AI-GEOSTATS: Lognormal kriging and Back Transformation
In response to some replies concerning my earlier query regarding the unfortunate property of back-transformation (BT) during lognormal kriging :
LESS DATA = HIGHER VARIANCE = HIGHER GRADE
I had some interesting replies, many purely academic, and some from a more practical point of view. The situation is such that this unfortunate property of the BT is illogical when it comes to practical mineral resource / reserve evaluation. From all these replies it appears that no-one is really sure what this means, or how to treat it. Thus, at the risk of being shot down in flames, I will put forward my own proposal (which I now implement):
We have seen that less data = high variance, and during BT this translates to high grades. My suggestion for treating and interpreting this information is to consider higher variance as less confidence in the estimate. Thus, an area might have a very high grade estimate, but alos a high variance because of less data. Estimation variance and kriging variance has been suggested by many authors (e.g. Annels) as a means of classifying resources/reserves, and I believe that this unfortunate property of BT allows for this implementation.
Your further thoughts?
Colin Badenhorst MSc
[Non-text portions of this message have been removed]
As I already pointed out
higher variance => higher lagrangian multiplier
so that some of the efect is cancelled out anyway.
We (Geostokos) use the following as a filter:
ygiagam (proven resource): kriging variance should be
less than original sample variance (total sill) less
within block variance
probable resource: kriging variance should be less
than twice the above and at least 4 samples should be
used in the estimation
These are fairly arbitrary but have proved sound over
the last 10-15 years.
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