Conditional Bias of Geostatistical Simulation for Estimation of Recoverable Reserves

CIM Vancouver 2002
Jason McLennan, Clayton Deutsch,
Abstract Conditional bias is an infamous problem with estimation methods including kriging. Changing estimation parameters will mitigate, but not remove, conditional bias. The conditional bias of kriging is well understood; however, there is widespread confusion in the literature and among practicing geostatisticians regarding the conditional bias of geostatistical simulation. There is no conditional bias of simulation when the simulation results are used correctly. The correct use of simulation for recoverable reserves calculation and mine planning is to (1) generate multiple realizations conditional to all available data at a small scale, (2) linearly average all realizations to the chosen block size, and (3) calculate the probability of each block being ore and the grade of the block if it is ore. The “probability of ore” and the “grade if ore” are conditionally non-biased.
Keywords: Geostatistics, Kriging, Ore reserves, Simulation, Recoverable reserves
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