Performance evaluation of RBF- and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of S-A multifractal model and mineralization controls

  • Reza Ghezelbash
  • Abbas MaghsoudiEmail author
  • Emmanuel John M. Carranza
Research Article


Definition of the efficient ore-forming processes, which are considered as mineralization controls is a fundamental stage in mineral prospectivity modeling. In this contribution, four efficient targeting criteria of geochemical, geological and structural data related to porphyry-type Cu deposits in Varzaghan district, NW Iran, were integrated. For creation of multi-element geochemical layer, a two-stage factor analysis was firstly conducted on ilr-transformed data of 18 selected elements and it was found that factor 1 (F1) is the representative of Cu-Au-Mo-Bi elemental association in the study area. Then, the combined model of multifractal inverse distance weighting (IDW) interpolation technique and spectrum-area (S-A) fractal method of F1 as the significant mineralization-related multi-element geochemical layer was integrated with geological-structural evidence layers. For this purpose, two supervised machine learning algorithms, namely radial basis function (RBF) neural network and support vector machine (SVM) with RBF kernel were used for generating data-driven predictive models of porphyry-Cu mineral prospectivity. Comparison of the generated models demonstrates that the former is more successful in delineating exploration targets than the latter one.


Porphyry-Cu deposits Prospectivity modeling Multifractal inverse distance weighting (MIDW) interpolation Spectrum-area (S-A) fractal RBF neural network SVM 



The authors would like to thank Prof. H. A. Babaie, Editor-in-Chief, and two anonymous reviewers for their constructive comments and edits.


  1. Abbaszadeh M, Hezarkhani A, Soltani-Mohammadi S (2013) An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Chem Erde 73:545–554CrossRefGoogle Scholar
  2. Abedi M, Norouzi G (2012) Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. J Appl Geophys 83:35–45CrossRefGoogle Scholar
  3. Abedi M, Norouzi GH, Bahroudi A (2012) Support vector machine for multi-classification of mineral prospectivity areas. Comput Geosci 46:272–283CrossRefGoogle Scholar
  4. Afzal P, Khakzad A, Moarefvand P, Omran NR, Esfandiari B, Alghalandis YF (2010) Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. J Geochem Explor 104:34–46CrossRefGoogle Scholar
  5. Afzal P, Alghalandis YF, Khakzad A, Moarefvand P, Omran NR (2011) Delineation of mineralization zones in porphyry cu deposits by fractal concentration–volume modeling. J Geochem Explor 108:220–232CrossRefGoogle Scholar
  6. Afzal P, Harati H, Alghalandis YF, Yasrebi AB (2013) Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type cu deposit, Iran. Chem Erde 73:533–543CrossRefGoogle Scholar
  7. Aghazadeh M, Hou Z, Badrzadeh Z, Zhou L (2015) Temporal–spatial distribution and tectonic setting of porphyry copper deposits in Iran: constraints from zircon U–Pb and molybdenite re–Os geochronology. Ore Geol Rev 70:385–406CrossRefGoogle Scholar
  8. Agterberg FP, Bonham-Carter GF (2005) Measuring the performance of mineral-potential maps. Nat Resour Res 14:1–17CrossRefGoogle Scholar
  9. Aitchison J (1986) Coda: a microcomputer package for the statistical analysis compositional data. Chapman and HallGoogle Scholar
  10. Alavi M (2004) Regional stratigraphy of the Zagros fold-thrust belt of Iran and its proforeland evolution. Am J Sci 304:1–20CrossRefGoogle Scholar
  11. Behnia P (2007) Application of radial basis functional link networks to exploration for Proterozoic mineral deposits in Central Iran. Nat Resour Res 16:147–155CrossRefGoogle Scholar
