Re: AI-GEOSTATS: Simulation and trends
Chris As have told you Isobel you can loos de main information that gave de SGS, that is the local variability. You most be careful of how mush spaced is your data, the SGS can be in occasions les accurate than the turning band (with large quantity of random bands) and other kriging methods as consequence of increasing of error associated to the sequential approach. For test the simulations you can simulate with few variants of the inner kriging methods of the SGS in know points extracted randomly from the data, if your software dont have implemented the option of Jackknife you can migrate de points to the closer nodes of a dense grid, and compare it mean with the true value. In any way the most accurate methods must be SGS with kriging with local mean and ordinary kriging (remember the advise of Isobel about the universal Kriging) I hop it help you King Regards Adrian Martínez Vargas ISMM, las Coloradas s/n Moa, Holguín, Cuba CP 83329http://www.geocities.com/adriangeologo/adrian.html - Original Message - From: Chris Lloyd To: ai-geostats Sent: Wednesday, August 06, 2003 12:27 PM Subject: AI-GEOSTATS: Simulation and trends Hello, I am currently using sequential Gaussian simulation (SGS) to generate microtopographic soil surfaces from sparse data. There are nearly 16000 observations, so Im using a small search neighbourhood (e.g., 16 observations). The mean of the variable Im concerned with (heights) increases systematically from the top to the bottom of the data set and R squared for a fitted first order polynomial is 0.885. An obvious choice is to detrend the data, use SGS based on simple kriging and then add the trend back. An alternative might be to use the fitted trend to define the locally-varying mean and apply SGS based on simple kriging with locally-varying means (rather than taking the mean of the residuals as the constant mean and applying standard simple kriging). I suspect that ordinary kriging (using a power model fitted to the raw variogram) would result in predictions as accurate as those obtained through detrending in some way, but given the trend is so obvious I dont want to ignore it. I am aware that there is a lot of relevant work in the literature about the application of these approaches in the context of kriging. However, I would be interested in details of any case studies that have dealt with large scale trends in a simulation context. I would also be interested in the views of list members about the approaches Ive mentioned or any others that may be appropriate. Many thanks in advance, Chris Lloyd
Re: AI-GEOSTATS: Simulation and trends
Chris We have always found that estimating the semi-variogram from the reiduals of a global trend was sufficient, provided care is taken to use the cross validation to avoid 'over fitting'. I guess this is also true for genuine first-stage UK as well! One of the free tutorials which is downloadable from the web, on the Wolfcamp data, illustrates this. Isobel http://ecosse.ontheweb.com/softwares Want to chat instantly with your online friends? Get the FREE Yahoo! Messenger http://uk.messenger.yahoo.com/ -- * 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
Re: AI-GEOSTATS: Simulation and trends
Adrian Thank you for the reminder of one of the strengths of Turning Bands. Certainly I have no argument with your points. However Chris' question was about how to include trend in SGS and that is what my answer is about. Isobel http://ecosse.ontheweb.com Want to chat instantly with your online friends? Get the FREE Yahoo! Messenger http://uk.messenger.yahoo.com/ -- * 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
Re: AI-GEOSTATS: Simulation and trends
Chris
Could I be incredibly obvious and suggest that, if you
use Universal Kriging, the trend is fitted and
simulated automatically with SGS. This is one of the
major advantages of SGS over approaches like Turning
Bands or Monte-Carlo -- if you can krige it, you can
simulate it.
There is a lot of evidence in the literature, dating
back to the early '80s that kriging residuals and
adding back the trend gives you pretty much the same
estimated surface as Universal Kriging. However, what
it doesn't do is give you the right standard error
since it doesn't allow for the trend fitting error. So
I would hazard a guess that simulations done this way
would underestimate the 'true' variability.
Isobel {Clark}
http://drisobelclark.ontheweb.com
PS: could I take this opportunity to remind anyone
interested that the IAMG 2003 is rapidly approaching.
If you haven't registered yet, sort yourself out at
http://www.iamg2003.com or follow the links from our
page at http://ecosse.ontheweb.com/whatsnew.htm
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