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]
=
Gali Sirkis wrote:
Hi Monica,
thanks for quick reply. The interpolated data is a
different data set with is by its nature (speaking
about geological properties) should be correlated with
the sparse one.
This is a geological data over not huge area - around
20x30 kilometers. It should have at least some spatial
correlation. The variogram is not of striking beauty
:) but it is not a pure nugget effect, though.
The only other way meaningfully interpolate between
those sparse points, it seems to use the simple linear
regression between those two datasets.
The literature about kriging/interpolating for very
sparse data would definitely help, if anybody know
about, please let know.
Thanks,
Gali
--- Monica Palaseanu-Lovejoy
[EMAIL PROTECTED] wrote:
Hi,
I am not sure i understood correctly your question.
Fist of all, do
the interpolated data have come from your sparse
data
interpolation? What method of interpolation did you
use in this
case?
After Burrough and McDonnel, 2000, you need at least
50 points to
have reliable results through kriging. Certainly you
can do it on less
data, but until now i never saw a study considering
this problem in
depth (maybe there is literature out there, and if
it does and
anybody knows about it - i would like to know it
also ;-))
Secondly, if you know the outlier is not an error,
but you interpret it
as representing a different combination of
properties than the rest
of your data - i am not very sure it is wise to use
it together with
your rest of the data in any interpolation exercise.
The outlier may
represent a different population and in this case i
cannot see any
physical reason to treat all your data together if
parts of the data
represent different things. At least this is my
opinion.
Besides, if your data is not only sparse (5 or 6
data points it is
really very sparse i think) but also far away in
space, they can be
at distances grater than the spatial correlation
range, and in this
case i really don't think you can use kriging
you will have either
a pure nugget effect or a very high nugget value and
not a too high
spatial correlation.
Monica
Dear list members,
Please advise what to do in following case:
The sparse dataset for kriging inlcudes only few
(5-6) original data points + interpolated external
data, that covering whole study area. One of the original data
points seems completly not to
fit to the main correlation line between original and
external data, however mostly probable is not an
error, but might represent different combination of
data properties. Is there is any chance to use this outlying point?
Does is sound feasible for you as specialists in
statistical analysis to use the kriging method in this
case?
Many thanks in advance for your help,
Gali Sirkis
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