Hello,
I am currently using sequential Gaussian simulation (SGS)
to generate microtopographic soil surfaces from sparse data. There are nearly
16000 observations, so I’m
using a small search neighbourhood (e.g., 16 observations). The mean of the variable
I’m concerned with (heights) in
ter your advice I'll start with UK-based
SGS and go from there.
Chris
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Isobel Clark
Sent: 06 August 2003 21:17
To: Chris Lloyd
Cc: [EMAIL PROTECTED]
Subject: Re: AI-GEOSTATS: Simulation and trend
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
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
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 downloadab
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
