Hello everyone, Here is a summary of my question about information on lognormal distributions in mining.
> Hello, > > I was wondering if someone could give me recommendations on > 'Lognormal Distributions: Theory and Applications' Edwin L. Crow > for one interested in theoretical and practical aspects of nonlinear > (lognormal) geostatistics. > > Regards Digby Millikan B.Eng > Jeff Myers wrote: I can't help on the reference, but I would offer a friendly reminder that the lognormal sample distribution could be a result of incorrect sampling and subsampling procedures. If the sample support (mass/volume) at either the sample or subsample (laboratory) level is too small, chances are good that your distribution will look lognormal. Applying Pierre Gy's sampling theory may help normalize the distribution. In my training courses, attendees sample a material with different-sized sampling devices. Those with smaller devices produce highly skewed histograms whereas those with larger devices get normal-looking histograms. If you'd like, I can send you a paper that describes an actual application of this sample material to the design of a sampling program for explosives in soils at the Pueblo Chemical Depot (Colorado), where particulate materials (soils) were being sampled. Both sample support and the distributional assumption were key issues. The paper also contains histograms showing the distributions obtained from different sample supports. Best Regards, Jeff Myers I've attached a copy of the paper for you. Enjoy. FYI, I've also attached a description of the training. I started life as a mining engineer/geostatistician. I learned in western US gold mines that lab results were highly speculative due to the fundamental sampling error. If your data aren't representative of the orebody, you are trying to model an illusion. As I write to the ai-geostats list periodically, you can't contour yourself out of something you sampled yourself into. If your sample data don't reflect reality, even your statistician doesn't know for sure. Krige's relationship tells us that we have a continuum from point samples to the whole deposit. Point samples have the highest variability (related to Gy's theory). The deposit has no variance (it is what it is). Sub-deposit supports (blocks, bulk samples, etc.) have variability which, at some supports, follow normal distributions. The problem is that these support volumes may not be practical for sampling. However, it is typical for precious metals deposits to take samples that have inappropriately small support volumes. Thus, it is usually possible to improve your estimation by adjusting the sampling approach to reduce the sampling error as much as is feasible. Ironically, most people don't do it..... Jeff -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
