Estimation of Iron concentration by using a support vector machineand an artificial neural network - the case study of the Choghart deposit southeast of Yazd, Yazd, Iran

Document Type: Research Paper


1 Department of Mining and Metallurgy Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

2 Department of mining, Imam Khomeini International Qazvin University, Qazvin, Iran


Estimation of the metal value in the metallic deposits is one of the important factors to evaluate the deposits in exploration studies and
mineral processing. Therefore, one accurate estimator is essential to obtain a fine insight into the accumulation of the ore body. There
are geostatistical methods for the estimation of the concentration of iron which performance of these models is complexity of analysis.
The support vector machine (SVM) is by far one of the most robust artificial intelligence techniques used successfully for predictions
and estimations of deposits because of its ability to generalize. Keeping this is view, the aim of this article is to use the SVM and back
propagation neural networks (BPNN) to estimate the concentration of the iron element in the Choghartdeposit, in Iran. Comparing the
obtained results with those of the validation process demonstrates that the SVM method is faster than the BPNN method and is more
precise for the estimation of the iron concentration in the Choghartmine. The results of this study show that artificial intelligence–
based models can evaluate the iron concentration with an acceptable accuracy.


Article Title [Persian]

تخمین غلظت آهن با استفاده از ماشین برداری پشتیبان و شبکه عصبی مصنوعی- مطالعه موردی، معدن آهن چغارت شمال شرقی یزد، یزد، ایران

Authors [Persian]

  • شاهو ملکی 1
  • حمیدرضا رمضی 2
  • سیروان مرادی 2
1 دانشکده معدن و متالوژی، دانشگاه صنعتی امیرکبیر، تهران، ایران
2 دانشکده معدن، دانشگاه بین المللی امام خمینی قزوین، قزوین، ایران.
Abstract [Persian]

تعیین مقدار ذخایر فلزی یکی از موضوعات بسیار مهم در مطالعات ارزیابی ذخیره برای اکتشاف مواد معدنی دارای صرفه اقتصادی می‌باشد. بنابراین، برای درک مناسب انباشتگی و پیچیدگی‌های مواد معدنی وجود یک تخمین‌گر مناسب و دقیق ضروری به نظر می‌رسد. روش‌های زمین آماری یکی از متداول‌ترین روش‌هایی است که معمولاً برای تخمین و مدل سازی ذخایر فلزی مانند آهن بکار می‌روند که مقادیر و مدل‌های حاصل از آن، دارای پیچیدگی‌ها و مشکلات بسیار زیادی می‌باشد. امروزه با پیدایش روش‌های کاربردی هوش مصنوعی مانند ماشین برداری پشتیبان با توجه به قابلیت تعمیم بالا آن و سادگی در پیش‌بینی و تخمین مدل‌‌سازی می‌توان به عنوان یکی از بهترین و موفق‌ترین روش‌های هوش مصنوعی در تخمین ذخایر معدنی فلزی بکار گرفته شود. ازینرو، در این تحقیق از روش ماشین برداری پشتیبان و شبکه عصبی برگشتی برای تخمین ذخیره آهن در معدن چغارت استفاده گردیده است. مقایسه نتایج بدست آمده برای تعیین غلظت این ذخیره نشان می‌دهد که ماشین برداری پشتیبان نسبت به شبکه عصبی برگشتی سریع‌تر و دقیق‌تر می‌باشد و همچنین مدل‌سازی‌های حاصل از این روش می‌تواند برای ارزیابی و تخمین غلظت ذخایر آهن با خصوصیات مشابه، با دقت قابل قبولی بکار گرفته شود.

