- Apr 3, 2002Dear list members,

I wish to make a remark on the discussion started on the kriging variance.

In my opinion the SD of the solution obtained by kriging is determined by

two effects. One is the (geometrical) distribution of the data, the other

one is the (a priori) standard deviation of the data. Richards remark is

right if the data has equal weights or standard deviations (SD). However if

the data is all weighted equally (by say 1.0) we will get a scaled 'SD'. If

you like studentized statistics, you can get a estimate for the SD of the

solution by multiplying with the a posteriori variance factor.

In our problems (cross validation of bathymetry data) we have a estimate for

the SD, which we use in the covariance function for the kriging problem. The

resulting SD of the solution will depend on the chosen a priori SD for the

samples.

Just my 2 cents,

David.

Quality Positioning Services bv, Huis ter Heideweg 16, 3705 LZ

Zeist, the Netherlands

Tel +31 (0)30 6925825, Fax +31 (0)30 6923663, Web http://www.qps.nl

<http://www.qps.nl/>

-----Original Message-----

From: Richard Hague [mailto:richardh@...]

Sent: woensdag 3 april 2002 6:48

To: ai-geostats@...

Subject: Re: AI-GEOSTATS: Ore Reserves Classification

List 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.

REFERENCES

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).

Richard Hague

Hellman & Schofield Pty Ltd

Brisbane Office

p&f: +61 (0)7 3217 7355

e: richardh@... <mailto:richardh@...>

w: http://www.hellscho.com.au <http://www.hellscho.com.au>

----- Original Message -----

From: José <mailto:cuador@...> Quintín Cuador Gil

To: ai-geostats@... <mailto:ai-geostats@...>

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

Cuba

< cuador@... <mailto:cuador@...> >

[Non-text portions of this message have been removed] - << Previous post in topic