High-grade zone estimation using the SVM and BPPN algorithms in Chah Firuzeh porphyry copper deposit

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.

2 Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran.

3 Department of Drilling and Geo-engineering, Faculty of Drilling, Oil and Gas, AGH University of Krakow, Krakow, Poland.

10.22059/ijmge.2026.401926.595303

Abstract

In the Chah Firuzeh porphyry copper deposit, a number of thirteen coring boreholes were drilled to evaluate the copper grades in the anomaly. While twelve of the boreholes intersected only the low- and medium-grade copper zones, the borehole CHF06 reached a high-grade zone. As such a high-grade zone is economically invaluable in the financial perspective of the mine, the corresponding copper grades must be estimated precisely. The primary goal of the current study is to estimate the copper grades in such high-profit zone using three artificial intelligence (AI) techniques: Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). Due to the porphyry nature of deposit, no clear relation was found between the copper grades of the borehole CHF06 and the rest. To address this issue, the Genetic Algorithm-Artificial Neural Network (GA-ANN) and Principal Component Analysis (PCA) algorithms were utilized to choose the best input dataset for those three AI techniques. Both the GA-ANN and PCA algorithms detected that the copper grades of the boreholes CHF05, CHF21, CHF24, and CHF26 are the most appropriate input data to be imported into the SVM and BPNN models. After grade estimation, the R-square (R2) of the SVM and BPNN, techniques were obtained as 0.98 and 0.72, respectively. Moreover, further analysis uncovered that the SVM model has the least sensitivity to the ratio of training data to testing data. Hence, the SVM approach was recognized as the most reliable AI technique to accurately solve the complex resource estimation problems in mining projects. This key finding implies that a SVM estimator can be applied not only for the uniform-mineralization ores but also for the deposits exhibiting a highly inconsistent grade-trend in their structures.

