Document Type : Original Research Paper


1 Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran.

2 Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.

3 Department of Mining Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran.



Most machine learning-monitored algorithms used to create mineral potential prediction maps require noise-free data to achieve high performance and reliable results. Unsupervised clustering methods are highly effective for uncovering a dataset’s hidden structures. Therefore, this study attempts a combination of supervised and unsupervised methods employing training and testing data to generate a highly accurate potential map of the Sonajil copper-gold deposit located in the NW of Iran. Here, a semi-supervised Bayesian algorithm is used to map the mineral landscape. Initially, ten raster layers of exploratory features are prepared. Then based on the copper concentration, 27 exploratory drilled boreholes are divided into four classes, C1 to C4, and from each class, two boreholes are selected, and 100-meter buffering is performed around these boreholes to extract 1113 training data based on the behavioral pattern of boreholes and surface samples. Subsequently, the existing data is clustered using the FCM method, and the total dataset and the clustering data are entered into the Bayesian algorithm to evaluate the accuracy of the Bayesian classifier method across five distinct clusters. The results show increased average accuracy when using clustered data instead of whole data for MPM mapping. Notably, the Bayesian semi-supervised algorithm achieved an impressive accuracy rate of 96% when cluster five data is excluded. To validate the Bayesian semi-supervised method, boreholes data that is not used in training were employed, which confirm the credibility of generated MPM. Overall results highlight the value of the Bayesian semi-supervised algorithm in improving the accuracy and reliability of mineral prospectivity mapping via the application of the FCM clustering method that efficiently organize the data, enabling the Bayesian algorithm to evaluate the accuracy of the Bayesian classifier method across different clusters and providing a successful optimal result in detecting blind ores in areas without exploratory boreholes and delineating more mineralization targets in the Sonajil and adjoining areas.


