Dear members of the list,
Below is the summary of all answers for the sparse
data problem (sorry for the delay, I was out of my
email for a while). Thank you all for interesting and
meaningful answers. I'll let you know about further
developments in the problem solution.
Happy Holydays and season greetings to everybody.
With much appreciation,
Gali
From: Isobel Clark [EMAIL PROTECTED] Add
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Subject: AI-GEOSTATS: Re: sparse data problem
To: Marcel_Vallée [EMAIL PROTECTED]
CC: [EMAIL PROTECTED]
Everybody (especially Gali!)
Just to put the base case in perspective. Many
half-billion dollar projects in Southern Africa have
been evaluated and floated on the stock exchange on
the basis of 5 or 6 holes. When a sample costs a
couple of million dollars to acquire, there is little
point in hoping for more.
We use an extremely well sampled case in our (free)
tutorial analyses. Look for the GASA data which has 27
samples. An embarrassement of riches in the mid-1980s,
I can assure you.
Isobel Clark
http://geoecosse.bizland.com/softwares
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Date: Fri, 05 Dec 2003 13:20:07 -0700
From: Donald E. Myers [EMAIL PROTECTED] Add
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To: Gali Sirkis [EMAIL PROTECTED]
Subject: Re: AI-GEOSTATS: sparse data problem
Gali
For you information
There is no difference between RBF and kriging, the
multiquadric is simply a particular choice of a
generalized covariance. In the geostatistics
literature, the RBF would be called dual kriging.
Donald E. Myers
http://www.u.arizona.edu/~donaldm
Date: Fri, 05 Dec 2003 14:11:42 -0500
From: Marcel_Vallée [EMAIL PROTECTED]
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To: Gali Sirkis [EMAIL PROTECTED],
[EMAIL PROTECTED]
Subject: Re: AI-GEOSTATS: sparse data problem
Gail
Sorry for not responding earlier to your request.
Your explanatory comment to Monica does not convince
me
as a exploration and mining geologist. I think her
comments are
wise and should be considered.
A 20x30 km area is a large one even when dealing with
very
uniform geology. Even in such conditions, different
properties
may be encountered, either as faults, vein or
fracturation
system, small intrusive bodies, mineral showings or
deposits,
pollution zones, etc.
Such a small sample set as you have [few (5-6)
original data
points + interpolated external data] that covering
whole study
area] does not allow you to really appraise the
validity and/or
the geological cause of this outlier. (There might
be a
sampling or assaying cause also). In such a case, it
should be
shown as an anomaly, not averaged out or kriged out.
Excluding sampling/analytical problems, the outlier
only has a
detectionvalue, meaning that the geology is not as
uniform as
expected and that additional geological observations
and sampling
in the vicinity is required to elucidate this problem.
We should view geostatistics as an ancillary tool to
understand a
two or three dimensional geological universe.
Whenever data ara
as sparse as in your exemple, kriged values should
not replace
and/or eliminate the potential meaning of sparse field
observations.
Sincerely
Marcel Vallée
Marcel Vallée Eng., Geo.
Géoconseil Marcel Vallée Inc.
706 Routhier St
Québec, Québec,
Canada G1X 3J9
Tel: (1) 418, 652, 3497
Email: [EMAIL PROTECTED]
Date: Thu, 04 Dec 2003 18:52:47 +0100
From: Umberto Fracassi [EMAIL PROTECTED] Add to
Address Book
To: Gali Sirkis [EMAIL PROTECTED]
Subject: Re: AI-GEOSTATS: sparse data problem
Hi Gali..
I got the info accessing the algorithm description in
Surfer 7.0 help.
That's the best reference I can offer:
CARLSON R.E. and FOLEY T.A., 1991, Radial Basis
Interpolation Methods on Track Data, Lawrence
Livermore National Laboratory, UCRL-JC-1074238
I found it launching a search on google...
Hope it helps!
Ciao,
Umberto
Date: Wed, 03 Dec 2003 13:47:37 -0500
From: Yetta Jager [EMAIL PROTECTED] Add to Address
Book
Subject: Re: AI-GEOSTATS: sparse data problem
To: Gali Sirkis [EMAIL PROTECTED]
Hi Gali,
I'd say 5 points isn't enough even for kriging with an
external drift
as
one would need more than that for a regression. If
you can get more
data,
say 25 points or so, that would be a feasible
solution. However, since
the
more common data is already interpolated, its not
clear why a kriging
model
would be substituted for it -- just use your
regression directly to
estimate the sparse variable.
Don't shoot the messenger!
Yetta
From: Monica Palaseanu-Lovejoy
[EMAIL PROTECTED] Add to
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To: Gali Sirkis [EMAIL PROTECTED]
Date: Wed, 3 Dec 2003 18:39:30 -
Subject: Re: AI-GEOSTATS: sparse data problem
Hi Gali,
Now i have even more questions ;-) If the dataset from
which you
have the interpolated data and your own data set
represent the
same phenomenon, then why you don't add your data to
the
original data
