dear members, Is there anyone with experiences in indicator kriging with soft data using gslib ? i have a continous primary variable and a continousMessage 1 of 1 , Apr 8, 2002View Sourcedear members,
Is there anyone with experiences in indicator kriging with soft data using
i have a continous primary variable and a continous (exhaustive) secondary
variable. I allready did "normal" indicator kriging (so i allready have a
set of transformed hard indicator data and variograms for the corresponding
thresholds) and the result looks rather plausible. my idea now is to
incorporate the secondary variable within indicator kriging to improve the
estimates in regions where sample densitiy is sparse (similar to kriging
with external drift or simple kriging with varying local means using uncoded
the most straightforward method seems to be simple indicator kriging using
soft prior probabilities as described by Goovaerts (1997; pp 307) and
Deutsch & Journel (1998; pp77, i hope they mean the same by "simple kriging
with prior means" !).
some questions about that method just to be sure that i am on the right way:
1. first i have to classify my secondary (continous) soft data. how do i get
discrete classes of soft data with a "calibration scattergram". In Deutsch &
Journel 1998; pp92 i can not recognize how they decided to classify ! what
is the best way to classify - the more classes the better ?
2. i have to calculate soft prior probabilities. the calibration step is to
calculate for each class the proportions of data that do not exceed one (or
more) threshold(s) (i have 7 thresholds!). example: i have defined 3 (or 5)
classes of soft data (question 1) so i have to calculate 3(5) different
frequencies of not exceeding one threshold. the classes of soft data are
then replaced by the calculated soft probabilities (=local prior
probabilities) => if i have more thresholds i get 3(5) different values as
local prior probabilities for each threshold ???
3. the rest of the method is very similar to simple kriging with varying
local means. the residuals i get by substracting hard indicator data [0,1]
from local soft probabilities (0 ....1) calculated in question 3. the
residuals are used to calculate semivariograms.
4. how can i do the kriging step within wingslib? with ik3d (what about the
exhaustive data-file?) or with kt3d? at least i want to use all the
advantages of indicator kriging (maps of estimated values, probabilities,
p.s. i am looking for some basic references on indicator kriging (using soft
data) with gslib, especially Journel (1987): Geostatistic for the
environmental sciences, EPA project no. cr 811893. Technical report. U.S.
EPA Lab, Las Vegas, NV. it's hard to get in germany ...
Bayer. Geologisches Landesamt
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