Main Subjects

[1]. Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). 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 Research28, 1299-1316.R.
[2]. Sun, G., Zeng, Q., & Zhou, J. X. (2022). Machine learning coupled with mineral geochemistry reveals the origin of ore deposits. Ore Geology Reviews142, 104753.
[3]. Liu, Y., Cheng, Q., & Zhou, K. (2019). New insights into element distribution patterns in geochemistry: a perspective from fractal density. Natural Resources Research28, 5-29.
[4]. Jahangiri, M., Ghavami Riabi, S. R., & Tokhmechi, B. (2018). Estimation of geochemical elements using a hybrid neural network-Gustafson-Kessel algorithm. Journal of Mining and Environment9(2), 499-511.
[5]. Zekri, H., Cohen, D. R., Mokhtari, A. R., & Esmaeili, A. (2019). Geochemical prospectivity mapping through a feature extraction–selection classification scheme. Natural Resources Research28, 849-865.
[6]. Zuo, R., Kreuzer, O. P., Wang, J., Xiong, Y., Zhang, Z., & Wang, Z. (2021). Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions. Natural Resources Research30, 3059-3079.
[7]. Vemuri, V. K. (2020). The Hundred-Page Machine Learning Book: by Andriy Burkov, Quebec City, Canada, 2019, 160 pp., 49.99(Hardcover); 29.00 (paperback); 25.43(KindleEdition),(Alternatively,canpurchaseatleanpub.comataminimumpriceof 20.00), ISBN 978-1999579517.
[8]. Nykänen, V., Groves, D. I., Ojala, V. J., & Gardoll, S. J. (2008). Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences55(1), 39-59.
[9]. Zhang, Z., Zuo, R., & Xiong, Y. (2016). A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences59, 556-572.
[10]. Cheng, Q. (2015). BoostWofE: A new sequential weights of evidence model reducing the effect of conditional dependency. Mathematical Geosciences47(5), 591-621.
[11]. Ghezelbash, R., Maghsoudi, A., Bigdeli, A., & Carranza, E. J. M. (2021). Regional-scale mineral prospectivity mapping: Support vector machines and an improved data-driven multi-criteria decision-making technique. Natural Resources Research30, 1977-2005.
[12]. Tao, J., Zhang, N., Chang, J., Chen, L., Zhang, H., & Chi, Y. (2022). Unlabeled sample selection for mineral prospectivity mapping by semi-supervised support vector machine. Natural Resources Research31(5), 2247-2269.
[13]. 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 Research25, 35-50.
[14]. Gao, Y., Zhang, Z., Xiong, Y., & Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews75, 16-28.
[15]. 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 Research25(2), 125-143.
[16]. Zhang, D., Ren, N., & Hou, X. (2018). An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1. 0) and its application to mineral prospectivity mapping. Geoscientific Model Development11(6), 2525-2539.
[17]. Zhang, Z. J., Cheng, Q. M., Yang, J., & Hu, X. L. (2018). Characterization and origin of granites from the Luoyang Fe deposit, southwestern Fujian Province, South China. Journal of Geochemical Exploration184, 119-135.
[18]. Mohammadzadeh, M., & Nasseri, A. (2018). Geochemical modeling of orogenic gold deposit using PCANN hybrid method in the Alut, Kurdistan province, Iran. Journal of African Earth Sciences139, 173-183.
[19]. Chen, G., Huang, N., Wu, G., Luo, L., Wang, D., & Cheng, Q. (2022). Mineral prospectivity mapping based on wavelet neural network and Monte Carlo simulations in the Nanling W-Sn metallogenic province. Ore Geology Reviews143, 104765.
[20]. Li, S., Chen, J., Liu, C., & Wang, Y. (2021). Mineral prospectivity prediction via convolutional neural networks based on geological big data. Journal of Earth Science32, 327-347.
[21]. Porwal, A., Carranza, E. J. M., & Hale, M. (2006). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences32(1), 1-16.
[22]. Liu, Y., Cheng, Q., Xia, Q., & Wang, X. (2015). The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China. Frontiers of Earth Science9, 342-354.
[23]. Zaidi, F. K., Nazzal, Y., Ahmed, I., Naeem, M., & Jafri, M. K. (2015). Identification of potential artificial groundwater recharge zones in Northwestern Saudi Arabia using GIS and Boolean logic. Journal of African Earth Sciences111, 156-169.
[24]. Aryafar, A., & Roshanravan, B. (2020). Improved index overlay mineral potential modeling in brown-and green-fields exploration using geochemical, geological and remote sensing data. Earth Science Informatics13, 1275-1291.
[25]. Carranza, E. J. M., Van Ruitenbeek, F. J. A., Hecker, C., van der Meijde, M., & van der Meer, F. D. (2008). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation10(3), 374-387.
[26]. Abedi, M., Norouzi, G. H., & Fathianpour, N. (2013). Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation21, 556-567.
[27]. Hosseini, S. A., & Abedi, M. (2015). Data envelopment analysis: a knowledge-driven method for mineral prospectivity mapping. Computers & Geosciences82, 111-119.
[28]. Zuo, R. (2017). Machine learning of mineralization-related geochemical anomalies: A review of potential methods. Natural Resources Research26, 457-464.
[29]. Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.".
[30]. Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., & Sadeghi, B. (2022). Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran. Minerals12(6), 689.
[31]. Zhou, Q., Yin, J. Y., Liang, W. Y., Chen, D. M., Yuan, Q., Feng, B. L., ... & Wang, Y. T. (2021). Various machine learning approaches coupled with molecule simulation in the screening of natural compounds with xanthine oxidase inhibitory activity. Food & function12(4), 1580-1589.
