559Re: AI-GEOSTATS: Ore Reserves Classification
- Apr 2, 2002List Members,
The use of the kriging variance to categorise/classify Mineral (Ore) Resources and/or Ore Reserves is an old chestnut that periodically raises it's ugly head. The kriging variance is related, pure and simply, to the data configuration and has nothing to do with the sample grades/variables being used for interpolation. As an example say a grade was being interpolated into a block which has been sampled on each corner, regardless of what the individual sample grades are, the kriging variance for that block is going to be the same. Example: if all four samples have the same grade of (say) 2.35g/t Au you will get the same kriging variance as the case where the four samples grades are (say) 0.01, 102.9, 0.88 and 3.60 g/t Au. Naturally I would have more confidence in the interpolated grade in the former scenario than the latter; thus the use of the kriging variance to determine confidence (or classification) of an estimate is misleading.
One method of obtaining some feel for the possible error range would be to run a large number of grade simulations into the block, the variance of all simulated grades would give an indication of error - again in the example given above, the variance of the simulated grades using the former case would be much smaller than in the latter case.
Of course classification of Mineral (Ore) Resources and/or Ore Reserves needs to take into account a lot more items (as expounded by the JORC (1999) code) - than just some objective measure of estimation error, it needs to take into consideration, amongst other things, data quality - if you have poor quality data (eg biased/inaccurate), regardless of how good any statistical measure of the estimation error is, you will always have poor estimate.
JORC; 1999: Australasian code for reporting of mineral resources and ore reserves (the JORC Code). Prepared by the Joint Ore Reserves Committee of the Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia (JORC).
Hellman & Schofield Pty Ltd
p&f: +61 (0)7 3217 7355
----- Original Message -----
From: José Quintín Cuador Gil
Sent: Wednesday, March 27, 2002 4:27 AM
Subject: AI-GEOSTATS: Ore Reserves Classification
Dear list members
The Kriging variance has some uses. In mining, it can be used in the Ore Reserves Classification.
What is the opinion about this in the Geostatistical community?
It is possible to use the Kriging variance for ores reserves classification?, (Yes or No).
Thanks in advances for any opinion.
José Quintín Cuador Gil
Department of Informática
University of Pinar del Río
[Non-text portions of this message have been removed]
- << Previous post in topic Next post in topic >>