Dear Sebastiano Trevisani,
I'd like to suggest you to give a look at Bayesian Maximum Entropy (BME) approach.
I guess that this methodology can help you in dealing with such kind of dataset.
The BME approach is a geostatistical proposal that take into account physical knowledge basis, hard and soft data, spatiotemporal structure and so on.
I reccomend you the book:
Christakos, G., P. Bogaert, and M.L. Serre (2002) Temporal GIS: Advanced Functions for Field-Based Applications, Springer-Verlag, New York, N.Y., 217 p.
But you also may find some nice refferences at George Christakos and Marc Serre homepages:
http://www.unc.edu/~fchrist/CISEhome.htm
http://www.unc.edu/~mserre/ESEhome.html
Att.
Rubens Caldeira Monteiro
Avaliação de Recursos - Complexo de Mariana
Gerência de Geologia - GAPCS
Depto Ferrosos Sul - DIFS / GEPMS
Companhia Vale do Rio Doce - CVRD
brazilian iron ore mining company
carrier 849 ramal 6534
+55 (31) 3559-6534 / +55 (31) 8797-2081 (cel.)
[EMAIL PROTECTED]
| sebastiano trevisani <[EMAIL PROTECTED]>
Enviado Por: [EMAIL PROTECTED] 03/07/2006 04:52
|
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Dear List Members
I'm trying to find some paper explaining relationship and differences
between classical geostatistical methodologies and objective analysis.
I'm working with oceanographic data set (really complex: the data are
4D) ....it seem that in the oceanography world objective analysis is
more popular!
Thank you in advance for your help.
S. Trevisani
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