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Hi Kevin, In the case of block estimation, I do not
tend to remove or cut-back outliers to a known value – these high grade samples
are after all a unique feature of the deposit, they must mean something right?
I do, however, tend to limit their influence on blocks being estimated.
Typically, I do this by ignoring
any sample above a threshold lying outside a nominated search ellipsoid. I am sure there are many other ways of
treating these samples, one is the 95% limit you mention, so it will be
interesting to see the various responses. Regards, Colin From: Kevin Lowe
(Office Park) [mailto:[EMAIL PROTECTED] Hi,
The
sampling is carried out by an automatic belt sampler prior to the ore being
milled. The samples are collected and stored in a bin until there is
approximately 1 ton of sample. The bin is then sent off to a lab which crushes
and splits the 1 ton bin sample to produce 8 separate samples which are then
assayed. Assuming there are no issues with the lab procedures, how should one
treat a very high value? For
example purposes, say the 8 samples returned grades (g/t) of 2.8, 4.6, 5.2,
4.5, 35.6, 3.6, 4.2, 4.7. The arithmetic mean for the eight is 8.15g/t but if
the one high grade is removed the arithmetic mean is 4.23g/t. Should I simply
exclude the high value or should I cut the value of the sample to some
arbitrary value (say the upper 95% confidence limit)? Although individual chip
samples collected from the orebody, for the purposes of evaluation, are highly
skewed, the samples from the bin approximate a normal distribution (excluding
the high value). I
look forward to any comments or perhaps direction to papers or web sites on
this topic. Many
Thanks Kevin
Lowe This e-mail message has been scanned for Viruses and Content and
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