Variogram modeling is usually a pre-requisite for kriging and/or stochastic simulation. It's not usally something that you'd want to "automate" in some sort of computer program. Selection of a model type/range/sill will usually be based on available sample points, or analagous samples of the same origin as the dataset one is looking at, complemented by a qualitative interpretation of the spatial model. I guess one can try to "program" this whole process from start to finish (exploratory data analysis/variogram modeling/kriging) but this is not at all recommended. Perhaps in some applications with abundant data, yes, but probably not in a geoscience setting.
You haven't told us what your applications are? Will you be mapping some geological variable? Interpolating 6 million pixels in an image file? Trying to gauge the distribution of a certain species of rare tropical flower? Syed ---- Original message ---- >Date: Mon, 8 Jul 2002 20:56:15 -0700 >From: "Eva Pierce" <[EMAIL PROTECTED]> >Subject: AI-GEOSTATS: Optimal Kriging parameters >To: <[EMAIL PROTECTED]> > >Hi. > >I want to use Ordinary Kriging on an arbitrary dataset of X,Y, and Z >values to estimate the Z values on a grid of arbitrary size/density. But >I don't know what length and scale parameters to choose for the >semivariogram. So I need to answer the following questions. I'm looking >for guidance and resources, not necessarily definitive answers. When >answering, please keep in mind that I'm a computer programmer, not a >statistician, by education and experience. :-) > >1. How does one measure the "goodness" or "badness" of a Kriging >estimate? E.g. when the bounds of the grid are fairly close to the >bounds of the dataset, I might expect the estimated surface of Z values >to have roughly the same number of "bumps" and "valleys" as the original >dataset (if discernible), and not too many flat regions. How do I >quantify such characteristics, and are there others I should be looking >for? >2. How does one arrive at the "optimal" length and scale parameters >for the semivariogram when doing ordinary Kriging, given these measures >of "goodness" and "badness"? (here's where my comp. sci education would >come in handy, if I knew the answer to #1) > >I'll send out a summary of answers that I receive. Thanks! >Eva > > > > -- * 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
