Assessing the 2D models of geo-technological variables in a block of a cuban lateritic ore body. 2nd part: Sampling grid density influence on variogram
Keywords:
lateritic ore body, sampling grid, variogram, anisotropyAbstract
Kriging is one of the most common used methods to model mining and metallurigical technological variables; such as crust thickness and the concentrations of the chemicals that are of interest for the metallurgical processes. Adequate implementation of the method greatly depends on determining the corresponding variogram describing the variability of each property as a function of distance and of geometric directions. This work evaluates the influence of the sampling grid density on the 2D variable variograms: thickness (L) and nickel (Ni), iron (Fe) and cobalt (Co) contents in a lateritic ore body block in Cuba.Downloads
References
BARNETT, R. & DEUTSCH, C. 2012: Practical Implementation of Non-linear Transforms for Modeling Geometallurgical Variables. En Geostatistics Oslo 2012. Springer Netherlands, p. 409-422.
BRONSHTEIN, I. N.; SEMENDYAYEV, K. A.; MUSIOL, G. & MÜHLIG, H. 2007: Handbook of Mathematics. Springer-Verlag, Berlin, 1 163 p.
CALDER, C. & CRESSIE, N. A. 2009: Kriging and variogram models. In: KITCHIN, R. & THRIFT, N. (Eds.). International Encyclopedia of Human Geography 1: 49-55. Oxford Elsevier. Consultado: 14 dic 2014. Disponible en: http://dx.doi.org/10.1016/B978-008044910-4.00461-2.
CRESSIE, N. 1991: Statistics for Spatial Data. John Wiley and Sons, New York, 900 p.
DAGBERT, M. 2014. Review of the Book Mineral Resource Estimation. By Mario Rossi and Clayton Deutsch, 332 p. Published by Springer in Early 2014. Mathematical Geosciences 46(8): 1 029-1 034.
DE IACO, S. & MAGGIO, S. 2011: Validation Techniques for Geological Patterns Simulations Based on Variogram and Multiple-Point Statistics. Mathematical Geosciences 43(4): 483-500.
DESASSIS, N. & RENARD, D. 2012: Automatic Variogram Modeling by Iterative Least Squares: Univariate and Multivariate Cases. Mathematical Geosciences 45(4): 453-470.
GEOVARIANCES. 2010: ISATIS Software. Beginner's Guide. Versión 9.0. Geovariances & Ecole des Mines de Paros, France.
GOLDEN SOFTWARE, INC, 2012: Surfer. Surface Mapping System. Versión 11.0.642. Golden, Colorado, USA.
ILYAS, A. & KOIKE, K. 2012: Geostatistical Modeling of Ore Grade Distribution from Geomorphic Characterization in a Laterite Nickel Deposit. Natural Resource Research 21(2): 177-191.
KITANIDIS, P. 1997: Introduction to Geostatistics-Applications in Hydrogeology. Cambridge University Press, New York, 249 p.
LEGRÁ-LOBAINA, A. A. 1999: Metodología para el pronóstico, planificación y control integral de la minería en yacimientos lateríticos. Tesis doctoral. Instituto Superior Minero Metalúrgico. 187 p.
LEGRÁ-LOBAINA, A. A.; GUILARTE-NOA, A. M. & GUEVARA-PÉREZ, T. 2015: Evaluación de modelos de variables geo-tecnológicas en un bloque de un yacimiento laterítico cubano: Primera parte: Kriging e Inverso de la Distancia. Minería y Geología 31(1): 29-51.
LI, S. & LU, W. 2010: Automatic Fit of the Variogram. In: Third International Conference on Information and Computing. Wuxi, China, June 2010. Volume 4: 129-132. ISBN: 978-1-4244-7082-2.
LY, S.; CHARLES, C. & DEGRE, A. 2011: Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences 15(7): 2 259-2 274.
MARTÍNEZ, A. & PÉREZ, N. 2006: ¿Cuál es el mejor método para estimar variables en yacimientos lateríticos de níquel y cobalto? Minería y Geología 22(3): 1-21.
MILLER, I.; FREUND, J. & JOHNSON, R. 2005: Probabilidades y Estadísticas para ingenieros. Volumen I y II de la cuarta edición. Prentice-Hall Hispanoamericana S. A., México, 624 p.
OLIVER, M. 2010: The Variogram and Kriging. In: Handbook of Applied Spatial Analysis. Springer Berlin Heidelberg, p. 319-352.
PESQUER, L.; CORTÉS, A. & PONS, X. 2011: Parallel ordinary kriging interpolation incorporating automatic variogram fitting. Computer and Geosciences 37(4): 464–473. Consultado: 16 nov 2014. Disponible en: http://www.sciencedirect.com/science/article/ pii/S0098300410003341.
QUADOR, J. Q. 2005: Estudios de estimación y simulación geoestadística para la caracterización de parámetros geólogo–industriales en el yacimiento laterítico Punta Gorda. Minería y Geología 21(4): 1-48.
ROMANO, E.; BALZANELLA, A. & VERDE, R. 2013: A Regionalization Method for Spatial Functional Data Based on Variogram Models: An Application on Environmental Data. In: Advanced in Theoretical and Applied Statistics (Part II), p. 99-108.
ZIMMERMAN, D. L. & ZIMMERMAN, M. B. 1991. A Comparison of Spatial Semivariogram Estimators and Corresponding Ordinary Kriging Predictors. Technometrics 33(1): 77-91.
Published
How to Cite
Issue
Section
- Authors retain copyright and guaranteeing the right magazine to be the first publication of the work as licensed under a Creative Commons Attribution-NonCommercial that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors may establish separate supplemental agreements for the exclusive distribution version of the work published in the journal (eg, place it in an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are allowed and recommended to disseminate their work through the Internet (e.g., in institutional telematic archives or on their websites) before and during the submission process, which can produce interesting exchanges and increase citations of the published work. (See The effect of open access)
