### 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 language | English |
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Title of host publication | 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 |

Publisher | Capital Publishing Company |

Pages | 333-336 |

Number of pages | 4 |

ISBN (Electronic) | 9789381891254 |

Publication status | Published - 2014 |

Externally published | Yes |

Event | 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 Duration: Oct 17 2014 → Oct 20 2014 |

### Other

Other | 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 |
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Country | India |

City | New Delhi |

Period | 10/17/14 → 10/20/14 |

### Fingerprint

### 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

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

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.

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

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: Challenges, Processes and Strategies, IAMG 2014

PB - Capital Publishing Company

ER -