TY - GEN
T1 - Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation
AU - Mai, N. L.
AU - Erten, O.
AU - Topal, E.
N1 - Publisher Copyright:
© 2014, Capital Publishing Company.
PY - 2014
Y1 - 2014
N2 - Considering the multivariable deposits that consist of various attributes that are frequently spatially correlated, the uncertainty associated with the grade-tonnage curves is assessed through the joint conditional simulation techniques. This paper presents the joint simulation of five attributes using the Minimum/Maximum Autocorrelation Factors (MAF). The methodology for joint simulation is three-fold: (1) MAF is used to transform the attributes to non-correlated factors; (2) the variograms for each MAF are computed and modelled; (3) the independent MAFs are individually simulated and back-transformed to the original data space. The methodology is demonstrated in an iron ore deposit in Western Australia, where the attributes of an iron ore deposit are successfully decorrelated and simulated independently. The grade-tonnage curves for each realisation are plotted and compared with the generated one by the kriging estimate. The MAF approach proves itself to be an efficient method for joint simulation of multivariable deposits.
AB - Considering the multivariable deposits that consist of various attributes that are frequently spatially correlated, the uncertainty associated with the grade-tonnage curves is assessed through the joint conditional simulation techniques. This paper presents the joint simulation of five attributes using the Minimum/Maximum Autocorrelation Factors (MAF). The methodology for joint simulation is three-fold: (1) MAF is used to transform the attributes to non-correlated factors; (2) the variograms for each MAF are computed and modelled; (3) the independent MAFs are individually simulated and back-transformed to the original data space. The methodology is demonstrated in an iron ore deposit in Western Australia, where the attributes of an iron ore deposit are successfully decorrelated and simulated independently. The grade-tonnage curves for each realisation are plotted and compared with the generated one by the kriging estimate. The MAF approach proves itself to be an efficient method for joint simulation of multivariable deposits.
KW - Grade-tonnage curves
KW - Iron ore deposit
KW - Minimum/Maximum Autocorrelation factors
KW - Multivariate simulation
UR - http://www.scopus.com/inward/record.url?scp=84957968343&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84957968343&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84957968343
T3 - Proceedings of the 16th International Association for Mathematical Geosciences - Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment: Challenges, Processes and Strategies, IAMG 2014
SP - 333
EP - 336
BT - Proceedings of the 16th International Association for Mathematical Geosciences - Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment
A2 - Raju, N. Janardhana
PB - Capital Publishing Company
T2 - 16th International Association for Mathematical Geosciences - Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment: Challenges, Processes and Strategies, IAMG 2014
Y2 - 17 October 2014 through 20 October 2014
ER -