Home Articles List Article Information
Iranian Journal of Earth Sciences
Journal Archive
Volume Volume 11 (2019)
Volume Volume 10 (2018)
Volume Volume 9 (2017)
Volume Volume 8 (2016)
Volume Volume 7 (2015)
Volume Volume 6 (2014)
Volume Volume 5 (2013)
Volume Volume 4 (2012)
Volume Volume 3 (2011)
Volume Volume 2 (2010)
Volume Volume 1 (2009)
Maleki, M., Madani, N. (2022). Multivariate geostatistical analysis: an application to ore body evaluation. Iranian Journal of Earth Sciences, 8(2), 173-184.
Mohammad Maleki; Nasser Madani. "Multivariate geostatistical analysis: an application to ore body evaluation". Iranian Journal of Earth Sciences, 8, 2, 2022, 173-184.
Maleki, M., Madani, N. (2022). 'Multivariate geostatistical analysis: an application to ore body evaluation', Iranian Journal of Earth Sciences, 8(2), pp. 173-184.
Maleki, M., Madani, N. Multivariate geostatistical analysis: an application to ore body evaluation. Iranian Journal of Earth Sciences, 2022; 8(2): 173-184.

Multivariate geostatistical analysis: an application to ore body evaluation

Article 7, Volume 8, Issue 2, Summer and Autumn 2016, Page 173-184  XML PDF (1.98 MB)
Document Type: Original Article
Authors
Mohammad Maleki1; Nasser Madani email 2
1Department of Mining Engineering, University of Chile, Santiago, Chile
2Young Researchers Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
It is now common in the mining industry to deal with several correlated attributes, which need to be jointly simulated in order to reproduce their correlations and assess the multivariate grade risk reasonably. Approaches to multivariate simulation which remove the correlation between attributes of interest prior to simulate and then re-impose the relationship afterward have been gaining popularity over the more common joint simulation methods because of their better accuracy and computational efficiency as the number of attribute being simulated increases. Principal component analysis (PCA) is one of these approaches. However, PCA suffers from some drawbacks such as the factors that are uncorrelated just for collocated locations. Minimum/ maximum autocorrelation factors (MAF) is a modification of PCA approach which the factors are uncorrelated for two lags. As an expectation, when linear co-regionalization model contains only two nested structures, the factors do not have any spatial correlations. The main aim of this research is to compare the results of MAF approach with some traditional approach for multivariate simulation (Co-simulation and independent simulation approach). To this end, two variables have been simulated with three different methods and are then compared together based on some yardsticks such as ability to reproduce the original correlation coefficient between two variables.
Keywords
Geostatistical simulation; co-simulation; MAF; Correlation
Supplementary Files
Statistics
Article View: 181
PDF Download: 716