[32]. Rahimi, H., Abedi, M., Yousefi, M., Bahroudi, A., & Elyasi, G. R. (2021). Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof. Applied Geochemistry128, 104940.
[33]. Yousefi, M., & Hronsky, J. M. (2023). Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting. Applied Geochemistry149, 105561.
[34]. Yousefi, M., Carranza, E. J. M., Kreuzer, O. P., Nykänen, V., Hronsky, J. M., & Mihalasky, M. J. (2021). Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook. Journal of Geochemical Exploration229, 106839.
[35]. Keykhay-Hosseinpoor, M., Kouhsari, A. H., Hossein Morshedy, A., & Porwal, A. (2021). Porphyry Cu-Au prospectivity modelling using semi-supervised learning algorithm in Dehsalm district, eastern Iran. Journal of Economic Geology13(1), 193-213.
[36]. Afzal, P., Farhadi, S., Boveiri Konari, M., Shamseddin Meigooni, M., & Daneshvar Saein, L. (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia12(1), 191-199.
[37]. Chen, Y., & Lu, L. (2023). The Anomaly Detector, Semi-supervised Classifier, and Supervised Classifier Based on K-Nearest Neighbors in Geochemical Anomaly Detection: A Comparative Study. Mathematical Geosciences, 1-23.
[38]. Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. J. O. G. R. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews71, 804-818.
[39]. Wang, Z., & Zuo, R. (2022). Mineral prospectivity mapping using a joint singularity-based weighting method and long short-term memory network. Computers & Geosciences158, 104974.
[40]. Seyedrahimi-Niaraq, M., Mahdiyanfar, H., & Mokhtari, A. R. (2022). Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts. Journal of Geochemical Exploration234, 106924.
[41]. Hosseinzadeh, G., Calagari, A. A., Moayyed, M., Hadj-Alilu, B., & Moazzen, M. (2010). Study of Hypogen Alteration and Copper Mineralization in Sonajil Area (East of Herris, East Azarbaidjan). Scientific Quarterly Journal of Geosciences19(74), 3-12.
[42]. Moshefi, P., Hosseinzadeh, M. R., Moayyed, M., & Lentz, D. R. (2018). Comparative study of mineral chemistry of four biotite types as geochemical indicators of mineralized and barren intrusions in the Sungun Porphyry Cu-Mo deposit, northwestern Iran. Ore Geology Reviews97, 1-20.
[43]. Hernández-González, J., Inza, I., & Lozano, J. A. (2013). Learning Bayesian network classifiers from label proportions. Pattern Recognition46(12), 3425-3440.
[44]. Wu, J., Pan, S., Zhu, X., Cai, Z., Zhang, P., & Zhang, C. (2015). Self-adaptive attribute weighting for Naive Bayes classification. Expert Systems with Applications42(3), 1487-1502.
[45]. Webb, G. I., Boughton, J. R., Zheng, F., Ting, K. M., & Salem, H. (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Machine learning86, 233-272.
[46]. Taalab, K., Corstanje, R., Zawadzka, J., Mayr, T., Whelan, M. J., Hannam, J. A., & Creamer, R. (2015). On the application of Bayesian networks in digital soil mapping. Geoderma259, 134-148.
[47]. Webb, A. R., & Copsey, K. D. (2002). Statistical Pattern Recognition. John Wiley & Sons. New York, USA.
[48]. Zabihi, S. M., & Akbarzadeh-T, M. R. (2012). Generalized fuzzy C-means clustering with improved fuzzy partitions and shadowed sets. International Scholarly Research Notices2012.
[49]. Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.
[50]. Xie, X. L., & Beni, G. (1991, August). A new fuzzy clustering validity criterion and its application to color image segmentation. In Proceedings of the 1991 IEEE International Symposium on Intelligent Control (pp. 463-468). IEEE.
[51]. Bensaid, A. M., Hall, L. O., Bezdek, J. C., Clarke, L. P., Silbiger, M. L., Arrington, J. A., & Murtagh, R. F. (1996). Validity-guided (re) clustering with applications to image segmentation. IEEE Transactions on fuzzy systems4(2), 112-123.
[52]. Salehi, T., & Tangestani, M. H. (2020). Per-pixel analysis of ASTER data for porphyry copper hydrothermal alteration mapping: a case study of NE Isfahan, Iran. Remote Sensing Applications: Society and Environment20, 100377.
[53]. Zhao, Z. F., Zhou, J. X., Lu, Y. X., Chen, Q., Cao, X. M., He, X. H., ... & Feng, W. J. (2021). Mapping alteration minerals in the Pulang porphyry copper ore district, SW China, using ASTER and WorldView-3 data: Implications for exploration targeting. Ore Geology Reviews134, 104171.
[54]. Pour, A. B., & Hashim, M. (2012). The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore geology reviews44, 1-9.
[55]. 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 geology reviews70, 385-406.
[56]. Du, X., Zhou, K., Cui, Y., Wang, J., & Zhou, S. (2021). Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model. ISPRS International Journal of Geo-Information10(11), 766.
[57]. Chen, Y., & Wu, W. (2017). Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data. Australian Journal of Earth Sciences64(5), 639-651.
[58]. Ding, K., Xue, L., Ran, X., Wang, J., & Yan, Q. (2022). Siamese network based prospecting prediction method: A case study from the Au deposit in the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China. Ore Geology Reviews, 105024.
[59]. Oonk, S., & Spijker, J. (2015). A supervised machine-learning approach towards geochemical predictive modelling in archaeology. Journal of archaeological science59, 80-88.
[60]. Sun, G., Zeng, Q., & Zhou, J. X. (2022). Machine learning coupled with mineral geochemistry reveals the origin of ore deposits. Ore Geology Reviews142, 104753.
[61]. Zhang, N., Zhou, K., & Li, D. (2018). Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China. Earth Science Informatics11, 553-566.