Mineral potential mapping of porphyry copper using a fuzzy inference system in the Kerman belt

Document Type : Research Article

Authors

1 Dept. of Mining Engineering, Tabriz University of Technology (Sahand), Tabriz, Iran

2 Dept. of Mining Engineering, University of Tehran, Tehran, Iran

10.22034/anm.2025.22750.1664

Abstract

The implementation of mineral exploration operations is inherently expensive and time-consuming, often requiring extensive fieldwork, laboratory analysis, and the integration of diverse datasets. One effective strategy for optimizing resources and reducing costs is the application of mineral potential modeling (MPM), which aims to predict areas with higher probabilities of mineralization. MPM can be categorized as an information-driven decision-making process that synthesizes various geoscientific datasets—such as geological, geochemical, geophysical, remote sensing, and alteration data—each offering complementary insights into subsurface conditions. In the present study, a fuzzy-based approach is proposed to model the mineral potential for the exploration of porphyry copper deposits. The method integrates multiple exploration datasets, including geophysical, geochemical, geological, and remote sensing layers. Initially, each thematic map was transformed through a fuzzification process using linguistic variables and trapezoidal membership functions to model the inherent uncertainty and ambiguity in boundary definitions between spatial features. These fuzzified layers were then integrated using a Mamdani-type fuzzy inference system (FIS), which enables the translation of expert knowledge into a rule-based system for predictive mapping. To evaluate and validate the performance of the proposed fuzzy approach, a comparative analysis was conducted using the modified TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, a multi-criteria decision-making technique. Both models were assessed using Prediction-Area (P-A) plots and Normalized Density metrics. The Normalized Density value for the modified TOPSIS approach was calculated as 4, while the proposed fuzzy logic approach yielded a significantly higher Normalized Density value of 6.14, indicating its superior predictive capability. This enhanced performance suggests that the fuzzy-based model not only improves the accuracy of mineral potential maps but also offers greater flexibility for application under various geological scenarios. The ability to incorporate expert knowledge and handle data uncertainty makes the proposed method a robust and interpretable tool for mineral exploration targeting.

