AI-GEOSTATS: Optimal Kriging parameters

[Eva Pierce]

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. J   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? 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