Skip to main content

Advertisement

SpringerLink
Log in

Regional-Scale Mineral Prospectivity Mapping: Support Vector Machines and an Improved Data-Driven Multi-criteria Decision-Making Technique

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

Abstract

Mapping mineral prospectivity (MPM) is mostly beset with prediction uncertainties, which are generally categorized into (a) stochastic and (b) systemic types. The stochastic type is usually linked to the low quality as well as insufficiency/inefficiency of data used. In contrast, inaccurate selection of exploration criteria, exaggerated and arbitrary weighting of spatial evidence layers resulting from subjective judgment of analyst and applying an integration methodology, which is not able to consider the complexities of geological processes, are main sources of systemic type. This paper aims for reducing the second type of MPM uncertainty in delineating favorable exploration targets for Cu-Au mineralization in the Moalleman District, NE Iran. Thus, several efficient evidence layers were translated from geospatial criteria (e.g., geochemical, geological, structural and hydrothermal alterations) and were considered for integration purpose in the study area. Then, an improved data-driven simple additive weight (data-driven SAW) procedure was introduced for generating prospectivity model. In this procedure, prediction-area plots and frequency ratio method were applied for assigning objective weights to efficient evidence layers and their corresponding classes, respectively. Furthermore, a supervised algorithm for machine learning classification namely support vector machine (SVM) with radial basis function kernel was executed for comparison purposes. The results indicated that the two prospectivity models are succeeded in delineating favorable targets of mineralization; however, the SVM model is more reliable than data-driven SAW in predicting high-potential areas of mineralization.

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.

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

Similar content being viewed by others

References

  • Abedi, M., & Norouzi, G. H. (2016). A general framework of TOPSIS method for integration of airborne geophysics, satellite imagery, geochemical and geological data. International Journal of Applied Earth Observation and Geoinformation, 46, 31–44.

    Google Scholar 

  • Afshari, A., Mojahed, M., & Yusuff, R. M. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1(5), 511.

    Google Scholar 

  • Aitchison, J. (1986). The statistical analysis of compositional data. New York: Chapman Hall.

    Google Scholar 

  • Ali, L., Moon, C. J., Williamson, B. J., Shah, M. T., & Khattak, S. A. (2015). A GIS-based stream sediment geochemical model for gold and base metal exploration in remote areas of northern Pakistan. Arabian Journal of Geosciences, 8(7), 5081–5093.

    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, 1–11.

    Google Scholar 

  • 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(4), 417–429.

    Google Scholar 

  • Bahrampour, M., Lotfi, M., Akbarpour, A., & Bahrampour, E. (2017). Petrogenesis, geochemistry, fluid inclusions and the role of the subvolcanic intrusives in genesis of copper at Chahmora deposit, north of Torud, Semnan. Geosciences, 102, 117–136.

    Google Scholar 

  • Beucher, A., Fröjdö, S., Österholm, P., Martinkauppi, A., & Edén, P. (2014). Fuzzy logic for acid sulfate soil mapping: Application to the southern part of the Finnish coastal areas. Geoderma, 226, 21–30.

    Google Scholar 

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

    Google Scholar 

  • Bonham-Carter, G. F., & Agterberg, F. P. (1990). Application of a microcomputer-based geographic information system to mineral potential mapping. In T. Hanley & D. F. Merriam (Eds.), Microcomputer applications in geology (Vol. 2, pp. 49–74). Oxford: Pergamon Press.

    Google Scholar 

  • Breiman, L. (1984). Classification and regression trees. London: Chapman & Hall/CRC.

    Google Scholar 

  • Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS (Vol. 11). Amsterdam: Elsevier.

    Google Scholar 

  • Carranza, E. J. M. (2009). Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Computers & Geosciences, 35(10), 2032–2046.

    Google Scholar 

  • Carranza, E. J. M. (2010). 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. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110(2), 167–185.

    Google Scholar 

  • Carranza, E. J. M. (2017). Natural resources research publications on geochemical anomaly and mineral potential mapping, and introduction to the special issue of papers in these fields. Natural Resources Research, 26(4), 379–410.