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Main Subjects

References

[1].    Mostafaei, K. and Ramazi, H. Mineral Resource estimation using a combination of drilling and IP-Rs data using statistical and cokriging methods. Bulletin of the mineral research and exploration. 2019; 160: 177-195.
[2].   Yama, B.R., Lineberry, G.T. Artificial neural network application for a predictive task in mining. Mining Engineering. 1999; 51: 59 – 64.
[3].   Tahmasebi, P. and Hezarkhani, A. Application of adaptive neuro-fuzzy inference system for grade estimation; case study, Sarcheshmeh porphyry copper deposit, Kerman, Iran. Australian Journal of Basic and Applied Sciences. 2010; 4(3): 408-420.
[4].   Rumelhart, D.E., Hinton, G.E., Wiliams, R.J. Learning representations by back-propagating errors. Nature. 1986; 323: 533-536.
[5].   Fahlman, S.E. Faster-learning variations of back-propagation: An empirical study. In 1988 Connectionist Models Summer School. Morgan Kaufmann. 1998: 38-51.
[6].   Clarici, E., Owen, D., Durucan, S., Ravencroft, P. Recoverable reserve estimation using a neural network. In 24th International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM), Montreal, Quebec.1993: 145-152.
[7].   Hagan M.T., Menhaj, M. Training feed forward networks with the Marquardt algorithm. IEEE Trans, Neural Networks.1994; 5(6): 989-993.
[8].   Wilamowski, B.M., Cotton, N.J., Kaynak, O., Dundar, G. Computing gradient vector and Jacobian matrix in arbitrarily connected neural networks. IEEE Transaction on industrial Electronic 56(10), 3784-3790 (2008).
[9].   Maleki, Sh., Moradzadeh, A., Ghavami, R., and Sadeghzadeh, F. A Robust Methodology for Prediction of DT Wireline Log. Iranian Journal of Earth Sciences. 2013; 5, 33-40.
[10]. Mostafaei, K. and Ramazi, H. Investigating the applicability of induced polarization method in ore modelling and drilling optimization: a case study from Abassabad, Iran. Near Surface Geophysics.2019; 17:637-652.
[11].  Lin, N., Chen, Y., Liu, H., & Liu, H. A comparative study of machine learning models with hyperparameter optimization algorithm for mapping mineral prospectivity. Minerals. 2021; 11(2): 159.
[12]. Chen, G., Huang, N., Wu, G., Luo, L., Wang, D., & Cheng, Q. Mineral prospectivity mapping based on wavelet neural network and Monte Carlo simulations in the Nanling W-Sn metallogenic province. Ore Geology Reviews.2022; 143: 104765.
[13].  Chen, F., Tiwari, S., Mohammed, K. S., Huo, W., & Jamróz, P. Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs. Resources Policy.2023; 81, 103307.
[14]. Yousefi, M., Lindsay, M. D., & Kreuzer, O. 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; 105930.
[15].  Wu, X., Zhou, Y. Reserve estimation using neural network techniques. Computers and Geosciences, 1993; 19: 567-575.
[16].  Kapageridis, I. Input space configuration effects in neural network-based grade estimation. Computers & Geosciences. 2005; 31: 704-717.
[17].  Burnett, C.C. Application of neural networks to mineral reserve estimation, Ph.D. Dissertation, Department of Mineral Resources Engineering, University of Nottingham, Nottingham, 254 p. 1995.
[18]. Badel, M., Angorani, S., Shariat Panahi, M. The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit. Computers and Geosciences. 2010; 37: 530-540.
[19]. Maleki, Sh., Moradzadeh, A., Riabi, R.G., Sadaghzadeh, F. Comparison of Several Different Methods of in situ stresses determination, International Journal of Rock Mechanics & Mining Sciences. 2014; 71: 395-404.
[20]. Kapageridis, I., Denby, B. Ore grade estimation with modular neural network Systems-a case study. Information Technology in the Mineral Industry. In G. N. Panagiotou and T. N. Michalakopoulos, eds., Information technologies in the minerals industry: A. A. Balkema Publishers, Rotterdam, p. 52 (1998).
[21]. Vapnik, V. The nature of statistical learning theory. Springer science & business media. 1999. DOI 10.1007/978-1-4757-3264-1.
[22]. Maleki, Sh., Moradzadeh, A., Riabi, R.G., Gholami, R., Sadaghzadeh, F. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods. NRIAG Journal of Astronomy and Geophysics. 2014; 3:70-81.
[23]. Cristianini, N. Shawe-Taylor, J. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press. 2000.
[24]. Zuo, R., & Carranza, E. J. M. Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences.2011; 37(12): 1967-1975.
[25]. Zhang, N., Zhou, K., & Li, D. Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China. Earth Science Informatics.2018; 11: 553-566.
[26]. Xiong, Y., & Zuo, R. Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Computers & geosciences.2020; 140: 104484.
[27]. Mostafaei, K., Maleki, S., Jodeiri Shokri, B., & Yousefi, M. Predicting gold grade by using support vector machine and neural network to generate an evidence layer for 3D prospectivity analysis. International Journal of Mining and Geo-Engineering.2023; 57(4): 435-444.
[28]. Zheng, C., Yuan, F., Luo, X., Li, X., Liu, P., Wen, M & Albanese, S. Mineral prospectivity mapping based on Support vector machine and Random Forest algorithm-A case study from Ashele copper-zinc deposit, Xinjiang, NW China. Ore Geology Reviews. 2023; 105567.
