Soeren

 That works if your matrix is made up of covariance terms rather than
variogram terms. However you should use the variance of the error term
instead of the standard deviation.

 So  in your notation  A_ij = C_ij = D - Gamma_ij

 where Gamma_ij are the variogram values,  D is the sill of the variogram
(or larger than the largest variogram value if your variogram does not have
a sill) and at the diagonal you use C_ii+K_ii  where K_ii are the variance
error terms.

 This will not interpolate your data! It will filter the noise terms (which
you say that you know the variance of at each point)

Regards

Colin Daly



----- Original Message -----
From: "Soeren Nymand Lophaven" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, January 30, 2003 3:22 PM
Subject: AI-GEOSTATS: Observations with a known standard deviation


Dear list

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 ?? My idea was the following:

When finding the vector of weights (w) by solving the system of linear
equations A*w=b, I would exchange the zeros in the diagonal of the
A-matrix with the individual observation variances. Does this sound
reasonable ??


Best regards / Venlig hilsen

Søren Lophaven
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Informatics and Mathematical Modelling  |  Building 321, Room 011
Technical University of Denmark         |  2800 kgs. Lyngby, Denmark
E-mail: [EMAIL PROTECTED]                  |  http://www.imm.dtu.dk/~snl
Telephone: +45 45253419                 |
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