This mailing list is very useful espacially for
beginners who have something to ask.
Thanks all contributors
Ercan Yesilirmak
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Pierre,
Thanks for the comments. It's my first time to use Gaussian simulation to do
something possibly useful, and I have also found the calculation quite slow
even though the speed of my computer is not so bad. I'm using Idrisi 32
(with GStat), and the grid is about 500*500.
What I worry
Dear Gregoire Dubois,
Your mailing list has infact gave me an opportunity to learn more about the
experts adn the specialists in the field of Geostatistics. It made me
confident of my work as I can see that the replies are many a time coincide
with my thinking. I had the great opportunity of
I am curious about the use of 100 realizations to generate a probability
map. is this a standard approach? if so, is a small p-value (such as
.05) used? if so, it would seem like 100 iterations might be a smallish
sample size for distinguishing, say, .05 (ie 5 outcomes out of 100) from,
say,
Chaosheng, I agree with Pierre that if your only goal is to generate a
probability map, then IK is faster and more straightforward than simulation
and that MG kriging will give the same results, faster, than MG simulation.
However, we have found a couple of practical reasons where it may be
My tuppence worth.
The major advantages of simulation as a risk
assessment tool lie in the cases where you are trying
to derive some conclusion from the data rather than
just look at the values themselves.
For example, see Bill and my papers at Battelle
Conference 1987 or the paper at the
From: McKenna, Sean A [EMAIL PROTECTED]
1) When trying to explain the concepts of spatial variability and
uncertainty, we have found that showing example realizations of what the
possible distribution of contaminants could look like provides the groups
involved to get a more intuitive
