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Abstract

Ore grade evaluation is a decisive task in mine planning operations. A reliable estimate can represent the deposit close to reality. A neural network (NN) model was used to develop a reliable estimate of ore grades of a study mine. The learning parameters for the NN model were selected by genetic algorithm. The optimum hidden layer nodes were selected based on minimum mean square error of a validation data set. The performance of the model was tested and compared with an ordinary kriging model. The comparative result revealed that the NN model performed better for all four attributes of the case study mine than the ordinary kriging model. The developed NN model incorporates lithological information as input parameters. Therefore, lithological maps of the deposit were generated using sequential indicator simulation. Ten different ore grade maps were generated for calculation of the uncertainty associated with the model. The grade tonnage curve along with uncertainty will help mine management by improving understanding about the deposit.

Keywords

ORE GRADE ESTIMATION NEURAL NETWORK GENETIC ALGORITHM ORDINARY KRIGING SEQUENTIAL INDICATOR SIMULATION LIMESTONE

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References

Arik

: Int. J. Min. Geol. Eng., 1990, 8, 319–331.

Armano

, Marchesi

and Murru

: Int. J. Inform. Sci., 2005, 170, (1), 3–33.

Bishop

C. M.

: 1998, ‘Neural networks for pattern recognition’, 256; 1998, Oxford, Clarendon Press.

Chatterjee

, Bhattacherjee

, Samanta

and Pal

S. K.

: Appl. GIS, 2006, 2, (1), 2.1–2.20.

David

R. L.

and Gregory

J. M.

: Water Resour. Res., 1999, 35, (1), 233 –241.

Dengiz

, Atiparmak

and Smith

A. E.

: IEEE Trans. Evol. Comput., 1997, 1, (3), 179–188. Author: Please cite this reference in the text.

Deutsch

C. V.

and Journel

A. G.

: ‘GSLIB: geostatistical software library and user's guide’, 2nd edn, 369; 1998, New York, Oxford University Press.

Goovaerts

: Math. Geol., 1996, 28, (7), 909–921.

Goovaerts

: ‘Geostatistics for natural resources evaluation’, 483; 1997, New York,Oxford University Press.

10.

Hagan

M.T.

, Demuth

H. B.

and Beale

: ‘Neural network design’, 19 chapters; 1995, Boston, MA, PWS Publishing Company.

11.

Haykins

: ‘Neural networks: a comprehensive foundation’, 2nd edn, 824; 1999, Upper Saddle River, NJ, Prentice Hall, New Jersey, pp.

12.

Holland

: ‘Adaptation in natural and artificial systems’; 1975, Ann Arbor, MI, The University of Michigan Press.

13.

Journel

A. G.

: Math. Geol., 1983, 15, (3), 445–468.

14.

Journel

A. G.

and Huijbregts

C. J.

: ‘Mining geostatistics’, 600; 1978, London, Academic Press.

15.

Koike

and Matsuda

: Nat. Resour. Res., 2003, 12, (3), 209–222.

16.

Koike

, Matsuda

, Suzuki

and Ohmi

: Nat. Resour. Res., 2002, 11, (2), 135–156.

17.

J.-P.

, Balazs

M. E.

and Parks

G. T.

: Eng. Optim., 2007, 39, (2), 147–161.

18.

Limas

M. S.

: ‘A MORE flexible neural network package’, 2006, available at: http://wiki.r-project.org/rwiki/doku.php?id=packages-s:cran:amore

19.

Merelo

J. J.

, Patlón

, Canas

, Prieto

and Morán

: Lecture Notes Comput. Sci., 1993, 686, 185–192.

20.

Petridis

, Kazarlis

, Papaikonomu

and Filelis

: Artif. Neural Networks, 1992, 2, 953–956.

21.

Plaut

, Nowlan

and Hinton

G. E.

: ‘Experiment on learning by back propagation’, Technical report CMU-CS-86-126, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 1986.

22.

Puntonet

C. G.

, Mansour

and Rojas

: Proc. Int. Conf. On ‘Artificial intelligence and soft computing’, Banff, Canada, July 2002, IASTED, 357–185.

23.

Samanta

, Bandopadhyay

and Ganguli

: Explor. Min. Geol., 2004a, 11, 69–76.

24.

Samanta

, Bandopadhyay

, Ganguli

and Dutta

: Nat. Resour. Res., 2004b, 13, (3), 189–200.

25.

Samanta

, Ganguli

and Bandopadhyay

: Trans. Inst. Min. Metall., 2005, 114, 129–139.

26.

Willighagen

: ‘R based genetic algorithm for binary and floating point chromosomes’, 2005, available at: http://rss.acs.unt.edu/Rdoc/library/genalg/html/00Index.html

27.

Yama

B. R.

and Lineberry

G. T.

: Min. Eng., 1999, 51, (2), 59–64.

28.

Zhang

, Ma

, Zhang

H.-J.

, Wang

B.-L.

and Chen

Y.-T.

: Proc. Int. Conf. on ‘Machine learning and cybernetics’, Baoding, Hebei Province, China, August 2003, Machine Learning Centre of the Hebei University, 1704–1707.

Genetic algorithm-based neural network learning parameter selection for ore grade evaluation of limestone deposit

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