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Nonrenewable Resources

, Volume 7, Issue 3, pp 197–210 | Cite as

Three-dimensional distribution analysis of phosphorus content of limestone through a combination of geostatistics and artificial neural network

  • Katsuaki Koike
  • Bin Gu
  • Michito Ohmi
Article

Abstract

One of the factors that determines the suitability of limestone for industrial use and its commercial value is phosphorus (P) content, i.e., the weight percentage of phosphorus contained in small quantities of limestone. Because P content changes locally, geostatistical techniques including semivariogram, ordinary kriging, and conditional indicator sequential simulation were used in this study to identify the spatial correlation of P content and to estimate its three-dimensional distribution in an open-pit mine. The P content data at 43,000 points of five different bench levels were analyzed. It was found that the horizontal semivariograms produced by using the data at the same bench level show anisotropic behavior and are represented by the sum of two spherical models with different ranges and sills. The twelve vertical semivariograms were also constructed from P content in boring cores. After these semivariograms were classified into four types, a multilayered neural network was applied to clarify the horizontal distribution of each one. One of the twelve semivariograms was assigned to an arbitrary grid point in the study area by the criterion that its type is the same as the one estimated at the point and the borehole site producing the semivariogram is the nearest to the point. With this technique, ordinary kriging combined with the semivariogram of borehole data provided a proper estimation of P content in the depth direction.

Key words

Phosphorus content semivariogram ordinary kriging sequential indicator conditional simulation multilayered neural network 

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References

  1. Aarts, E. H. L., and Korst, J. H. M., 1990, Simulated annealing and Boltzmann machines: New York, J. Wiley, 267 p.Google Scholar
  2. Deutsch, C. V., and Journel, A. G., 1992, GSLIB, Geostatistical software library and user’s guide: Oxford, Oxford University Press, 340 p.Google Scholar
  3. Fujinuki, T., 1968, Geochemical study of limestone (1)—On the minor elements in the akasaka limestone: Bulletin of the Geological Survey of Japan, v. 19, no. 9, p. 603–628 (in Japanese with Engl. abs.).Google Scholar
  4. Gómez-Hernández, J. J., and Srivastava, R. M., 1990, ISIM3D: An ANSI-C three-dimensional multiple indicator conditional simulation program: Computers & Geosciences, v. 16, no. 4, p. 395–440.CrossRefGoogle Scholar
  5. Gu, B., Koike, K., and Ohmi, M., 1997, Distribution analysis of metalliferous vein using artificial neural network: Geoinformatics, v. 8, no. 1, p. 15–21.Google Scholar
  6. Journel, A. G., 1989, Fundamentals of geostatistics in five lessons: Short course in geology, Vol. 8: Washington, D.C., American Geophysical Union, 40 p.Google Scholar
  7. Journel, A. G., and Huijbregts, C. H., 1978, Mining geostatistics: New York, Academic Press, 600 p.Google Scholar
  8. Laine, E., 1996, Quality mapping of the Ryytimaa Dolomite in Western Finland. Mathematical Geology, v. 28, no. 4, p. 477–499.CrossRefGoogle Scholar
  9. Lippmann, R. P., 1987, An introduction to computing with neural nets. IEEE Trans. Acoust., Speech. Signal Processing, v. 4, no. 2, p. 4–22.Google Scholar
  10. Rumelhart, D. E., Hinton, G. E., and Williams, R. H., 1986, Learning representations by back propagating errors: Nature (London), v. 323, no. 9, p. 533–536.CrossRefGoogle Scholar
  11. Sano, H., and Kanmera, K., 1988, Paleogeographic reconstruction of accreted oceanic rocks, Akiyoshi, Southwest Japan: Geology, v. 16, p. 600–603CrossRefGoogle Scholar
  12. Taira, A., Tokuyama, H., and Soh, W., 1989, Accretion tectonics and evolution of japan,in Ben-Avraham, Z., ed., The evolution of the Pacific Ocean margin: Oxford. Oxford University Press, p. 100–123.Google Scholar
  13. Van der Meer, F., 1994, Sequential indicator conditional simulation and indicator kriging applied to discrimination of dolomitization in GER 63-channel imaging spectrometer data: Nonrenewable Resources, v. 3, no. 2, p. 146–164.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geology 1998

Authors and Affiliations

  1. 1.Faculty of EngineeringKumamoto UniversityKumamotoJapan
  2. 2.Graduate School of Science and TechnologyKumamoto UniversityKumamotoJapan

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