Application of an improved artificial neural network model for prediction of Cu and Au concentration in the porphyry copper-epithermal gold deposits, Case study: Masjed Daghi, NW Iran

Articles in Press

Document Type : Research Paper

Authors

Department of Mining Engineering, Faculty of Engineering, Urmia University, Urmia, Iran.

10.22059/ijmge.2024.376761.595167

Abstract

Modeling of geochemical data to predict elements is done with different methods. The proposed method in this research is the use of an intelligent model and pathfinder elements. In this study, drilling and sampling were done in two porphyry and epithermal mineralization of the Masjed Daghi porphyry copper deposit, and we used the data from the porphyry mineralization to predict copper and the data from the epithermal mineralization to predict gold. By using geochemical data and performing correlation and sensitivity analyses, copper and gold pathfinder elements (Pb, Zn, Ag, Mo, As) were determined. Then, using the data of pathfinder elements and an intelligent artificial neural network model, we predict the grade of gold and copper elements. The data of pathfinder elements were used as input and the grade of gold and copper elements were used as output of the model. In this research, the optimization of the artificial neural network is done using several optimization algorithms such as simulated annealing algorithm (SAA), firefly algorithm (FA), invasive weed optimization algorithm (IWO) and shuffled frog leaping algorithm (SFLA). Comparing the results showed that ANN-SAA (Combining ANN with SAA) performs better than other built models. This superiority was evident both in the porphyry and epithermal mineralization. R2 and MSE of ANN-SAA model for Cu prediction were 0.8275 and 0.0303 for training data, 0.7357 and 0.0371 for testing data respectively. Also, R2 and MSE of ANN-SAA model for Au prediction were 0.6713 and 0.0463 for training data, 0.7040 and 0.0333 for testing data respectively.