  12. Berberian M, King GCP (1981) Towards a paleogeography and tectonic evolution of Iran. Can J Earth Sci 18:210–265CrossRefGoogle Scholar
  13. Bishop C, Bishop CM (1995) Neural networks for pattern recognition. Oxford university pressGoogle Scholar
  14. Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists 13:398Google Scholar
  15. Bonham-Carter GF, Agterberg FP, Wright DF (1990) Weights of evidence modelling: a new approach to mapping mineral potential. Statistical applications in the earth sciences 89:171–183Google Scholar
  16. Brown WM, Gedeon TD, Groves DI, Barnes RG (2000) Artificial neural networks: a new method for mineral prospectivity mapping. Aust J Eart Sci 47:757–770CrossRefGoogle Scholar
  17. Carranza EJM (2004) Weights of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines. Nat Resour Res 13:173–187CrossRefGoogle Scholar
  18. Carranza EJM (2008) Geochemical anomaly and mineral prospectivity mapping in GIS (Vol. 11). ElsevierGoogle Scholar
  19. Carranza EJM (2009) Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Comput Geosci 35:2032–2046CrossRefGoogle Scholar
  20. Carranza EJM (2011) Geocomputation of mineral exploration targets. Comput Geosci 37:1907–1916CrossRefGoogle Scholar
  21. Carranza EJM (2017) Natural resources research publications on geochemical anomaly and mineral potential mapping, and introduction to the special issue of papers in these fields. Nat Resour Res 26:379–410CrossRefGoogle Scholar
  22. Carranza EJM, Hale M (2002) Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Nat Resour Res 11:45–59CrossRefGoogle Scholar
  23. Carranza EJM, Laborte AG (2015a) Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: application of random forests algorithm. Ore Geol Rev 71:777–787CrossRefGoogle Scholar
  24. Carranza EJM, Laborte AG (2015b) Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Comput Geosci 74:60–70CrossRefGoogle Scholar
  25. Carranza EJM, Laborte AG (2016) Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines). Nat Resour Res 25:35–50CrossRefGoogle Scholar
  26. Carranza EJM, Hale M, Faassen C (2008) Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geol Rev 33:536–558CrossRefGoogle Scholar
  27. Chen C, He B, Zeng Z (2014) A method for mineral prospectivity mapping integrating C4. 5 decision tree, weights-of-evidence and m-branch smoothing techniques: a case study in the eastern Kunlun Mountains, China. Earth Sci Inf 7:13–24CrossRefGoogle Scholar
  28. Cheng Q (1999) Spatial and scaling modelling for geochemical anomaly separation. J Geochem Explor 65:175–194CrossRefGoogle Scholar
  29. Cheng Q (2000) Interpolation by means of multiftractal, kriging and moving average techniques. In GAC/MAC meeting of GeoCanada2000, CalgaryGoogle Scholar
  30. Cheng Q (2007) Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geol Rev 32:314–324CrossRefGoogle Scholar
  31. Cheng Q, Agterberg FP, Ballantyne SB (1994) The separation of geochemical anomalies from background by fractal methods. J Geochem Explor 51:109–130CrossRefGoogle Scholar
  32. Cheng Q, Xu Y, Grunsky E (1999) Integrated spatial and spectral analysis for geochemical anomaly separation. In: Lippard, S.J., Naess, A., Sinding-Larsen, R. (Eds.), Proceedings of the fifth annual conference of the International Association for Mathematica Geology, Trondheim, Norway 6 e11th august. 11, pp. 87e92Google Scholar
  33. Cheng Q, Xu Y, Grunsky E (2000) Integrated spatial and spectrum method for geochemical anomaly separation. Nat Resour Res 9:43–52CrossRefGoogle Scholar