    Google Scholar 

  • Carranza, E. J. M., & Hale, M. (1997). A catchment basin approach to the analysis of geochemical-geological data from Albay province, Philippines. Journal of Geochemical Exploration, 60, 157–171.

    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.

    Google Scholar 

  • Carranza, E. J. M., Hale, M., & Faassen, C. (2008). Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geology Reviews, 33(3–4), 536–558.

    Google Scholar 

  • Carranza, E. J. M., & Laborte, A. G. (2015). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of random forests algorithm. Ore Geology Reviews, 71, 777–787.

    Google Scholar 

  • 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(1), 35–50.

    Google Scholar 

  • 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 Science Informatics, 7, 13–24.

    Google Scholar 

  • Chen, Y., Wu, W., & Zhao, Q. (2019). A bat-optimized one-class support vector machine for mineral prospectivity mapping. Minerals, 9(5), 317.

    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(2), 109–130.

    Google Scholar 

  • 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.

    Google Scholar 

  • Cox, S. F., Etheridge, M. A., & Wall, V. J. (1987). The role of fluids in syntectonic mass transport, and the localization of metamorphic vein-type ore deposits. Ore Geology Reviews, 2(1–3), 65–86.

    Google Scholar 

  • Daviran, M., Maghsoudi, A., Cohen, D. R., Ghezelbash, R., & Yilmaz, H. (2020). Assessment of various fuzzy C-mean clustering validation indices for mapping mineral prospectivity: Combination of multifractal geochemical model and mineralization processes. Natural Resources Research, 29(1), 229–246.

    Google Scholar 

  • Daviran, M., Maghsoudi, A., Ghezelbash, R., & Pradhan, B. A. (2021). A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104688.

    Article  Google Scholar 

  • Demir, N., Kaynarca, M., & Oy, S. (2016). Extraction of coastlines with fuzzy approach using SENTINEL-1 SAR image. The International Archives of Photogrammetry, Remote Sensing and spatial Information Sciences, 41, 747.

    Google Scholar 

  •  Eshraghi, S. A., &  Jalali, A. (2006). Geological Map of Moalleman, 1: 100000. Geological Survey of Iran (GSI).

  • Imamjomeh, A. (2005). Geology, mineralogy, geochemistry and genesis of Chahmoosa copper mine, northwest of Torood, Semnan province. MSc thesis (in Persian).

  • Fard, M., Rastad, E., & Ghaderi, M. (2006). Epithermal gold and base metal mineralization at Gandy deposit, north of Central Iran and the role of rhyolitic intrusions.

  • 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.

    Google Scholar 

  • Ghezelbash, R., & Maghsoudi, A. (2018). A hybrid AHP-VIKOR approach for prospectivity modeling of porphyry Cu deposits in the Varzaghan District NW Iran. Arabian Journal of Geosciences, 11(11), 275.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019a). Performance evaluation of RBF-and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of SA multifractal model and mineralization controls. Earth Science Informatics, 12(3), 277–293.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019b). An improved data-driven multiple criteria decision-making procedure for spatial modeling of mineral prospectivity: Adaption of prediction–area plot and logistic functions. Natural Resources Research, 28(4), 1299–1316.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019c). Mapping of single-and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models. Journal of Geochemical Exploration, 199, 90–104.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., Daviran, M., & Yilmaz, H. (2019d). Incorporation of principal component analysis, geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district, NW Iran. Geochemistry, 79(2), 323–336.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2020). Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district NW Iran. Ore Geology Reviews, 120, 103394.

    Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2020). Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm. Computers & Geosciences, 134, 104335.

    Google Scholar 

  • Han, S., Qubo, C., & Meng, H. (2012). Parameter selection in SVM with RBF kernel function. In World Automation Congress 2012 (pp. 1–4). IEEE.

  • 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.

    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(2), 91–124.

    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.

    Google Scholar 

  • Hu, D., Liu, D., & Xue, Sh. (1995). Explanatory text of geochemical map of Feizabad (7760). Tehran: Geological Survey of Iran.

