possible ancient continental margins in Iran. In The geology of continental margins (pp. 873–887). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  79. Tessema, A. (2017). Mineral Systems Analysis and Artificial Neural Network Modeling of Chromite Prospectivity in the Western Limb of the Bushveld Complex, South Africa. Natural Resources Research, 26, 465–488.CrossRefGoogle Scholar
  80. Van der Meer, F. D., & De Jong, S. M. (Eds.). (2011). Imaging spectrometry: Basic principles and prospective applications (Vol. 4). Berlin: Springer.Google Scholar
  81. Vicente, L. E., & Souza Filho, C. R. D. (2011). Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sensing of Environment, 115, 1824–1836.CrossRefGoogle Scholar
  82. Yaghubpur, A., & Hassannejad, A. A. (2006). The spatial distribution of some chromite deposits in Iran, using Fry analysis. Journal of Sciences, Islamic Republic of Iran, 17, 147–152.Google Scholar
  83. Yousefi, M. (2017). Recognition of an enhanced multi-element geochemical signature of porphyry copper deposits for vectoring into mineralized zones and delimiting exploration targets in Jiroft area, SE Iran. Ore Geology Reviews, 83, 200–214.CrossRefGoogle Scholar
  84. Yousefi, M., & Carranza, E. J. M. (2015a). Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping. Computers & Geosciences, 83, 72–79.CrossRefGoogle Scholar
  85. Yousefi, M., & Carranza, E. J. M. (2015b). 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.CrossRefGoogle Scholar
  86. Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25, 3–18.CrossRefGoogle Scholar
  87. 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.CrossRefGoogle Scholar
  88. 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.CrossRefGoogle Scholar
  89. 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.CrossRefGoogle Scholar
  90. Zhang, N., Zhou, K., & Du, X. (2017). Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China. Journal of African Earth Sciences, 128, 84–96.CrossRefGoogle Scholar
  91. Zou, K. H., O’Malley, A. J., & Mauri, L. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 115, 654–657.CrossRefGoogle Scholar
  92. Zuo, R. (2018). Selection of an elemental association related to mineralization using spatial analysis. Journal of Geochemical Exploration, 184, 150–157.CrossRefGoogle Scholar
  93. Zuo, R., Zhang, Z., Zhang, D., Carranza, E. J. M., & Wang, H. (2015). Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China. Ore Geology Reviews, 71, 502–515.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2018

Authors and Affiliations

  • Bijan Roshanravan
    • 1
  • Hamid Aghajani
    • 1
  • Mahyar Yousefi
    • 2
    Email author
  • Oliver Kreuzer
    • 3
    • 4
  1. 1.School of Mining, Petroleum and GeophysicsShahrood University of TechnologyShahroodIran
  2. 2.Faculty of EngineeringMalayer UniversityMalayerIran
  3. 3.Corporate Geoscience GroupRockingham BeachAustralia
  4. 4.Economic Geology Research Centre (EGRU), School of Earth and Environmental ScienceJames Cook UniversityTownsvilleAustralia

Personalised recommendations