So are you saying that the external drift variable in the matrix is just the magnitude of the the drift variable at that point?
ie : 3 point kriging s = drift variable [ k11 k12 k13 1 s1 ] [ l1 ] [ k01 ] [ k21 k22 k23 1 s2 ] [ l2 ] [ k02 ] [ k31 k32 k33 1 s3 ] [ l3 ] = [ k03 ] [ 1 1 1 0 0 ] [ u0 ] [ 1 ] [ s1 s2 s3 0 0 ] [ u1 ] [ s0 ] So in this 3 point Kriging case I just plug in the magnitude of my drift variable in for s0, s1, s2,and s3? And if you substituted a drift with a magnitude which was computed based upon 1st order polynomial you would get the same results from this matrix as you would by removing the 1st order polynomial trend and kriging the residuals and adding the 1st order polynomial trend back in? It is all starting to make sense now (if I'm correct in what I'm saying above). Thanks so much for your help! Mike -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On Behalf Of [EMAIL PROTECTED] Sent: Thursday, December 06, 2001 11:59 AM To: Michael Dennis Cc: AI-Geostats Mailing List Subject: Re: AI-GEOSTATS: Kriging with External Drift On Thursday, December 6, 2001, at 04:12 AM, Michael Dennis wrote: > > I don't think this is right but if you can explain to me how the Drift > is > actually applied in laymans terms it would be greatly appreciated. Also > when you do kriging with external drift do you have to model a > variogram or > can a reasonable one be computed automatically, if so how would you > compute > it? Kriging with an external drift is just an extension of universal kriging. UK assumes that one knows the shape of the trend but not its magnitude (or coefficients). For example a linear drift could be modeled by Mean = a + bX + cY where X and Y are the coordinates of the data. And so on and so forth for higher order polynomial trends. In KED, the trend shape is not defined analytically; rather, it is assumed that it is defined explicitly at all locations based on some densely sampled secondary variable. However, such a secondary variable must be smoothly varying in space, and also it must be available at all locations of the primary data and the locations being estimated. As in UK, the magnitude of the trend is unimportant, it is the shape that we're interested in. An external drift that varies linearly with X and Y would be equivalent to UK with an analytical trend of the same order polynomial, i.e. 1. Regards, Syed Maersk Copenhagen -- * 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 -- * 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