    Google Scholar 

  • Hushmandzadeh, A. R., Alavi Naini, M., & Haghipour, A.A. (1978). Evolution of geological phenomenon in Totud area: Geological Survey of Iran Report H5, 136 p. (in Farsi).

  • Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making, 186, 58-191.

    Google Scholar 

  • Jolliffe, I. T. (2002). Principal components in regression analysis. Springer-Verlag New York, 167–198.

  • 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.

    Google Scholar 

  • Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352–359.

    Google Scholar 

  • Kreuzer, O. P., Etheridge, M. A., Guj, P., McMahon, M. E., & Holden, D. J. (2008). Linking mineral deposit models to quantitative risk analysis and decision-making in exploration. Economic Geology, 103, 829–850.

    Google Scholar 

  • Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.

    Google Scholar 

  • Lewkowski, C., Porwal, A., & González-Álvarez, I. (2010). Genetic programming applied to base-metal Prospectivity Mapping in the Aravalli Province, India.

  • Lisitsin, V., 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.

    Google Scholar 

  • Liu, P. (2013). Some geometric aggregation operators based on interval intuitionistic uncertain linguistic variables and their application to group decision making. Applied Mathematical Modelling, 37, 2430–2444.

    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, 128–138.

    Google Scholar 

  • 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(2), 125–143.

    Google Scholar 

  • Mehrabi, B., & Siani, M. G. (2012). Intermediate sulfidation epithermal Pb-Zn-Cu (±Ag-Au) mineralization at cheshmeh hafez deposit, Semnan Province Iran. Journal of the Geological Society of India, 80(4), 563–578.

    Google Scholar 

  • Mehrabi, B., Ghasemi, S. M., & Tale, F. E. (2014). Base and precious metal ore-forming system in the Cheshme Hafez and Challu mining area, Torud-Chah shirin magmatic arc. Geosciences, 93, 105–118.

    Google Scholar 

  • 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(3), 209–226.

    Google Scholar 

  • Moon, C. J. (1999). Towards a quantitative model of downstream dilution of point source geochemical anomalies. Journal of Geochemical Exploration, 65(2), 111–132.

    Google Scholar 

  • Moon, W. M. (1990). Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing, 28, 711–720.

    Google Scholar 

  • Moore, F., Rastmanesh, F., Asadi, H., & Modabberi, S. (2008). Mapping mineralogical alteration using principal-component analysis and matched filter processing in the Takab area, north-west Iran, from ASTER data. International Journal of Remote Sensing, 29(10), 2851–2867.

    Google Scholar 

  • Niroomand, S., Hassanzadeh, J., Tajeddin, H. A., & Asadi, S. (2018). Hydrothermal evolution and isotope studies of the Baghu intrusion-related gold deposit, Semnan province, north-central Iran. Ore Geology Reviews, 95, 1028–1048.

    Google Scholar 

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

    Google Scholar 

  • Oh, H.-J., Kim, Y.-S., Choi, J.-K., & Lee, S. (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City Korea. Journal of Hydrology, 399, 158–172.

    Google Scholar 

  • Oh, H. J., & Lee, S. (2010). Application of artificial neural network for gold-silver deposits potential mapping: A case study of Korea. Natural Resources Research, 19, 103–124.

    Google Scholar 

  • Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.

    Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2016). Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran. Journal of Geochemical Exploration, 165, 111–124.

    Google Scholar 

  • Parsa, M., Maghsoudi, A., & Ghezelbash, R. (2016). Decomposition of anomaly patterns of multi-element geochemical signatures in Ahar area, NW Iran: A comparison of U-spatial statistics and fractal models. Arabian Journal of Geosciences, 9(4), 260.

    Google Scholar 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2017). 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.

    Google Scholar 

  • 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 Geology Reviews, 92, 97–112.

    Google Scholar 

  • Pirajno, F. (2012). Hydrothermal mineral deposits: principles and fundamental concepts for the exploration geologist. Berlin: Springer.

    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.

    Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2006). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences, 32, 1–16.

