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Geostatistical analysis of variability of silica dioxide content within limestone deposit

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Języki publikacji
EN
Abstrakty
EN
In the following paper, the geostatistical analysis of qualitative parameter within a limestone deposit was presented. The parameter was content of silica dioxide. Geostatistical analysis was carried out in order to identify variability of the parameter, what significantly influenced ore exploration. Sampling data was considered with regards to descriptive statistics; logarithmical character of parameter’s distribution was indicated. After logarithmical transformation omnidirectional semivariograms were calculated due to the fact that directional anisotropy was not proven. Few theoretical models were fitted to the semivariogram, further on they were verified by means of cross-validation method. Estimation results were obtained by lognormal ordinary kriging technique. They did not confirm that models classified during cross-validation as best fit are also most reliable during estimation. It is recommended to continue research on variability of parameters within the limestone deposit, including analysis conducted by indicator kriging technique. All stages of geostatistical analysis were carried out in Isatis software.
Czasopismo
Rocznik
Strony
181--193
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
  • Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
Bibliografia
  • 1. ASUERO A. G., SAYAGO A., GONZÁLEZ A. G., 2006. The Correlation Coefficient: An Overview, Critical, Reviews in Analytical Chemistry, 36:1, pp. 41-59.
  • 2. BURNOTTE E. AND LAUWERS A., 1998. DSS Central Europe, Geology and Mining Database.
  • 3. DEUTSCH C.V. AND JOURNEL A.G., 1998. Gslib geostatistical software library and user’s guide, 2nd Edition, Nowy Jork, Oxford University Press.
  • 4. GÓRSKA-ZABIELSKA M. AND STACH A., 2008. Spatial structure analysis and estimation of petrographical composition of Vistulian fluvioglacial deposits within glaciomarginal zone in the Odra lobe and the adjacent REGIONS, in: Przegląd Geograficzny, 80, 1, p. 75–104 (in Polish).
  • 5. JOURNEL A.G. AND HUIJBREGTS C.H.J., 1978. Mining Geostatistics, Academic Press Limited, San Diego.
  • 6. KOKESZ Z., 2010. Preparation of contour maps with use of ordinary kriging - advantages and limitations, Scientific Sheets of Mineral Resources and Energy Institute PAN, in: Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi i Energią PAN, Kraków (in Polish).
  • 7. LAWRENCE I., 1989. A concordance correlation coefficient to evaluate reproducibility, International Biometrics Society, in: Biometrics.
  • 8. MUCHA J., 1994. Geostatistical methods for resource evaluation, script of Department of Geology, Geophysics and Environmental Protection, Cathedral of Mining Geology, Kraków (in Polish).
  • 9. MUCHA J. AND WASILEWSKA M., 2005. Predicting of interpolation error of coal seam parameters in Upper Silesian Coal Basin (USCB), Monthly magazine of WUG 2005, nr 6, s. 23-24 (in Polish).
  • 10. NAMYSŁOWSKA–WILCZYŃSKA B., 2006. Geostatistics. Theory and applications, Oficyna Wydawnicza PWr, Wrocław (in Polish).
  • 11. PEROŃ J., 1984. Description of coal deposits’ paramaters with utilization of e.m.c. Part 1. Analysis of spatial variability, in: Technika Poszuk. Geol. 1984, nr 5-6, pp. 36–43 (in Polish).
  • 12. RODGERS J. L. AND NICEWANDER W. A., 1988. Thirteen ways to look at the correlation coefficient, in: The American Statistician.
  • 13. ŚWITOŃ J. M., 2014. Benefits and limitations of chosen computer aided geostatistical calculations, in: Interdyscyplinarne zagadnienia w górnictwie i geologii, Wrocław.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a7d153a8-1356-46b4-8274-eb5e61770845
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