  34. 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 7:3019–3025CrossRefGoogle Scholar
  35. Cooke DR, Hollings P, Walshe JL (2005) Giant porphyry deposits: characteristics, distribution, and tectonic controls. Econ Geol 100:801–818CrossRefGoogle Scholar
  36. Cox DP, Singer DA (1986) Mineral deposit models (Vol. 1693). Bulletin. US Government Printing OfficeGoogle Scholar
  37. Cox SF, Etheridge MA, Wall VJ (1987) The role of fluids in syntectonic mass transport, and the localization of metamorphic vein-type ore deposits. Ore Geol Rev 2:65–86CrossRefGoogle Scholar
  38. De Palomera PA, van Ruitenbeek FJ, Carranza EJM (2015) Prospectivity for epithermal gold–silver deposits in the Deseado Massif, Argentina. Ore Geol Rev 71:484–501CrossRefGoogle Scholar
  39. Dilek Y, Imamverdiyev N, Altunkaynak Ş (2010) Geochemistry and tectonics of Cenozoic volcanism in the lesser Caucasus (Azerbaijan) and the peri-Arabian region: collision-induced mantle dynamics and its magmatic fingerprint. Int Geol Rev 52:536–578CrossRefGoogle Scholar
  40. Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C (2003) Isometric logratio transformations for compositional data analysis. Math Geol 35:279–300CrossRefGoogle Scholar
  41. Geranian H, Tabatabaei SH, Asadi HH, Carranza EJM (2016) Application of discriminant analysis and support vector machine in mapping gold potential areas for further drilling in the sari-Gunay gold deposit, NW Iran. Nat Resour Res 25:145–159CrossRefGoogle Scholar
  42. Ghezelbash R, Maghsoudi A (2018a) A hybrid AHP-VIKOR approach for prospectivity modeling of porphyry cu deposits in the Varzaghan District, NW Iran. Arab J Geosci 11:275CrossRefGoogle Scholar
  43. Ghezelbash R, Maghsoudi A (2018b) Comparison of U-spatial statistics and C–A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district, NW Iran. C R Geosci 350:180–191CrossRefGoogle Scholar
  44. Ghezelbash R, Maghsoudi A, Daviran M (2018a) Prospectivity modeling of porphyry copper deposits: recognition of efficient mono-and multi-element geochemical signatures in the Varzaghan district, NW Iran. Acta Geochim 1–14Google Scholar
  45. Ghezelbash R, Maghsoudi A, Carranza EJM (2018b) An improved data-driven multiple criteria decision-making procedure for spatial modeling of mineral prospectivity: adaptation of prediction-area plot and logistic functions. Nat Resour Res.
  46. 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 26:489–507CrossRefGoogle Scholar
  47. Harris D, Zurcher L, Stanley M, Marlow J, Pan G (2003) A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Nat Resour Res 12:241–255CrossRefGoogle Scholar
  48. Hezarkhani A (2006) Petrology of the intrusive rocks within the Sungun porphyry copper deposit, Azerbaijan, Iran. J Asian Earth Sci 27:326–340CrossRefGoogle Scholar
  49. Hezarkhani A, Williams-Jones AE (1998) Controls of alteration and mineralization in the Sungun porphyry copper deposit, Iran; evidence from fluid inclusions and stable isotopes. Econ Geol 93:651–670CrossRefGoogle Scholar
  50. Hu S, Cheng Q, Wang L, Xu D (2013) Modeling land price distribution using multifractal IDW interpolation and fractal filtering method. Landsc Urban Plan 110:25–35CrossRefGoogle Scholar
  51. Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749CrossRefGoogle Scholar
  52. Jamali H, Dilek Y, Daliran F, Yaghubpur A, Mehrabi B (2010) Metallogeny and tectonic evolution of the Cenozoic Ahar–Arasbaran volcanic belt, northern Iran. Int Geol Rev 52:608–630CrossRefGoogle Scholar
  53. Kavzoğlu T, Çölkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs 11:352–359CrossRefGoogle Scholar