Keywords

Main Subjects

References

[1]. Hornik, K., M. Stinchcombe, and H. White, (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
[2]. Journel, A.G. and C.J. Huijbregts, (1978). Mining geostatistics, Academic press, Virginia, USA, 600 P.
[3]. Misra, D., et al., (2007). Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data. International Journal of Mining, Reclamation and Environment, 21(4), 282-294. https://doi.org/10.1080/17480930701259294
[4]. Rendu, J. (1979), Kriging, logarithmic Kriging, and conditional expectation: comparison of theory with actual results, in Proc. 16th APCOM Symposium. Tucson, Arizona, 199-212.
[5]. Agterberg, F. and G. Bonham-Carter. (1999), Logistic regression and weights of evidence modeling in mineral exploration, in Proceedings of the 28th International Symposium on Applications of Computer in the Mineral Industry (APCOM), Colorado School of Mines, Colorado, USA, 483-490.
[6]. Porwal, A., et al., (2010). Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geology Reviews, 38(3), 184-196. https://doi.org/10.1016/j.oregeorev.2010.04.002
[7]. Abedi, M., G.H. Norouzi, and N. Fathianpour, (2013). Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation, 21(1), 556-567. https://doi.org/10.1016/j.jag.2012.07.012
[8]. Abedi, M., S. Torabi, and G. Norouzi, (2013). 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, 54(2), 145-164. https://doi.org/10.4430/bgta0085
[9]. Afzal, P., et al., (2023). Mineral Resource Classification Using Geostatistical and Fractal Simulation in the Masjed Daghi Cu–Mo Porphyry Deposit, NW Iran. Minerals, 13(3), 370. https://doi.org/10.3390/min13030370
[10]. Daneshvar Saein, L., et al., (2022). Application of an improved zonality index model integrated with multivariate fractal analysis: epithermal gold deposits. Geopersia, 12(2), 379-394. https://doi.org/10.22059/geope.2022.339864.648652
[11]. Farhadi, S., et al., (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. Minerals, 12(6), 689. https://doi.org/10.3390/min12060689
[12]. Porwal, A., E.J.M. Carranza, and M. Hale, (2006). Bayesian network classifiers for mineral potential mapping. Computers and Geosciences, 32(1), 1-16.
[13]. Ziaii, M., A. Abedi, and M. Ziaei, (2009). Geochemical and mineralogical pattern recognition and modeling with a Bayesian approach to hydrothermal gold deposits. Applied Geochemistry, 24(6), 1142-1146. https://doi.org/10.1016/j.apgeochem.2009.02.006
[14]. Ghavami-Riabi, R., et al., (2010). U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. Journal of Geochemical exploration, 104(1-2), 27-33.
[15]. Mahdiyanfar, H. and M. Seyedrahimi-Niaraq, (2023). Integration of Fractal and Multivariate Principal Component Models for Separating Pb-Zn Mineral Contaminated Areas. Journal of Mining and Environment, 14(3), 1019-1035. 10.22044/jme.2023.13227.2424
[16]. Mahdiyanfar, H. and M. Seyedrahimi-Niaraq, (2024). Application of hybrid Wavelet-Fractal approach for denoising and spatial modeling of environmental pollution. Journal of Mining and Environment, -. 10.22044/jme.2024.14197.2643
[17]. Seyedrahimi-Niaraq, M., H. Mahdiyanfar, and A.R. Mokhtari, (2022). Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts. Journal of Geochemical Exploration, 234, 106924.
[18]. Brown, W.M., et al., (2000). Artificial neural networks: a new method for mineral prospectivity mapping. Australian journal of earth sciences, 47(4), 757-770.
[19]. Harris, D. and G. Pan, (1999). Mineral favorability mapping: a comparison of artificial neural networks, logistic regression, and discriminant analysis. Natural Resources Research, 8(2), 93-109. https://doi.org/10.1023/A:1021886501912
[20]. Harris, D., et al., (2003). A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Natural Resources Research, 12(4), 241-255. https://doi.org/10.1023/B:NARR.0000007804.27450.e8
[21]. Lee, S., et al., (2014). A case study for the integration of predictive mineral potential maps. Central European Journal of Geosciences, 6(3), 373-392.
[22]. Leite, E.P. and C.R. de Souza Filho, (2009). Artificial neural networks applied to mineral potential mapping for copper‐gold mineralizations in the Carajás Mineral Province, Brazil. Geophysical Prospecting, 57(6), 1049-1065. https://doi.org/10.1111/j.1365-2478.2008.00779.x
[23]. Leite, E.P. and C.R. de Souza Filho, (2009). Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil. Computers & Geosciences, 35(3), 675-687.
[24]. Oh, H.-J. and S. Lee, (2010). Application of artificial neural network for gold–silver deposits potential mapping: a case study of Korea. Natural resources research, 19(2), 103-124.
[25]. Rigol-Sanchez, J., M. Chica-Olmo, and F. Abarca-Hernandez, (2003). Artificial neural networks as a tool for mineral potential mapping with GIS. International Journal of Remote Sensing, 24(5), 1151-1156. https://doi.org/10.1080/0143116021000031791
[26]. Singer, D.A. and R. Kouda, (1996). Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan. Mathematical Geology, 28(8), 1017-1023. https://doi.org/10.1007/BF02068587
[27]. Skabar, A. (2003), Mineral potential mapping using feed-forward neural networks, in Neural Networks, 2003. Proceedings of the International Joint Conference on: IEEE, 1814-1819.
[28]. Skabar, A., (2007). Mineral potential mapping using Bayesian learning for multilayer perceptrons. Mathematical Geology, 39(5), 439-451. https://doi.org/10.1007/s11004-007-9106-8
[29]. Skabar, A.A., (2005). Mapping mineralization probabilities using multilayer perceptrons. Natural Resources Research, 14(2), 109-123. https://doi.org/10.1007/s11053-005-6955-z
[30]. Dutta, S., et al., (2010). Machine learning algorithms and their application to Ore Reserve estimation of sparse and imprecise data. Journal of Intelligent Learning Systems and Applications, 2(02), 86.
[31]. Samanta, B., S. Bandopadhyay, and R. Ganguli, (2002). Data segmentation and genetic algorithms for sparse data division in Nome placer gold grade estimation using neural network and geostatistics. Exploration and mining geology, 11(1-4), 69-76. https://doi.org/10.2113/11.1-4.69
[32]. Samanta, B., S. Bandopadhyay, and R. Ganguli, (2006). Comparative evaluation of neural network learning algorithms for ore grade estimation. Mathematical geology, 38(2), 175-197. https://doi.org/10.1007/s11004-005-9010-z
[33]. Samanta, B., et al., (2004). Sparse data division using data segmentation and Kohonen network for neural network and geostatistical ore grade modeling in Nome offshore placer deposit. Natural resources research, 13(3), 189-200. https://doi.org/10.1023/B:NARR.0000046920.95725.1b
