Integration of Disparate Data with Logratios of Conditional Probabilities

Author	dc.contributor.author	Hong, S.
Author	dc.contributor.author	Ortiz Cabrera, Julián	es_CL
Author	dc.contributor.author	Deutsch, C. V.	es_CL
Admission date	dc.date.accessioned	2010-03-29T19:33:25Z
Available date	dc.date.available	2010-03-29T19:33:25Z
Publication date	dc.date.issued	2007
Cita de ítem	dc.identifier.citation	Petroleum Geostatistics 2007	en_US
Identifier	dc.identifier.uri	https://repositorio.uchile.cl/handle/2250/125294
Abstract	dc.description.abstract	Combining conditional probabilities based on logratios is considered and compared with other integration models. Each data set is at first treated separately and merged with considering data source redundancies. Permanence of ratios and tau-model are presented as a way of merging conditional probabilities and their results are compared to those of logratio model. In logratios model, redundancy factors are iteratively optimized in order to improve the integrated estimate. Measure of goodness is used to quantitatively evaluate the models of integration and logratio model gives the best experimental results in terms of local uncertainty and closeness to true facies.	en_US
Lenguage	dc.language.iso	en	en_US
Título	dc.title	Integration of Disparate Data with Logratios of Conditional Probabilities	en_US
Document type	dc.type	Artículo de revista