    Google Scholar 

  • Rashidnejad Omran, N. (1992). The study of magmatic evolution in the baghu area and relation with gold mineralization, SE Damghan (M.Sc. thesis). University of Tarbiat Moalem, Tehran, p. 324.

  • Rigol-Sanchez, J. P., Chica-Olmo, M., & Abarca-Hernandez, F. (2003). Artificial neural networks as a tool for mineral potential mapping with GIS. International Journal of Remote Sensing, 24, 1151–1156.

    Google Scholar 

  • 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 Geology Reviews, 71, 804–818.

    Google Scholar 

  • Shamanian, G. H., Hedenquist, J. W., Hattori, K. H., & Hassanzadeh, J. (2004). The Gandy and Abolhassani epithermal prospects in the Alborz magmatic arc, Semnan province Northern Iran. Economic Geology, 99(4), 691–712.

    Google Scholar 

  • Singer, D. A., & Kouda, R. (1988). Integrating spatial and frequency information in the search for Kuroko deposits of the Hokuroku District Japan. Economic Geology, 83(1), 18–29.

    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.

    Google Scholar 

  • Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293–300.

    Google Scholar 

  • Tangestani, M. H., & Moore, F. (2001). Comparison of three principal component analysis techniques to porphyry copper alteration mapping: A case study, Meiduk area, Kerman Iran. Canadian Journal of Remote Sensing, 27(2), 176–182.

    Google Scholar 

  • Tangestani, M. H., & Moore, F. (2002). 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Thompson, M., & Howarth, R. J. (1976). Duplicate analysis in geochemical practice. Part I. Theoretical approach and estimation of analytical reproducibility. Analyst, 101(1206), 690–698.

    Google Scholar 

  • Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study. Springer, Boston, MA. 44, 5–21.

  • Vapnik, V. (1998). Statistical learning theory. New York: Wiley.

    Google Scholar 

  • Vapnik, V., & Chervonenkis, A. Y. (1964). A class of algorithms for pattern recognition learning. Avtomat. i Telemekh, 25(6), 937–945.

    Google Scholar 

  • Wang, Y. J. (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Systems with Applications, 34, 1837–1845.

    Google Scholar 

  • Wang, P., Zhu, Z., & Wang, Y. (2016). A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design. Information Sciences, 345, 27–45.

    Google Scholar 

  • Yilmaz, I. (2007). GIS based susceptibility mapping of karst depression in gypsum: A case study from Sivas basin (Turkey). Engineering Geology, 90, 89–103.

    Google Scholar 

  • Yilmaz, H., Sonmez, F. N., & Carranza, E. J. M. (2015). Discovery of Au-Ag mineralization by geochemical grassroots exploration in metamorphic terrain with extensional tectonic regime in western Turkey. Journal of Geochemical Exploration, 158, 55–73.

    Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015). Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping. Computers and Geosciences, 83, 72–79.

    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.

    Google Scholar 

  • Zuo, R., & Carranza, E. J. M. (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers and Geosciences, 37(12), 1967–1975.

    Google Scholar 

  • 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.

    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.

    Google Scholar 

Download references

Acknowledgments

We thank the Associate Editor and two anonymous reviewers for their constructive edits/comments, which helped us improve this paper, considerably. The senior author is greatly indebted to Mr. Mehrdad Daviran for his generous assistance in the preparation of revised version of this paper.

Author information

Authors and Affiliations

  1. Faculty of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

    Reza Ghezelbash, Abbas Maghsoudi & Amirreza Bigdeli

  2. Geological Sciences, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

    Emmanuel John M. Carranza

Authors
  1. Reza Ghezelbash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Maghsoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amirreza Bigdeli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emmanuel John M. Carranza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abbas Maghsoudi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghezelbash, R., Maghsoudi, A., Bigdeli, A. et al. Regional-Scale Mineral Prospectivity Mapping: Support Vector Machines and an Improved Data-Driven Multi-criteria Decision-Making Technique. Nat Resour Res 30, 1977–2005 (2021). https://doi.org/10.1007/s11053-021-09842-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-021-09842-4

Keywords

Search

Navigation