[34]. Abedi, M., G.H. Norouzi, and A. Bahroudi, (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers and Geosciences, 46(1), 272-283.
[35]. Li, X., Y. Xie, and Q. Guo. (2010), A new intelligent prediction method for grade estimation, in 7th International Symposium on Neural Networks, Shanghai University, China: Springer, 507-515.
[36]. Li, X.l., et al., (2013). Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing, 118(1), 179-190.
[37]. Zuo, R. and E.J.M. Carranza, (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 37(12), 1967-1975.
[38]. Afzal, P., et al., (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia, 12(1), 191-199.
[39]. Brown, W., D. Groves, and T. Gedeon, (2003). Use of fuzzy membership input layers to combine subjective geological knowledge and empirical data in a neural network method for mineral-potential mapping. Natural Resources Research, 12(3), 183-200.
[40]. Porwal, A., E. Carranza, and M. Hale, (2004). A hybrid neuro-fuzzy model for mineral potential mapping. Mathematical Geology, 36(7), 803-826. https://doi.org/10.1023/B:MATG.0000041180.34176.65
[41]. Simpson, P.K., (1991). Artificial neural systems: foundations, paradigms, applications, and implementations, McGraw-Hill, Inc.209 P.
[42]. Toğan, V., (2012). Design of planar steel frames using teaching–learning based optimization. Engineering Structures, 34, 225-232.
[43]. Toğan, V., (2013). Design of pin jointed structures using teaching-learning based optimization. Structural Engineering and Mechanics, An Int'l Journal, 47(2), 209-225.
[44]. Uzlu, E., et al., (2014). Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm. Energy, 75, 295-303. https://doi.org/10.1016/j.energy.2014.07.078
[45]. Price, R.H. and S.J. Bauer, (1985), Analysis of the elastic and strength properties of Yucca Mountain tuff, Nevada, in 26th US Symposium on Rock Mechanics. United States. p. 89–96.
[46]. Hajihassani, M., et al., (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics, 80, 57-67.
[47]. Aghazadeh, M., et al., (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 reviews, 70, 385-406.
[48]. Hassanpour, S., (2013). The alteration, mineralogy and geochronology (SHRIMP U–Pb and 40Ar/39Ar) of copper-bearing Anjerd skarn, north of the Shayvar Mountain, NW Iran. International Journal of Earth Sciences, 102(3), 687-699.
[49]. Jamali, H., et al., (2010). Metallogeny and tectonic evolution of the Cenozoic Ahar–Arasbaran volcanic belt, northern Iran. International Geology Review, 52(4-6), 608-630.
[50]. Maghsoudi, A., et al., (2014). Porphyry Cu–Au mineralization in the Mirkuh Ali Mirza magmatic complex, NW Iran. Journal of Asian Earth Sciences, 79, 932-941. https://doi.org/10.1016/j.jseaes.2012.10.002
[51]. Imamalipour, A. and R. Mousavi, (2018). Vertical geochemical zonation in the Masjed Daghi porphyry copper-gold deposit, northwestern Iran: implications for exploration of blind mineral deposits. Geochemistry: Exploration, Environment, Analysis, 18(2), 120-131.
[52]. Ebrahimi, S., et al., (2017). Geology, mineralogy and ore fluid characteristics of the Masjed Daghi gold bearing veins system, NW Iran. Journal of Economic Geology, 9(2), 561-586. https://doi.org/10.22067/econg.v9i2.51493
[53]. Imamalipour, A., et al., (2011). Geological, Alteration and magnetic anomaly pattern of Masjeddaghi porphyry copper deposit (East of Julfa). Advanced Applied Geology, 1(2), 77-89.
[54]. Jorjani, E., S.C. Chelgani, and S. Mesroghli, (2008). Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel, 87(12), 2727-2734.
[55]. Monjezi, M. and H. Dehghani, (2008). Evaluation of effect of blasting pattern parameters on back break using neural networks. International Journal of Rock Mechanics and Mining Sciences, 45(8), 1446-1453.
[56]. Specht, D.F., (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568-576.
[57]. Acharya, C., et al., (2006). Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks. Ecological modelling, 190(1), 223-230.
[58]. Hagan, M.T., H.B. Demuth, and M.H. Beale, (1996). Neural network design, PWS Publishing Co, Boston London.
[59]. Basheer, I.A. and M. Hajmeer, (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of microbiological methods, 43(1), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
[60]. Assad, A. and K. Deep, (2018). A hybrid harmony search and simulated annealing algorithm for continuous optimization. Information Sciences, 450, 246-266.
[61]. Černý, V., (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of optimization theory and applications, 45(1), 41-51.
[62]. Kirkpatrick, S., C.D. Gelatt Jr, and M.P. Vecchi, (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
[63]. Metropolis, N., et al., (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
[64]. Xinchao, Z., (2011). Simulated annealing algorithm with adaptive neighborhood. Applied Soft Computing, 11(2), 1827-1836. https://doi.org/10.1016/j.asoc.2010.05.029
[65]. García-Martínez, C., M. Lozano, and F.J. Rodríguez-Díaz, (2012). A simulated annealing method based on a specialised evolutionary algorithm. Applied Soft Computing, 12(2), 573-588.
[66]. Fattahi, H. and H. Bazdar, (2017). Applying improved artificial neural network models to evaluate drilling rate index. Tunnelling and Underground Space Technology, 70, 114-124.
[67]. Ingber, L., (1993). Simulated annealing: Practice versus theory. Mathematical and computer modelling, 18(11), 29-57.
[68]. Yang, X.-S., (2010). Nature-inspired metaheuristic algorithms, Luniver press.
[69]. Mehrabian, A.R. and C. Lucas, (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366. https://doi.org/10.1016/j.ecoinf.2006.07.003
[70]. Zhou, Y., et al., (2015). A discrete invasive weed optimization algorithm for solving traveling salesman problem. Neurocomputing, 151, 1227-1236.
[71]. Maaroof, B.B., et al., (2022). Current studies and applications of shuffled frog leaping algorithm: a review. Archives of Computational Methods in Engineering, 29(5), 3459-3474.
[72]. Elbeltagi, E., T. Hegazy, and D. Grierson, (2007). A modified shuffled frog-leaping optimization algorithm: applications to project management. Structure and Infrastructure Engineering, 3(1), 53-60.
[73]. Chicco, D., M.J. Warrens, and G. Jurman, (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623.
 
International Journal of Mining and Geo-Engineering

Articles in Press, Accepted Manuscript
Available Online from 18 August 2024

Files

Share

How to cite

Statistics