dear members,
Is there anyone with experiences in indicator kriging with soft data using gslib ? 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 "raw" values). 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, quantiles) 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 ... regards Lenz Lorenz Dobler Bayer. Geologisches Landesamt Heßstr. 128 80797 München Tel.: 089/9214-2766 e-mail: [EMAIL PROTECTED] -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