Keywords

Main Subjects

References

[1]                 Saremi, M., et al., Unsupervised Anomaly Detection for Mineral Prospectivity Mapping Using Isolation Forest and Extended Isolation Forest Algorithms. Minerals, 2025. 15(4): p. 411.
[2]                 keykhay hosseinpoor, M. , Kohsary, A. H. , Hossein Morshedy, A. and Porwal, A. (2020). Mineral Potential Modeling Using Deep Learning Auto-Encoder Network in the Dehsalm District, Eastern Iran. Journal of Analytical and Numerical Methods in Mining Engineering, 10(22), 77-94. doi: 10.29252/anm.2020.12606.1412
[3]                 Saremi, M., et al., Evaluation of Deep Isolation Forest (DIF) Algorithm for Mineral Prospectivity Mapping of Polymetallic Deposits. Minerals, 2024. 14(10): p. 1015.
[4]                 Hronsky, J.M. and D.I. Groves, Science of targeting: definition, strategies, targeting and performance measurement. Australian Journal of Earth Sciences, 2008. 55(1): p. 3-12.
[5]                 Carranza, E.J.M., Geochemical anomaly and mineral prospectivity mapping in GIS. Vol. 11. 2008: Elsevier.
[6]                 Carranza, E.J.M. and M. Hale, Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Natural Resources Research, 2002. 11: p. 45-59.
[7]                 Zuo, R., et al., 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, 2015. 71: p. 502-515.
[8]                 Chen, C., et al. Mineral prospectivity mapping integrating multi-source geology spatial data sets and logistic regression modelling. in Proceedings 2011 IEEE international conference on spatial data mining and geographical knowledge services. 2011. IEEE.
[9]                 Porwal, A., et al., Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geology Reviews, 2010. 38(3): p. 184-196.
[10]             Porwal, A., E. Carranza, and M. Hale, Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Natural resources research, 2003. 12: p. 155-171.
[11]             Oh, H.-J. and S. Lee, Application of artificial neural network for gold–silver deposits potential mapping: A case study of Korea. Natural Resources Research, 2010. 19: p. 103-124.
[12]             Abedi, M. and G.-H. Norouzi, Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. Journal of Applied Geophysics, 2012. 83: p. 35-45.
[13]             Zuo, R. and E.J.M. Carranza, Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 2011. 37(12): p. 1967-1975.
[14]             Abedi, M., G.-H. Norouzi, and A. Bahroudi, Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences, 2012. 46: p. 272-283.
[15]             Carranza, E.J.M. and A.G. Laborte, Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geology Reviews, 2015. 71: p. 777-787.
[16]             Parsa, M., A. Maghsoudi, and M. Yousefi, Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran. Ore Geology Reviews, 2018. 92: p. 97-112.
[17]             Porwal, A., E.J.M. Carranza, and M. Hale, Bayesian network classifiers for mineral potential mapping. Computers & Geosciences, 2006. 32(1): p. 1-16.
[18]             Wang, C., J. Chen, and Y. Ouyang, Determination of predictive variables in mineral prospectivity mapping using supervised and unsupervised methods. Natural Resources Research, 2022. 31(4): p. 2081-2102.
[19]             Qaderi, S., et al., Geological Controlling Factors on Mississippi Valley-Type Pb-Zn Mineralization in Western Semnan, Iran. Minerals, 2024. 14(9): p. 957.
[20]             Hajihosseinlou, M., A. Maghsoudi, and R. Ghezelbash, Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran. Expert systems with applications, 2024. 237: p. 121668.
[21]             Yu, X., et al., Data-driven mineral prospectivity mapping based on known deposits using association rules. Natural Resources Research, 2024. 33(3): p. 1025-1048.
[22]             Abedi, M., S. Torabi, and G. Norouzi, Application of fuzzy AHP method to integrate geophysical data in a prospect scale, a case study: Seridune copper deposit. Bollettino di Geofisica Teorica ed Applicata, 2013. 54(2).
[23]             Yousefi, M. and E.J.M. Carranza, Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences, 2015. 74: p. 97-109.
[24]             Bahrami, Y., H. Hassani, and A. Maghsoudi, Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal, 2021. 86: p. 1797-1816.
[25]             Nykänen, V., et al., Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper–gold deposits in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences, 2008. 55(1): p. 25-38.
[26]             Afzal, P., et al., Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis. Journal of African Earth Sciences, 2016. 119: p. 139-149.
[27]             Moon, W.M., Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing, 1990. 28(4): p. 711-720.
[28]             Abedi, M. and G.-H. Norouzi, 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, 2016. 46: p. 31-44.
[29]             Abedi, M., et al., PROMETHEE II: A knowledge-driven method for copper exploration. Computers & Geosciences, 2012. 46: p. 255-263.
