Full text
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| Author(s): |
Anelise de Lima Souza
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Paulo. |
| Institution: | Universidade de São Paulo (USP). Instituto de Geociências (IG/BT) |
| Defense date: | 2007-07-12 |
| Examining board members: |
Jorge Kazuo Yamamoto;
Paulo Milton Barbosa Landim;
Marcelo Monteiro da Rocha
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| Advisor: | Jorge Kazuo Yamamoto |
| Abstract | |
This dissertation presents the results of an investigation into the effectiveness of the post-processing algorithm for correcting the smoothing effect of ordinary kriging estimates. Three different statistical distributions have been considered in this study: gaussian, lognormal and inverted lognormal. As we know among these distributions, the lognormal distribution is the most difficult one to handle, because this distribution presents a great number of low values and a few high values in which these high values are responsible for the great variability of the data set. Besides statistical distribution other parameters have been considered in this study: the influence of the sample size and the number of neighbor data points as well. For gaussian and inverted lognormal distributions the post-processing algorithm worked well in all situations. However, it was observed loss of local accuracy for lognormal data. Thus, for these data the technique of ordinary lognormal kriging was applied. Actually, a recently proposed approach for backtransforming lognormal kriging estimates was also applied. This approach is based on correcting the histogram of lognormal kriging estimates and then backtransforming it to the original scale of measurement. Results of back-transformed lognormal kriging estimates were always better than the traditional approach. Furthermore, lognormal kriging estimates have provided better results than the normal kriging ones. (AU) | |