Søren Lophaven writes: > Some time ago I asked the following question: > > I am currently working with spatial interpolation of geophysical > data. Each observation is associated with an individual and known > standard deviation. How should this infomation be incorporated if > I want to use ordinary kriging for interpolation ??
In addition the the quite helpful responses you received, you might also want to take a look at the following paper: Craig A. Schultz et al. (1998), "Nonstationary Bayesian Kriging: A Predictive Technique to Generate Spatial Corrections for Seismic Detection, Location, and Identification", Bulletin of the Seismological Society of Amercia, Vol. 88, No. 5, pp. 1275-1288. These authors consider a predictor that must perform robustly in areas of extrapolation as well as interpolation, based on sparsely sampled data with differing degrees of measurement error. They work with the covariance matrices rather than semivariograms, and they apply a normalized weighting function to introduce a Bayesian damping factor into the kriging estimator. Wil Rivers -- * 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
