Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation
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 us...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/30839 |
| _version_ | 1848753205361508352 |
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| author | Mai, N. Erten, O. Topal, Erkan |
| author_facet | Mai, N. Erten, O. Topal, Erkan |
| author_sort | Mai, N. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:20:49Z |
| format | Conference Paper |
| id | curtin-20.500.11937-30839 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:20:49Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-308392017-01-30T13:21:47Z Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation Mai, N. Erten, O. Topal, Erkan 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. 2014 Conference Paper http://hdl.handle.net/20.500.11937/30839 restricted |
| spellingShingle | Mai, N. Erten, O. Topal, Erkan Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation |
| title | Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation |
| title_full | Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation |
| title_fullStr | Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation |
| title_full_unstemmed | Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation |
| title_short | Joint conditional simulation of an iron ore deposit using Minimum or Maximum Autocorrelation Factor transformation |
| title_sort | joint conditional simulation of an iron ore deposit using minimum or maximum autocorrelation factor transformation |
| url | http://hdl.handle.net/20.500.11937/30839 |