Keywords [Persian]

  • ذخیره آهن
  • ذخایر معدنی
  • ماشین برداری پشتیبان
  • شبکه عصبی برگشتی
Al-Anazi, A.F., Gates, I.D., 2010. Support vector regression for porosity prediction in a heterogeneous reservoir: A
comparative study, Computers & Geosciences, 36: 1494-1503.
Amini, H., Gholami, R., Monjezi, M., Torabi, R., Zadhesh, J., 2011. Evaluation of flyrock phenomenon due to blasting
operation by support vector machine. Neural Comput & Applic, 11: 631-635.
Badel, M., Angorani, S., Shariat Panahi, M., 2010.The application of median indicator kriging and neural network in
modeling mixed population in an iron ore deposit. Computers & Geosciences, 37: 530-540.
Behzad, M., Asghari, K., Morteza, E., Palhang, M., 2009.Generalization performance of support vector machines and
neural networks in run off modeling. Elsevier, Expert Systems with Applications, 36: 7624-7629.
Boser, B.E., 1992. A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on
computational learning theory, Pittsburgh, 5: 144-152.
Burnett, C., 1995. Application of neural networks to mineral reserve estimation. Ph.D. Dissertation, Department of
Mineral Resources Engineering, University of Nottingham, Nottingham, 254 pp.
Chih-Hung, W., Gwo-Hshiung, T., Rong-Ho, L., 2009.A Novel hybrid genetic algorithm for kernel function and
parameter optimization in support vector regression. Expert Systems with Applications, 36: 4725-4735.
Clarici, E., Owen, D., Durucan, S., Ravencroft, P., 1993.Recoverable reserve estimation using a neural network. In:
Proceedings of the 24th International Symposium on the Application of Computers and Operations Research in the
Minerals Industries (APCOM), Montreal, Quebec. 145-152
Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn 20(3): 273-297.
Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines (and other kernel-based learning
methods). Cambridge University Press, UK.
Dibike, Y.B., Velickov, S., Solomatine, D., Abbott, M.B., 2001. Model Induction with support vector machines:
Introduction and Application. Journal of Computing in Civil Engineering 15(3): 208-216.
Fahlman, S.E., 1988. Faster-learning variations on back-propagation: An empirical study. Connectionist Models Summer
School, Sejnowski, T.J., Hinton, G.E., Touretzky, D.S. (Eds.), San Mateo, CA: Morgan Kaufmann.
Govett, G.J.S., 1983. Statistical Data Analysis in Geochemical Prospecting.Handbook of Exploration Geochemistry,
Hagan M.T., Menhaj, M., 1994. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural
Networks 5(6), 989-993.
Han, D., Cluckie, I., 2004. Support vector machines identification for runoff modeling. In Liong, S.Y., Phoon, K.K.,
Babovic, V. (Eds.), Proceedings of the sixth international conference on hydroinformatics 21-24.
Haykin, S., 1999.Neural Networks. Upper Saddle River, NJ: Prentice-Hall.
Howarth, R.J., Earle, S.A.M., 1979. Application of a Generalized Power Transformation to Geochemical Data.
Mathematical Geology 11(1): 45-58.
John, C.P., 1998. Sequential Minimal Optimization: a Fast Algorithm for Training Support Vector Machines. MSRTR:
Microsoft Research.
Kapageridis, I., 2005. Input space configuration effects in neural network-based grade estimation. Computers &
Geosciences, 31: 704-717.
Kapageridis, I., Denby, B., 1998. Ore grade estimation with modular neural networkSystems-a
casestudy.In:Panagiotou,G.(Ed.),ProceedingsoftheFirstInternational Conference on Information Technology in the
Minerals Industry(MineIT ‘97). AA Balkema, Rotterdam CDROM Paper Code: KI16.
Khandelwal, M., Kankar, P.K., Harsha, S.P., 2010. Evaluation and prediction of blast induced ground vibration using
support vector machine. Mining Science and Technology, 20: 64-70.
Kolen, J.F., 2001. A Field Guide to Dynamical Recurrent Networks. New York: IEEE Press.
210 Maleki et al. Geopersia, 4 (2), 2014
