Pierre,
Thank you very much for your response. I actually had not attempted
transforming the data yet — this was one of the questions I had in my
mind, namely, whether or not the transformation would do this. Regarding
the transformation, would a z-score or, perhaps, a Box-Cox, or some
other transformation be preferable? Or is it more an issue of what seems
to work best. I'm just getting started with geostatistical simulations,
so much of this is fairly new to me.
From what I can find in the GSTAT documentation, GSTAT only handles log
(natural logs) transforms internally, so I would have to transform the
data outside of GSTAT. This is not a problem, of course…
Regards,
Tom
Pierre Goovaerts wrote:
Hi Thomas,
I am assuming that you transform your data before conducting
your (sequential?) Gaussian simulation. In this case, the backtransform
would yield only positive values, assuming of course that like S-GeMS Gstat
asks the user to specify the minimum and maximum of the target histogram.
Regards,
Pierre
Pierre Goovaerts
Chief Scientist at BioMedware Inc.
Courtesy Associate Professor, University of Florida
President of PGeostat LLC
Office address:
516 North State Street
Ann Arbor, MI 48104
Voice: (734) 913-1098 (ext. 8)
Fax: (734) 913-2201
http://home.comcast.net/~goovaerts/
________________________________
From: [EMAIL PROTECTED] on behalf of Thomas Adams
Sent: Tue 7/25/2006 2:40 PM
To: [email protected]
Subject: AI-GEOSTATS: geostatistical simulation questions
List:
I am interested in doing some geostatistical simulations using GSTAT and
have some theoretical questions.
I am attempting to model hourly rainfall accumulations over a large
region, so there will almost always be zero rainfall somewhere. I can
generate random fields of precipitation, using both conditional and
unconditional simulations using GSTAT. However, I get negative values as
well as (mostly) positive values. The simulated fields otherwise look
very reasonable. The data I used (and must use) to estimate my variogram
has zero values where no rainfall occurs. What does this suggest to you?
I am using gaussian simulation.
I have seen some references to more exotic geostatistcal simulation
methods using bayesian or some other methods. Is this what I need?
From reading the literature, I have seen that some researchers have
successfully used indicator kriging and simulation. With GSTAT I can
successfully do simulations using 'method : is' rather than 'method :
gs', how using GSTAT do I model a continuous (non-binary) variable
using the GSTAT syntax?
Regards to all,
Tom
--
Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177
EMAIL: [EMAIL PROTECTED]
VOICE: 937-383-0528
FAX: 937-383-0033
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--
Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177
EMAIL: [EMAIL PROTECTED]
VOICE: 937-383-0528
FAX: 937-383-0033
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+ As a general service to list users, please remember to post a summary of any
useful responses to your questions.
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