Muna,
If the 'no search' algorithm means that all the 90 data are being used then this result (sometimes called kriging with a unique neighbourhood) always gives smoother results than using a local neighbourhood. The reason is that in local kriging you will krige a part of your field using some neighbourhood set but other parts of the field will use different neighbours.You tend to see discontinuity as you go from one sector to the next (as one neighbour point gets replaced by another). This effect is more noticeable when you use small neighbourhood sets. It is reduced for larger sets as the points getting swapped out are the distant points and will usually have small weights (dependent on the variogram used - they may not be small for something like a gaussian variogram for example). Fianlly this effect is not there at all if you use all the data points in your neighbourhood. So with only 90 points- you should use a unique neighbourhood. If the program is well written then you will only ever have to solve the kriging equations once and will never have to do any more neighbour searchs -so it should run fairly fast. This can be made to run faster if there is a dual kriging option in your kriging program - although you will not get kriging variance values in this case. I can't be sure what 'no search' means - you will have to look it up in the software manual - but my guess is that it refers to unique neighbourhood if you are seeing smoother results. Colin Daly ----- Original Message ---- From: "Isobel Clark" <[EMAIL PROTECTED]> To: "Muna Mirghani" <[EMAIL PROTECTED]> Cc: <[EMAIL PROTECTED]> Sent: Friday, June 01, 2001 9:51 AM Subject: Re: AI-GEOSTATS: Search options of Kriging > > I get totally different distributions when I use a > > "Search" option in kriging compared to a "No-search" > > option. > What is a "no search" option? > > Minimum and maximum numbers of samples in kriging > depend heavily on what sort of semi-variogram you have > and how much nugget effect is present. In addition, > specifying a number rather than (say) a quadrant > search leaves you with the possibility still of very > uneven clustered sets of samples used in the > estimation. Kriging deals with clusters but reasonably > even coverage is still more efficient. > > Isobel Clark > > ____________________________________________________________ > Do You Yahoo!? > Get your free @yahoo.co.uk address at http://mail.yahoo.co.uk > or your free @yahoo.ie address at http://mail.yahoo.ie > > -- > * 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 This message may contain privileged and confidential information. If you are not the intended recipient, then please notify the sender of this error. Any disclosure, copying, distribution or misuse of this information is prohibited. Copyright Roxar limited. -- * 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
