Hello, An interesting one may be samples from stockpiles. In our case the stockpiles are quite significant tonnages. Either way, as a theoretical exercise we have so far thought of: what do the stockpiles samples actually represent (i.e. any sample bias) and how are the stockpile samples correlated. We have a time-line in the stockpile so the samples are perhaps not quite independent but it's hard to put a structure around the samples. Some sort of weighting is needed but what.
Regards Craig. -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Alexandre Boucher Sent: Sunday, 23 July 2006 07:53 To: Raimon Tolosana Cc: [email protected] Subject: Re: AI-GEOSTATS: KWBP Test Program Dear Raimon, A few steps back may be necessary, mining geostatistics uses random functions to model the grade of the deposit, not of the samples. The samples, among other sources of information, are there to help you in that modeling process. The spatial continuity of the orebody itself is not a function of the sampling, it is derived from a series of complex natural phenomena. Under sampling the deposit does not make the "real" underlying continuity disappear. In light of that, if your data fail to show any kind of continuity, you should ask yourself: is it because the grades of the deposit are truly "random", or is it because the sampling failed to capture that continuity for a variety of reasons. If you decide to go with the assumption of spatial independence, you then decide that grades are uncorrelated in space. This may not be a safe decision, and may be very consequential for the development of your operation. By assuming a pure nugget effect, you will be expecting constant cash flow from your operation, (every block has the same expectation, all would be above the cutoff) and will be designing a pretty simple mining schedule (there is no rich or barren zone). But then, if your orebody had a spatial continuity, you will have varying cashflow and very un-optimal mining design, that may well end up transforming a profitable operation into the red. I do not say that such scenario does not exist, I just put it in context. The assumption of spatial independence is pretty consequential and should not be made too easily. Energy should be invested on understanding why, from a geological and metallogenical perspective, it is spatially uncorrelated ( e.g. folding, faulting, etc... ). Also recall that variograms only capture continuity along a line, the continuity of curvilinear features are not easily modeled (unfolding may be helpful here). I would anyway suggest to perform a sensitivity analysis on the spatial correlation and see how the economics hold based on your chosen mining design. Regards, Alex Boucher At 10:30 AM 7/22/2006, you wrote: >Dear list, > >this situation posed by Mr.Merks, in which spatial dependence is not >strong enough as to be useful for geostatiscs, might be rather common. >I'd like to ask to the list, what kind of estimation of reserves should >be done in this case? In the absence of spatial dependence, classical >statistics should apply: therefore, shall we estimate the mean value of >ore content in the "deposit" by the arithmetic mean of the samples? And >attach an error to it, in the fashion of the standard error of the mean >(something like the variance of the sample divided by number of data >used)? Or did I grossly misunderstand something in the discussion, with >so much bogus-hocus-pocus and 5-line sentences? > >thanks for the patience >Raimon Tolosana > >En/na JW ha escrit: >> >>Hello Readers, >> >> >> >>More talk and not test. I want to know what the KWBP methodology does >>with the Bre-X data. Is that too much to ask? >> >> >> >>Kind regards, >> >>Jan W Merks > >+ >+ To post a message to the list, send it to [email protected] To >+ unsubscribe, send email to majordomo@ jrc.it with no subject and >"unsubscribe ai-geostats" in the message body. 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