[30]             Moradpour, H., et al., Fusion of ASTER satellite imagery, geochemical and geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran. International Journal of Image and Data Fusion, 2022. 13(1): p. 71-94.
[31]             Porwal, A. and E.J.M. Carranza, Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration. 2015, Elsevier. p. 477-483.
[32]             Porwal, A., E.J.M. Carranza, and M. Hale, A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Natural Resources Research, 2006. 15: p. 1-14.
[33]             Porwal, A.K., Mineral potential mapping with mathematical geological models. 2006.
[34]             Ferrier, G., et al., Prospectivity mapping for epithermal deposits of Western Milos using a fuzzy multi criteria evaluation approach parameterized by airborne hyperspectral remote sensing data. Geosciences, 2019. 9(3): p. 116.
[35]             Pahlavani, P., S. Riahi, and B. Bigdeli, Ranking potentially favorable mineralization zones using fuzzy VIKOR vs. Dempster-Shafer-fuzzy AHP methods, a case study: southeast of the Sarcheshmeh copper mine, Kerman, Iran. Arabian journal of Geosciences, 2020. 13: p. 1-21.
[36]             Porwal, A., et al., 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, 2015. 71: p. 839-852.
[37]             Chudasama, B., Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations. MethodsX, 2022. 9: p. 101629.
[38]             Khammar, F., et al., Translating mineral systems criteria into a prospectivity model for IOCG deposits in the Kolari region, Finland. Ore Geology Reviews, 2024: p. 106326.
[39]             Barak, S., M. Abedi, and A. Bahroudi, A knowledge-guided fuzzy inference approach for integrating geophysics, geochemistry, and geology data in a deposit-scale porphyry copper targeting, Saveh, Iran. Bollettino di Geofisica Teorica ed Applicata, 2020. 61(2).
[40]             Barak, S., A. Imamalipour, and M. Abedi, Employing multiple prospectivity mapping and exploration targeting, a case study from the Sonajil porphyry copper deposit, north-western Iran. Bulletin of Geophysics and Oceanography, DOI, 2024. 10.
[41]             Khalifani, F.M., et al., The application of various mineral prospectivity modeling in the exploration of orogenic gold deposit in Saqez-Sardasht region, northwest Iran. Lithology and Mineral Resources, 2023. 58(4): p. 368-386.
[42]             Chudasama, B., S. Thakur, and A. Porwal, Fuzzy Inference Systems for Mineral Exploration, in Encyclopedia of Mathematical Geosciences. 2021, Springer. p. 1-5.
[43]             Parsa, M., A. Maghsoudi, and M. Yousefi, 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, 2017. 58: p. 157-167.
[44]             Hoogendoorn, S., S. Hoogendoorn-Lanser, and H. Schuurman. Fuzzy perspectives in traffic engineering. in Workshop on Intelligent Traffic Management Models. 1999.
[45]             Sayers, T., et al. Improving the traffic responsiveness of signal controllers using fuzzy logic. in IEE Colloquium on Urban Congestion Management. 1995. IET.
[46]             Sayers, T., et al. Traffic responsive signal control using fuzzy logic-A practical modular approach. in IEE Colloquium on Fuzzy Logic Controllers in Practice. 1996. IET.
[47]             Tangestani, M. and F. Moore, Mapping porphyry copper potential with a fuzzy model, northern Shahr‐e‐Babak, Iran. Australian Journal of Earth Sciences, 2003. 50(3): p. 311-317.
[48]             Barak, S., A. Bahroudi, and G. Jozanikohan, The use of fuzzy inference system in the integration of copper exploration layers in Neysian. Journal of Mining Engineering, 2018. 13(38): p. 97-112.
[49]             Bonham-Carter, G.F., Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists, 1994. 13: p. 398.
[50]             Karimi, M., M. Menhaj, and M. Mesgari, Mineral potential mapping of copper minerals using fuzzy logic in GIS invironment. Beijing, China: ISPRS, 2008: p. 170-181.
[51]             Yousefi, M., M.D. Lindsay, and O. Kreuzer, Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS). Ore Geology Reviews, 2024. 165: p. 105930.
[52]             Tripathi, K., A review on knowledge-based expert system: concept and architecture. IJCA Special Issue on Artificial Intelligence Techniques-Novel Approaches & Practical Applications, 2011. 4: p. 19-23.
[53]             Czogala, E. and J. Leski, Fuzzy and neuro-fuzzy intelligent systems. Vol. 47. 2012: Physica.
[54]             Galetakis, M., et al. Applications of fuzzy inference systems in mineral industry-an overview. in MATEC Web of Conferences. 2021. EDP Sciences.
[55]             Castellano, G., A.M. Fanelli, and C. Mencar, Generation of interpretable fuzzy granules by a double-clustering technique. Archives of Control Science, 2002. 12(4): p. 397-410.
[56]             Ying, H., Fuzzy control and modeling: analytical foundations and applications. 2000: Wiley-IEEE Press.
