Hello, I suspect that the problem resides in the use of 1 for setting up the unbiasedness constraints. In Gslib, the 1's are replaced by a constant that equals the largest value taken by the variogram (i.e. sill or pseudo-sill for unbounded models). While rescaling both the left and righ-hand sides of the unbiasedness constraint won't change the solution, it should make the matrix inversion much more stable.
Pierre On Sun, Aug 3, 2008 at 12:10 AM, M.J. Abedini <[EMAIL PROTECTED]> wrote: > > Dear All; > > While conducting ordinary kriging with anisotropic power model, some of the > entries in kriging coefficient matrix become unexpectly large creating > problem for its inversion. One possible remedy would be to limit OK with > moving neighborhood whereby the separation distance is under control. Is > there any other solution for this problem? > > Thanks > MJA > + > + To post a message to the list, send it to ai-geostats@jrc.it > + To unsubscribe, send email to majordomo@ jrc.it with no subject and > "unsubscribe ai-geostats" in the message body. DO NOT SEND > Subscribe/Unsubscribe requests to the list > + As a general service to list users, please remember to post a summary of > any useful responses to your questions. > + Support to the forum can be found at http://www.ai-geostats.org/ > -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 516 North State Street Ann Arbor, MI 48104 Voice: (734) 913-1098 (ext. 8) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/
