Three-dimensional distribution analysis of phosphorus content of limestone through a combination of geostatistics and artificial neural network |
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| Authors: | Katsuaki Koike Bin Gu Michito Ohmi |
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| Institution: | (1) Faculty of Engineering, Kumamoto University, Kurokami 2-39-1, 860 Kumamoto, Japan;(2) Graduate School of Science and Technology, Kumamoto University, Kurokami 2-39-1, 860 Kumamoto, Japan |
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| 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. |
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| Keywords: | Phosphorus content semivariogram ordinary kriging sequential indicator conditional simulation multilayered neural network |
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