[29]. Bilal, A., Imran, A., Baig, T. I., Liu, X., Long, H., Alzahrani, A., & Shafiq, M. Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification. Plos one. 2024; 19(1): e0295951.
[30]. Olya, B. A. M., & Mohebian, R. E. Z. A. Q-factor estimation from vertical seismic profiling (vsp) with deep learning algorithm, cudnnlstm. Journal of Seismic Exploration, 2023; 32, 89-104.
[31].  Rajabi, A., Mahmoodi, P., Rastad, E., Niroomand, S., Peernajmodin, H., Akbari, Z., & Haghi, A. A review of fluid inclusion investigations on Cretaceous sediment-hosted Zn-Pb (±Ba±Fe±Ag±Cu) deposits in the Malayer-Esfahan metallogenic belt (MEMB). 2019; In Third Biennial Iranian National Fluid Inclusion conference.
[32]. Rajabi, A., Rastad, E., Mahmoodi, P., Niroomand S., Peernajmodin, H., Movahednia, M., Fadaei, M., Olya, B. A. M., Boveiri, M. Zinc-Lead (± Ba ± Fe ± Ag ± Cu) mineralization in Malayer-Esfahan metallogenic belt, 2019: The 7th National Conference on Geomorphology and Structural Geology of Iran.
[33]. Hezarkhani, A. A Fluid Inclusion Investigation on Chah-Firuzeh Porphyry Copper Deposit, Based on Drill Core No. 6, Ahar Copper Company, Internal report, p. 42; 2007.
[34]. Hezarkhani, A. Hydrothermal fluid geochemistry at the Chah-Firuzeh porphyry copper deposit, Iran: Evidence from fluid inclusions, Journal of Geochemical Exploration. 2009; 101: 254–264.
[35]. Scholkopf, B., Smola, A.J., Muller, K.R. Nonlinear component analysis as a kernel igenvalues problem, Neural Computed.1998; 10: 1299-1319.
[36]. Mostafaei, K., Maleki, S., Zamani M.A.M Knez, D. Risk management prediction of mining and industrial projects by support vector machine. Resources Policy. 2022; 78 .1-8.
[37]. Tran, Q.A., Li, X. and Duan, H. Efficient performance estimate for one-class support vector machine. Pattern Recognition Letters.2005; 26: 1174-1182.
[38]. Walczack, B., Massart, D.L. The radial basis functions-partial least squares approach as a flexible non-linear regression technique. Anal.Chimical Acta. 1996; 331: 177-185.
[39]. Al-Anazi, A.F., Gates, I.D. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study. Computers and Geosciences.2010; 36: 1494-1503.
[40]. Keerthi, S.S., Lin, C.J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation.2003; 15(7): 1667-1689.
[41]. Schölkopf, B., Smola, A.J. and Bach, F. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press. London, England. 2002.
[42]. Li, Q., Licheng, J., Yingjuan, H. Adaptive simplification of solution for support vector machine, Pattern Recognition. 2007; 40: 972 -980.
[43]. Wu, C.H., Tzeng, G.H., Lin, R.H. A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications.2009; 36: 4725-4735.
[44]. Wang, L. Support Vector Machines: Theory and Applications, Springer Science & Business Media. 2005.
[45]. Platt, J. Sequential minimal optimization: A fast algorithm for training support vector machines. 1998.
[46]. Maleki Sh., Ramazi H.R., Moradi S. Estimation of Iron Concentration by Using a Support Vector Machine and an Artificial Neural Network - the Case Study of the Choghart Deposit southeast of Yazd, Yazd, Iran. Geopersia.2014; 4(2): 201-212.
[47]. Khanlari, G.R., Heidari, M., Momeni, A.A., Abdilor Y. Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology.2012; 131-132:11-18. 
[48]. Plett, G.L. Adaptive inverse control of linear and nonlinear systems using dynamic neural networks. IEEE Transactions Neural Network. 2003; 14 (2): 360-376. 
[49]. Jin, L., Gupta, M.M. Stable dynamic back propagation learning in recurrent neural networks. IEEE Transactions on Neural Network.1999; 10(6): 1321-1334. 
[50]. Reformat, M. Application of Genetic Algorithms in Control Design for Advanced Static VAR Compensator at ac/dc Interconnection. University of Manitoba Press, Canada. 1997.
[51].  Bandyopadhyay, S., Pal, S.K. Classification and learning using genetic algorithms: applications in bioinformatics and web intelligence. Springer Berlin, Heidelberg New York. 2007. 
[52]. Saemi M., Ahmadi M., Yazdian Varjani A. Design of neural networks using genetic algorithm for the permeability estimation of the reservoir, Journal of Petroleum Science and Engineering.2007; 59: 97-105.
[53]. Hegazy, T., Fazio, P., Moselhi, O. Developing practical Neural Network applications using Back-propagation. Microcornputers in Civil Engineehg, Blackwelf Publishers.1994; 9(2): 145-159.
[54]. Van-Rooij, A.J.F., Jain, L.C., Johnson, R.P. Neural Network Training Using Genetic Algorithms, World Scientific Publishing. Singapore. 1996.
[55]. Vonk, E., Jain, L.C., Johnson, R.P. Automatic Generation of Neural Network Architecture Using Evolutionary Computation. World Scientific Publishing Co. Pvt. Ltd, Singapore. 1997.
[56]. Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science.1901; 2(11): 559-572.
[57]. Hotelling, H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology. 1993; 24: 417–441. 
[58]. Gholami, R., Moradzadeh, A., Maleki, Sh., Saman, A., Hanach, J. Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs, Journal of Petroleum Science and Engineering.2014; 122: 643-656.
International Journal of Mining and Geo-Engineering
Volume 60, Issue 2
June 2026
Pages 149-159

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