Loading ...
Sorry, an error occurred while loading the content.

380AI-GEOSTATS: Lognormal kriging and Back Transformation

Expand Messages
  • Colin Badenhorst
    Sep 17 9:12 AM

      In response to some replies concerning my earlier query regarding the unfortunate property of back-transformation (BT) during lognormal kriging :


      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]
    • Show all 2 messages in this topic