  54. Kreuzer OP, Blenkinsop TG, Morrison RJ, Peters SG (2007) Ore controls in the charters towers goldfield, NE Australia: constraints from geological, geophysical and numerical analyses. Ore Geol Rev 32:37–80CrossRefGoogle Scholar
  55. 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. Appl Geochem 18:1853–1865CrossRefGoogle Scholar
  56. Lindsay M, Aitken A, Ford A, Dentith M, Hollis J, Tyler I (2016) Reducing subjectivity in multi-commodity mineral prospectivity analyses: modelling the West Kimberley, Australia. Ore Geol Rev 76:395–413CrossRefGoogle Scholar
  57. Lisitsin V (2015) Spatial data analysis of mineral deposit point patterns: applications to exploration targeting. Ore Geol Rev 71:861–881CrossRefGoogle Scholar
  58. Maghsoudi A, Yazdi M, Mehrpartou M, Vosoughi M, Younesi S (2014) Porphyry Cu–Au mineralization in the Mirkuh Ali Mirza magmatic complex, NW Iran. J Asian Earth Sci 79:932–941CrossRefGoogle Scholar
  59. McKay G, Harris JR (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. Nat Resour Res 25:125–143CrossRefGoogle Scholar
  60. Mehrpartou M (1993) Geological map of Varzaghan, scale 1: 1,000,000. Geological survey of IranGoogle Scholar
  61. Meshkani SA, 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 Geol Rev 55:48–63CrossRefGoogle Scholar
  62. Moon WM (1990) Integration of geophysical and geological data using evidential belief function. IEEE Trans Geosci Remote Sens 28:711–720CrossRefGoogle Scholar
  63. Niros AD, Tsekouras GE (2012) A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach. Fuzzy Sets Syst 193:62–84CrossRefGoogle Scholar
  64. 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. Nat Resour Res 17:29–48CrossRefGoogle Scholar
  65. Parsa M, Maghsoudi A, Yousefi M, Sadeghi M (2016a) Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran. J Geochem Explor 165:111–124CrossRefGoogle Scholar
  66. Parsa M, Maghsoudi A, Ghezelbash R (2016b) Decomposition of anomaly patterns of multi-element geochemical signatures in Ahar area, NW Iran: a comparison of U-spatial statistics and fractal models. Arab J Geosci 9:260CrossRefGoogle Scholar
  67. Parsa M, Maghsoudi A, Yousefi M, Sadeghi M (2016c) Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. J Afr Earth Sci 114:228–241CrossRefGoogle Scholar
  68. Parsa M, Maghsoudi A, Yousefi M, Carranza EJM (2017a) Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: implications for mapping exploration targets. J Afr Earth Sci 128:5–15CrossRefGoogle Scholar
  69. Parsa M, Maghsoudi A, Yousefi M (2017b) An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets. Int J Appl Earth Obs 58:157–167CrossRefGoogle Scholar
  70. Parsa M, Maghsoudi A, Yousefi M (2018) Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran. Ore Geol Rev 92:97–112CrossRefGoogle Scholar
  71. Pirajno F (2010) Intracontinental strike-slip faults, associated magmatism, mineral systems and mantle dynamics: examples from NW China and Altay-Sayan (Siberia). J Geodyn 50:325–346CrossRefGoogle Scholar
  72. Porwal A, Carranza EJM, Hale M (2003) Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Nat Resour Res 12:1–25CrossRefGoogle Scholar
  73. Reddy RKT, Bonham-Carter GF (1991) A decision-tree approach to mineral potential mapping in snow Lake area, Manitoba. Can J Remote Sens 17:191–200CrossRefGoogle Scholar
  74. Reimann C, Filzmoser P, Garrett RG (2002) Factor analysis applied to regional geochemical data: problems and possibilities. Appl Geochem 17:185–206CrossRefGoogle Scholar
  75. Rodriguez-Galiano VF, Chica-Olmo M, Chica-Rivas M (2014) Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain. Int J Geogr Inf Sci 28:1336–1354CrossRefGoogle Scholar
