Jul The warning about kriging small blocks is about "small" relative to the sampling density. For example, less than about one-third of the grid spacing.
The warning is the same as the one about 'point' kriging (mapping) The map is too smooth - or, at least, a lot smoother than the real surface would be. High value areas will be under-estimated and low value areas will be over-estimated. If your objective in kriging is to obtain general maps of an area with an idea of where the highs and lows are, then ordinary kriging is sufficient. The over- and under- estimations cancel out on average. In mining applications, where block kriging originated, most applications require a 'cutoff', where values below a certain value are not included in the 'plan'. In this case, mapping or estimating small blocks will result in an over-estimation of 'payable' ground and an under-estimation in average value. In pollution or environmental applications, the areas at risk will be under-estimated as will the true toxicity or risk factors. There are two major ways round this problem: (1) use a non-linear kriging approach such as disjunctive kriging or the multivariate gaussian. Ed Isaacs and Mohan Srivastava's book is th ebest reference for the latter. Rivoirard's book for DK. (2) simulation. There are lots of simulation methods around, which allow you to 'put back the roughness' and get an idea how bad the problem might be. GSLib is pretty good on this. Isobel http://geoecosse.bizland.com/course_brochure.htm If, as in mining, you wish to apply some sort ___________________________________________________________ALL-NEW Yahoo! Messenger - sooooo many all-new ways to express yourself http://uk.messenger.yahoo.com
* By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
