SciELO - Scientific Electronic Library Online

 
vol.108 número2Application of fuzzy set theory in the selection of underground mining methodDevelopment of luminescent diamond simulants for X-ray recovery índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Em processo de indexaçãoSimilares em Google

Compartilhar


Journal of the Southern African Institute of Mining and Metallurgy

versão On-line ISSN 2411-9717

J. S. Afr. Inst. Min. Metall. vol.108 no.2 Johannesburg Fev. 2008

 

TRANSACTION PAPER

 

An algorithm for quantifying regionalized ore grades

 

 

B. TutmezI; A.E. TercanII; U. KaymakIII

IInonu University, Department of Mining Engineering, Malatya, Turkey
IIHacettepe University, Department of Mining Engineering, Ankara, Turkey
IIIErasmus University Rotterdam, Econometric Institute, Rotterdam, The Netherlands

 

 


SYNOPSIS

We present a novel hybrid algorithm for quantifying the ore grade variability that has central importance in ore reserve estimation. The proposed algorithm has three stages: (1) fuzzy clustering, (2) similarity measure, and (3) grade estimation. The method first considers data clustering, and then uses the clustering information for quantifying the ore grades by means of a cumulative point semimadogram function. The method provides a measure of similarity and gives an indication of the regional heterogeneity. In addition, grade estimations can be obtained at different levels of similarity using a weighting function, which is the standard regional dependence function (SRDF).

Keywords: Grade, fuzzy clustering, similarity measure, point madogram, weighting function


 

 

“Full text available only in PDF format”

 

 

References

1. ŞEN, Z. Cumulative semivariogram model of regionalized variables. Mathematical Geology, vol. 21, 1989. pp. 891-903.         [ Links ]

2. JOURNEL, A.G. and HUIJBREGTS, Ch.J. Mining Geostatistics. Academic Press, 1981.         [ Links ]

3. ŞEN, Z. Point cumulative semivariogram for identification of heterogeneities in regional seismicity of Turkey. Mathematical Geology, vol. 30, no. 7, 1988. pp. 767-787.         [ Links ]

4. OZTOPAL, A. Artificial neural network approach to spatial estimation of wind velocity data. Energy Conversion and Management, vol. 47, no. 4, 2006. pp. 395-406.         [ Links ]

5. TUTMEZ, B. and HATIPOGLU, Z. Spatial estimation model of porosity. Computers & Geosciences, vol. 33, 2007. pp. 465-475.         [ Links ]

6. TUTMEZ, B., TERCAN, A.E., and KAYMAK, U. Fuzzy modeling for reserve estimation based on spatial variability. Mathematical Geology, vol. 39, no. 1, 2007. pp. 87-111.         [ Links ]

7. GOOVAERTS, P. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, 1997.         [ Links ]

8. BEZDEK, J.C., EHRLICH, R., and FULL, W. FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, vol. 10, nos. 2-3, 1984. pp. 191-203.         [ Links ]

9. BABUSKA, R. Fuzzy Modelling for Control. Kluwer Academic, 1998.         [ Links ]

10. SOUSA, J.M.C. and KAYMAK, U. Fuzzy Decision Making in Modelling and Control, World Scientific, Singapore, 2002.         [ Links ]

11. XIE, X.L. and BENI, G.A. A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell, vol. 13, no. 8, 1991. pp. 841-847.         [ Links ]

12. KAYMAK, U. and BABUSKA, R. Compatible cluster merging for fuzzy modelling. Proc. FUZZ-IEEE/IFES '95, Yokohama, 1995. pp. 897-904.         [ Links ]

13. KAYMAK, U. and SETNES, M. Extended fuzzy clustering algorithms. ERIM report series Research in Management, Erasmus University Rotterdam, ERS-2000-51-LIS. 2000.         [ Links ]

14. DEUTSCH, C.V. and JOURNEL, A.G. GSLIB: Geostatistical Software Library and User's Guide. 2nd edn. Oxford University Press, New York, 1998.         [ Links ]

15. TARAWNEH, A.D. and ŞAHIN, A.D. Regional wind energy assessment technique with applications, Energy Conversion & Management, vol. 44, no. 9, 2003. pp. 1563-1574.         [ Links ]

16. HÖPPNER, F., KLAWONN, F., KRUSE, R., and RUNKLER, T. Fuzzy Cluster Analysis, John Wiley & Sons, Chichester, 1999.         [ Links ]

17. WELLMER, F.-W. Statistical Evaluations in Exploration for Mineral Deposits, Springer, Heidelberg, 1998.         [ Links ]

 

 

Paper received Dec. 2006
Revised paper received Jan. 2008

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License