Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation

N. L. Mai, O. Erten, E. Topal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings 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
PublisherCapital Publishing Company
Pages333-336
Number of pages4
ISBN (Electronic)9789381891254
Publication statusPublished - 2014
Externally publishedYes
Event16th International Association for Mathematical Geosciences - Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment: Challenges, Processes and Strategies, IAMG 2014 - New Delhi, India
Duration: Oct 17 2014Oct 20 2014

Other

Other16th International Association for Mathematical Geosciences - Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment: Challenges, Processes and Strategies, IAMG 2014
CountryIndia
CityNew Delhi
Period10/17/1410/20/14

Fingerprint

Conditional Simulation
iron ore
Autocorrelation
ore deposit
Iron
autocorrelation
Attribute
simulation
methodology
variogram
kriging
Variogram
Simulation
Curve
Methodology
Kriging
Threefolds
transform
fold
attribute

Keywords

  • Grade-tonnage curves
  • Iron ore deposit
  • Minimum/Maximum Autocorrelation factors
  • Multivariate simulation

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Earth and Planetary Sciences(all)

Cite this

Mai, N. L., Erten, O., & Topal, E. (2014). Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation. In 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 (pp. 333-336). Capital Publishing Company.

Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation. / Mai, N. L.; Erten, O.; Topal, E.

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. Capital Publishing Company, 2014. p. 333-336.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mai, NL, Erten, O & Topal, E 2014, Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation. in 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. Capital Publishing Company, pp. 333-336, 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, New Delhi, India, 10/17/14.
Mai NL, Erten O, Topal E. Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation. In 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. Capital Publishing Company. 2014. p. 333-336
Mai, N. L. ; Erten, O. ; Topal, E. / Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation. 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. Capital Publishing Company, 2014. pp. 333-336
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