Document Type : Case Study


Department of Mining Engineering, Urmia University, Urmia, Iran


In the recent years, according to the difficulty of accurately measuring parameters and demarcation of earth sciences, attempts have been made to simplify the natural events for better investigation using geo-modelling. Modeling with intelligent methods is one of the new methods that has been considered in this field in the recent years. In this work, the intelligent method of adaptive neural-fuzzy inference system (ANFIS) is used to predict the elements of lead and zinc located in the Guard Kooh area, north of Yazd province in Iran. Descriptive statistics of data and correlation matrices of studied elements are obtained using the SPSS software. After the data is standardized, imported to the MATLAB software, and the lead and zinc elements are predicted using the ANFIS-SCM method. In this method, 70% of the data (175 samples) are set as the training data, and the rest (75 samples) are set as the test data, which are randomly selected. Using the obtained results, it is found that the grade of the estimated elements in the studied area has a good accuracy and a high correlation with the grade of the analyzed elements. As a result, the ANFIS-SCM intelligent method is a useful and accurate method for estimating the lead and zinc elements.


[1]. Bashkin, V. (2006). Modern biogeochemistry: Second Edition, Environmental Risk Assessment. Published by Springer, 3300 AA Dordrecht, The Netherlands, P.O. Box 17.
[2]. Bazdar, H. Fattahi, H., and Ghadim, F. (2015). Hybrid ANN with Invasive Weed Optimization Algorithm, a New Technique for Prediction of Gold and Silver in Zarshuran Gold Deposit, Iran. MSc Thesis, Arak University of Technology, Arak, Iran, 120 pp.
[3]. Skabar, A. (2003). Mineral potential mapping using feed-forward neural networks. Neural Networks, Proceedings of the International Joint Conference, 1814-1819.
[4]. Fung, CC. Iyer, V. Brown, W., and Wong, KW. (2005). Comparing the performance of different neural network architectures for the prediction of mineral prospectivity. Machine Learning and Cybernetics, Proceedings of 2005 International Conference, 394-398.
[5]. Leite, EP., and de Souza Filho, CR. (2009). Probabilistic neural networks were applied to the mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil. Computers and Geosciences, 35(3), 675-687.
[6]. Wang, G. Zhang, S. Yan, C. Song, Y. Sun, Y. Li, D., and Xu, F. (2011). Mineral potential targeting and resource assessment based on 3D geological modeling in Luanchuan region, China. Computers and Geosciences, 37(12), 1976-1988.
[7]. Twarakavi, NK. Misra, D., and Bandopadhyay, S. (2006). Prediction of arsenic in bedrock-derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research, 15(1), 15-26.
[8]. Abedi, M. Norouzi, GH., and Bahroudi, A. (2012). Support vector machine for multiclassification of mineral prospective areas. Computers & Geosciences, 83, 35-45.
[9]. Knox‐Robinson, C. (2000). Vectorial fuzzy logic: a novel technique for enhanced mineral prospectivity mapping, concerning the orogenic gold mineralization potential of the Kalgoorlie Terrane, Western Australia, Australian Journal of Earth Sciences, Vol. 47, No. 5, pp. 929-941.
[10]. Abedi, M. Torabi, S., and Norouzi, G. (2013). Application of the 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.
[11]. Tahmasebi, P. Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers and Geosciences, 42, 18-27.
[12]. Ziaii, M. Abedi, A., and Ziaii, M. (2007). Prediction of hidden ore bodies by new integrated computational model in marginal Lut region in east of Iran. Proc. Exploration, 957-961.
[13]. Renguang, Z. Jian, W., and Bojun, Y. (2021). Visualization and interpretation of geochemical exploration data using GIS and machine learning methods. Applied Geochemistry, volume 134.
[14]. Tahmooresi, M. Babaei, B., and Dehghan, S. (2021). Intelligent geochemical exploration modeling using multiclass support vector machine and integration with a continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran. Arabian Journal of Geosciences, volume 14, Article number: 1012.
[15]. Renguang, Z. Jian, W. Yihui, X., and Ziye, W. (2021). The processing methods of geochemical exploration data: past, present, and future. Applied Geochemistry, volume 132.
[16]. Guopeng, W. Guoxiong, Ch. Qiuming, Ch. Zhenjie, Z., and Jie, Y. (2021). Unsupervised Machine Learning for Lithological Mapping Using Geochemical Data in Covered Areas of Jining, China. Natural Resources Research, volume 30, pages1053–1068.
[17]. Bao-yi, Z. Man-yi, L. Wei-xia, L. Zheng-wen, J. Umair, K. Li-fang, W., and Fan-yun, W. (2021). Machine learning strategies for lithostratigraphic classification based on geochemical sampling data: A case study in area of Chahanwusu River, Qinghai Province, China. Journal of Central South University, volume 28, pages 1422–1447.
[18]. Ghadiyanloo, M.  Alimoradi, A., and Yousefi, M.  (2021). Recognizing Porphyry Copper Mineralization Targets in Chahar-Gonbad Area of Kerman Province Using Extreme Learning Intelligent Method. Journal of Mineral Resources Engineering (JMRE), volume 7, Issue 1 - Serial Number 23, Pages 39-61.
[19]. Nabavi, MH. (1976). An Introduction to Geology of Iran, Geological Survey of Iran.
[20]. Srinivasan, K. Fisher, D. (1995). Machine Learning Approaches to Estimating Software Development Effort. IEEE Transactions on Software Engineering, 21(2), 126–137.
[21]. Jang, JSR. Sun, CT. and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, 640p.
[22]. Kosko, B. (1992). Neural Networks and Fuzzy Systems. A Dynamical Approach to Machine Intelligence, Prentice-Hall, Englewood Cliffs, NJ, 449p.
[23]. Nava, P. and Taylor, J. (1996). The Optimization of Neural Network Performance through Incorporation of Fuzzy Theory. In: 11th Conference on Systems Engineering, 897-901.
[24]. MATLAB user’s guide. (2006). Fuzzy logic Toolbox, by math works Inc.
[25]. Chiu, SL. (1994). Fuzzy model identification based on cluster estimation. Journal of intelligent and Fuzzy Systems, 2(3), 267-278.
[26]. Gholami, R. Moradzadeh, A. Maleki, S. Amiri, S., and Hanachi, J. (2014). Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J Pet Sci Eng, 122, 643-56.
[27]. Jayalakshmi, T. and Santhakumaran, A. (2011). Statistical normalization and backpropagation for classification. Int J Comput Theory Eng, 3(1), 1793-8201.