Conditional joint simulation of random fields on block-support

Boucher, Alexandre. (2003). Conditional joint simulation of random fields on block-support PhD Thesis, School of Physical Sciences, The University of Queensland.

       
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Author Boucher, Alexandre.
Thesis Title Conditional joint simulation of random fields on block-support
School, Centre or Institute School of Physical Sciences
Institution The University of Queensland
Publication date 2003
Thesis type PhD Thesis
Total pages 161
Language eng
Subjects 02 Physical Sciences
Formatted abstract Realistic representations of multivariate natural phenomena such as many mineral deposits or petroleum reservoirs need to consider and reproduce the relationship between the variables. However, there is currently no method that can practically simulate multivariate real-size deposits. In this thesis, a method for the conditional simulation of a non-Gaussian vector random field on different support is presented. The method, derived from the direct block simulation algorithm, is shown to efficiently joint simulate large multivariate deposits directly on block support. Such a method permits the simulation of multivariate real-size deposit and is computationally very efficient.

The method, termed DBMAFSIM, is a multistage process. First, a vector random function is orthogonalised with minimum/maximum autocorrelation factors. Blocks are then simulated by performing a LU simulation on their discretised points, which are later back-rotated and averaged to yield the block value. The internal points are then discarded. Only the block value is stored in memory and is used for further conditioning, resulting in reduction of memory requirements and file storage. This method is successfully applied at Yandi Central 1, an iron ore deposit located in Western Australia. Five attributes are jointly simulated; iron, phosphorus, silica, alumina, and the loss on ignition.

The capacity to obtain multivariable stochastic representations of mineral deposits allows for the use and the development of more complete transfer functions, such as mine planning or production scheduling that requires all the significant attributes of a deposit. This would yield more reliable figures for highlighting risk associated with the exploitation of the resource.
Keyword Stochastic processes -- Mathematical models
Mines and mineral resources -- Mathematical models

 
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