Lia, Q., Licheng, J., Yingjuan, H., 2007. Adaptive simplification of solution for support vector machine, Pattern
Recognition, 40: 972 -980.
Liang J., Gupta, M.M., 1999. Stable dynamic backpropagation learning in recurrent neural networks. IEEE Trans. Neural
Netw 10 (6): 1321-1334.
Liu, H., Wen, S., Li, W., Xu, C., Hu, C., 2009. Study on Identification of Oil/Gas and Water Zones in Geological Logging
Base on Support-Vector Machine. Fuzzy Information and Engineering 2 (62) 849-857.
Liu, H., Yao, X., Zhang, R., Liu, M., Hu, Z., Fan, B., 2006. The accurate QSPR models to predict the bioconcentration
factors of nonionic organic compounds based on the heuristic method and support vector machine. Chemosphere 63:
Maleki, Sh., Moradzadeh, A., Ghavami, R., Sadeghzadeh, F., 2013. A Robust Methodology for Prediction of DT Wireline
Log. Iranian Journal of Earth Sciences, 5: 33-40.
Maleki, Sh., Moradzadeh, A., GhavamiRiabi, R., Sadaghzadeh, F., 2014a.Comparison of Several Different Methods of in
situ stress determination, International JournalofRockMechanics&MiningSciences, 71: 395-404.
Maleki, Sh., Moradzadeh, A., GhavamiRiabi, R., Gholami, R., Sadaghzadeh, F., 2014b. Prediction of shear wave velocity
using empirical correlations and artificial intelligence methods, NRIAG Journal of Astronomy and Geophysics, In
Press, Corrected Proof, Available online 29 July 2014.
Martinez-Ramon, M., Cristodoulou, Ch., 2006. Support Vector Machines for Antenna Array Processing and
Electromagnetic. Universidad Carlos III de Madrid, Spain, Morgan & Claypool, USA.
MATLAB Neural Network Toolbox [Online]. Available:; products; neural net.
Moor, F., Modabberi,S., 2003. Origin of Choghart iron oxide deposit, Bafq mining district, CentralIran: new isotopic and
geochemical evidence, Journal of science, Islamic Republic of Iran, 14(3): 259-269.
Narendra K., Parthasarathy, K., 1990. Identification and control of dynamical systems using neural networks. IEEE Trans.
Neural Netw, 1: 4-27.
Peng, K.L., Wu, C.H., Goo, Y.J., 2004. The development of a new statistical technique for relating financial information
to stock market returns. International Journal of Management 21(4): 492-505.
Plett, G.L., 2003. Adaptive inverse control of linear and nonlinear systems using dynamic neural networks. IEEE Trans.
Neural Netw 14 (3): 360-376.
Quang-Anh, T., Xing, L., Haixin, D., 2005.Efficient performance estimate for one-class support vector machine. Pattern
Recognition Letters, 26: 1174-1182.
Rumelhart, D.E., Hinton, G.E., Wiliams, R.J., 1986. Learning representations by back-propagating errors. Nature 323,
Scholkopf, B., Smola, A.J., Muller, K.R., 1998. Nonlinear component analysis as a kernel eigenvalues problem, Neural
Computet, 10: 1299-1319.
Steinwart, I., 2008. Support Vector Machines. Los Alamos National Laboratory, information Sciences Group (CCS-3),
Walczack, B., Massart, D.L., 1996. The radial basis functions-partial least squares approach as a flexible non-linear
regression technique. Anal. Chimical Acta, 331: 177-185.
Wang, L., 2005. Support Vector Machines: Theory and Applications, Nanyang Technological University, School of
Electrical & Electronic Engineering, Springer Berlin Heidelberg New York.
Wang, W.J., Xu, Z.B., Lu, W.Z., Zhang, X.Y., 2003. Determination of the spread parameter in the Gaussian kernel for
classification and regression. Neurocomputing, 55: 643-663.
Wilamowski, B.M., 2007. Neural networks and fuzzy systems for nonlinear applications. Int. Conf. Intelligent
Engineering Systems, Budapest, Hungary, 1: 13-19.
Wilamowski, B.M., Cotton, N.J., Kaynak, O., Dundar, G., 2008. Computing gradient vector and Jacobian matrix in
arbitrarily connected neural networks. IEEE Trans. Ind. Electron 55(10): 3784-3790.
Wu, X., Zhou, Y., 1993.Reserve estimation using neural network techniques. Computers & Geosciences, 19: 567-575.