Disertación de Maestría
DOI
10.11606/D.44.2008.tde-14082008-165227
Documento
Autor
Nombre completo
Jorge Watanabe
Dirección Electrónica
Área de Conocimiento
Fecha de Defensa
Publicación
São Paulo, 2008
Director
Tribunal
Yamamoto, Jorge Kazuo (Presidente)
Monteiro, Marcelo Costa
Rocha, Marcelo Monteiro da
Título en portugués
Métodos geoestatísticos de co-estimativas: estudo do efeito da correlação entre variáveis na precisão dos resultados
Palabras clave en portugués
Co-estimativa
Coeficiente de correlação de Pearson
Efeito de suavização
Krigagem com deriva externa
Modelo Markoviano
Resumen en portugués
Título en inglés
Co-estimation geostatistical methods: a study of the correlation between variables at results precision
Palabras clave en inglés
Co-estimation
Collocated sampling
Collocated simple cokriging
Cross semivariogram
External drift kriging
Markov model
Multicollocated sampling
Multivariate geostatistics
Pearson's correlation coefficient
Smoothing effect
Resumen en inglés
This master dissertation presents the results of a survey into co-estimation methods commonly used in geostatistics. These methods are ordinary cokriging, collocated cokriging and kriging with an external drift. Besides that ordinary kriging was considered just to illustrate how it does work when the primary variable is poorly sampled. As we know co-estimation methods depend on a secondary variable sampled over the estimation domain. Moreover, this secondary variable should present linear correlation with the main variable or primary variable. Usually the primary variable is poorly sampled whereas the secondary variable is known over the estimation domain. For instance in oil exploration the primary variable is porosity as measured on rock samples gathered from drill holes and the secondary variable is seismic amplitude derived from processing seismic reflection data. It is important to mention that primary and secondary variables must present some degree of correlation. However, we do not know how they work depending on the correlation coefficient. That is the question. Thus, we have tested co-estimation methods for several data sets presenting different degrees of correlation. Actually, these data sets were generated in computer based on some data transform algorithms. Five correlation values have been considered in this study: 0.993; 0.870; 0.752; 0.588 and 0.461. Collocated simple cokriging was the best method among all tested. This method has an internal filter applied to compute the weight for the secondary variable, which in its turn depends on the correlation coefficient. In fact, the greater the correlation coefficient the greater the weight of secondary variable is. Then it means this method works even when the correlation coefficient between primary and secondary variables is low. This is the most impressive result that came out from this research.

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JW.pdf (3.50 Mbytes)
Fecha de Publicación
2008-09-02