  76. Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M (2015) Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 71:804–818CrossRefGoogle Scholar
  77. Sibson RH (1996) Structural permeability of fluid-driven fault-fracture meshes. J Struct Geol 18:1031–1042CrossRefGoogle Scholar
  78. Sillitoe RH (2010) Porphyry copper systems. Econ Geol 105:3–41CrossRefGoogle Scholar
  79. Skabar AA (2005) Mapping mineralization probabilities using multilayer perceptrons. Nat Resour Res 14:109–123CrossRefGoogle Scholar
  80. Spadoni M, Voltaggio M, Cavarretta G (2005) Recognition of areas of anomalous concentration of potentially hazardous elements by means of a subcatchment-based discriminant analysis of stream sediments. J Geochem Explor 87:83–91CrossRefGoogle Scholar
  81. Tessema A (2017) Mineral systems analysis and artificial neural network modeling of chromite prospectivity in the Western limb of the bushveld complex, South Africa. Nat Resour Res 26:465–488CrossRefGoogle Scholar
  82. Vapnik V (1995) Nature of statistical learning theory. John Wiley and Sons, Inc., New YorkCrossRefGoogle Scholar
  83. Wang J, Zuo R (2015) A MATLAB-based program for processing geochemical data using fractal/multifractal modeling. Earth Sci Inf 8:937–947CrossRefGoogle Scholar
  84. Xiao F, Chen J, Zhang Z, Wang C, Wu G, Agterberg FP (2012) Singularity mapping and spatially weighted principal component analysis to identify geochemical anomalies associated with ag and Pb-Zn polymetallic mineralization in Northwest Zhejiang, China. J Geochem Explor 122:90–100CrossRefGoogle Scholar
  85. Xie S, Cheng Q, Xing X, Bao Z, Chen Z (2010) Geochemical multifractal distribution patterns in sediments from ordered streams. Geoderma 160:36–46CrossRefGoogle Scholar
  86. Yousefi M, Carranza EJM (2015) Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Comput Geosci 79:69–81CrossRefGoogle Scholar
  87. Yousefi M, Kamkar-Rouhani A, Carranza EJM (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. J Geochem Explor 115:24–35CrossRefGoogle Scholar
  88. Yousefi M, Kamkar-Rouhani A, Carranza EJM (2013) Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochem Explor Env Analysis:2012–2144Google Scholar
  89. Yuan F, Li X, Zhou T, Deng Y, Zhang D, Xu C, Jowitt SM (2015) Multifractal modelling-based mapping and identification of geochemical anomalies associated with Cu and Au mineralisation in the NW Junggar area of northern Xinjiang Province, China. J Geochem Explor 154:252–264CrossRefGoogle Scholar
  90. Zarasvandi A, Rezaei M, Sadeghi M, Lentz D, Adelpour M, Pourkaseb H (2015) Rare earth element signatures of economic and sub-economic porphyry copper systems in Urumieh–Dokhtar magmatic arc (UDMA), Iran. Ore Geol Rev 70:407–423CrossRefGoogle Scholar
  91. Zuo R (2011) 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). J Geochem Explor 111:13–22CrossRefGoogle Scholar
  92. Zuo R, Carranza EJM (2011) Support vector machine: a tool for mapping mineral prospectivity. Comput Geosci 37:1967–1975CrossRefGoogle Scholar
  93. Zuo R, Wang J (2016) Fractal/multifractal modeling of geochemical data: a review. J Geochem Explor 164:33–41CrossRefGoogle Scholar
  94. Zuo R, Cheng Q, Agterberg FP, Xia Q (2009) Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. J Geochem Explor 101:225–235CrossRefGoogle Scholar
  95. Zuo R, Xia Q, Wang H (2013) Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Appl Geochem 28:202–211CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Mining and Metallurgical EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Geological Sciences, School of Agricultural, Earth and Environmental SciencesUniversity of KwaZulu-NatalDurbanSouth Africa

Personalised recommendations