[57]             Berberian, F. and M. Berberian, Tectono‐plutonic episodes in Iran. Zagros Hindu Kush Himalaya Geodynamic Evolution, 1981. 3: p. 5-32.
[58]             Ahmad, T. and M. Posht Kuhi, Geochemisty and petrogenesis of Urumiah-Dokhtar volcanics around Nain and Rafsanjan areas: a preliminary study. Treatise on the geology of Iran, Iranian Ministry of Mines and Metals, 1993. 90.
[59]             Hassanzadeh, J., Metallogenic and tectonomagmatic events in the SE sector of the Cenozoic active continental margin of central Iran (Shahr e Babak area, Kerman Province). 1993: University of California, Los Angeles.
[60]             Omrani, J., et al., Arc-magmatism and subduction history beneath the Zagros Mountains, Iran: a new report of adakites and geodynamic consequences. Lithos, 2008. 106(3-4): p. 380-398.
[61]             Takin, M., Iranian geology and continental drift in the Middle East. nature, 1972. 235(5334): p. 147-150.
[62]             Dercourt, J., et al., Geological evolution of the Tethys belt from the Atlantic to the Pamirs since the Lias. Tectonophysics, 1986. 123(1-4): p. 241-315.
[63]             Dercourt, J., L.E. Ricou, and B. Vrielynck, Atlas Tethys palaeoenvironmental maps. (No Title), 1993.
[64]             Agard, P., et al., Convergence history across Zagros (Iran): constraints from collisional and earlier deformation. International journal of earth sciences, 2005. 94: p. 401-419.
[65]             Sengor, A., et al., Origin and assembly of the Tethyside orogenic collage at the expense of Gondwana Land. 1988.
[66]             Ricou, L., J. Braud, and B. JH, Le Zagros. 1977.
[67]             Waterman, G.C. and R. Hamilton, The Sar Cheshmeh porphyry copper deposit. Economic Geology, 1975. 70(3): p. 568-576.
[68]             Ranjbar, H. and M. Honarmand, 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, 2004. 25(21): p. 4729-4741.
[69]             Jahantigh, M., et al., Identifying high potential area of porphyry copper mineralization using the Aaptive Nero fuzzy method in Shahre-Babak studied area, Kerman province. Advanced Applied Geology, 2024. 14(1).
[70]             Pour, A.B. and M. Hashim, Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. Journal of Asian Earth Sciences, 2011. 42(6): p. 1309-1323.
[71]             maasoumi, I. and Ranjbar, H. (2018). Enhancement of Hydrothermal Alteration in ASTER Images of Sar Cheshmeh Area, Using Logical Operators. Journal of Analytical and Numerical Methods in Mining Engineering, 7(14), 61-71. doi: 10.29252/anm.7.14.61
[72]             Crowley, J.K., D.W. Brickey, and L.C. Rowan, Airborne imaging spectrometer data of the Ruby Mountains, Montana: mineral discrimination using relative absorption band-depth images. Remote Sensing of Environment, 1989. 29(2): p. 121-134.
[73]             Abedi, M., et al., ELECTRE III: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of applied geophysics, 2012. 87: p. 9-18.
[74]             Clark, D.A., Magnetic effects of hydrothermal alteration in porphyry copper and iron-oxide copper–gold systems: A review. Tectonophysics, 2014. 624: p. 46-65.
[75]             Siemon, B., Improved and new resistivity-depth profiles for helicopter electromagnetic data. Journal of Applied Geophysics, 2001. 46(1): p. 65-76.
[76]             Alaei Moghadam, S., et al., Modeling process of mineral potential mapping using Fuzzy inference systems (Case study: Chah Firoozeh Copper Deposit). Scientific Quarterly Journal of Geosciences, 2014. 24(93): p. 53-66.
[77]             Porwal, A., E. Carranza, and M. Hale, Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Natural resources research, 2003. 12(3): p. 155-171.
[78]             Cheng, Q., F. Agterberg, and S. Ballantyne, The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical exploration, 1994. 51(2): p. 109-130.
[79]             Ziaii, M. , Shabani, A. , Soleimani Monfared, M. , Shirazi, A. and Hamedi, S. A. (2025). Mineral Prospectivity Mapping of the Hidden Cu-Au Porphyry Mineralization in the Basiran and Kodegan 1:100,000 Sheets. Journal of Analytical and Numerical Methods in Mining Engineering, 15(42), 37-50. doi: 10.22034/anm.2023.18917.1568
[80]             Yousefi, M. and E.J.M. Carranza, Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 2015. 79: p. 69-81.
[81]             Mihalasky, M.J. and G.F. Bonham-Carter, Lithodiversity and its spatial association with metallic mineral sites, Great Basin of Nevada. Natural Resources Research, 2001. 10: p. 209-226.
[82]             Hwang, C.-L., et al., Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey, 1981: p. 58-191.
[83]             Ren, L., et al., Comparative analysis of a novel M-TOPSIS method and TOPSIS. Applied Mathematics Research eXpress, 2007. 2007: p. abm005.

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  • Receive Date: 04 February 2025
  • Revise Date: 01 October 2025
  • Accept Date: 08 November 2025

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