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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemarte.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Repositórioltid.inpe.br/sbsr/2002/11.15.10.48
Última Atualização2003:10.13.12.31.20 (UTC) administrator
Repositório de Metadadosltid.inpe.br/sbsr/2002/11.15.10.48.27
Última Atualização dos Metadados2018:06.06.02.41.43 (UTC) administrator
Chave SecundáriaINPE-9445-PRE/5101
ISBN85-17-00017-X
Chave de CitaçãoMoreiraAlmeCâma:2003:MoDaGe
TítuloModelamento de dados geológicos em pesquisa mineral segundo o teorema de Bayes
FormatoCD-ROM
Ano2003
Data de Acesso13 maio 2024
Tipo SecundárioPRE CN
Número de Arquivos1
Tamanho289 KiB
2. Contextualização
Autor1 Moreira, Fábio Roque da Silva
2 Almeida Filho, Raimundo
3 Câmara, Gilberto
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JJ4Q
Grupo1 DPI-INPE-MCT-BR
2 DSR-INPE-MCT-BR
3 DPI-INPE-MCT-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 fmoreira@ltid.inpe.br
2 rai@ltid.inpe.br
3 gilberto@dpi.inpe.br
EditorEpiphanio, José Carlos Neves
Fonseca, Leila Maria Garcia
Endereço de e-Mailfmoreira@ltid.inpe.br
Nome do EventoSimpósio Brasileiro de Sensoriamento Remoto, 11 (SBSR).
Localização do EventoBelo Horizonte
Data5-10 abr.2003
Editora (Publisher)INPE
Cidade da EditoraSão José dos Campos
Páginas895-902
Título do LivroAnais
OrganizaçãoInstituto Nacional de Pesquisas Espaciais
Histórico (UTC)2006-01-06 19:22:52 :: sbsr -> administrator ::
2009-06-03 15:16:32 :: administrator -> vinicius ::
2009-06-30 14:04:20 :: vinicius -> erich@sid.inpe.br ::
2010-05-14 02:53:00 :: erich@sid.inpe.br -> marciana ::
2011-02-16 13:52:58 :: marciana -> administrator :: 2003
2018-06-06 02:41:43 :: administrator -> :: 2003
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Palavras-ChaveBayesian method
spatial analysis
GIS
Poços de Caldas Plateau
ResumoAbstract. Bayesian theory was evaluated on the spatial analysis of geological data, to address potential areas for radioactive mineral occurrences in the Poços de Caldas Plateau ( 750 Km2). Spatial inference techniques were applied controlled by a prospecting model based on diagnostic criteria, represented by favorable lithology, structures features and gamma-ray intensity. In the analysis the 48 mineral occurrences were used to calculate statistical parameters, that served for weighting the evidences of the model. Firstly, Contrast studies were conducted to measure the spatial correlation between the evidences and the mineral deposits. This study allowed to determine the threshold of the binary evidences, where the binary patterns present the best spatial correlation with the mineral occurrences. Before the final integration, Qui-square (?2) tests were conduct comparing the binary evidences on pairs. The goal was to measure the conditional independence between the classes of the binary evidences. Verified the independence of the binary evidences, it was calculated the Sufficiency (LS) and Necessity (LN) ratio. The natural logarithm of LS and LN, W+ and W- respectively, were added to the prior odds of the mineral occurrences, controlled by the presence or absence of the potential classes of the binary evidences. This procedure yielded the posterior odds values, from where it was obtained the posterior probabilities considering the whole evidences. The resulting product was arbitrary sliced in four potential categories (null, low, medium and high). The bayesian scenario was qualitatively and quantitatively compared with previous results obtained by Boolean and Weighted Means based models. The comparison of both procedures showed that the bayesian scenario accomplished a medium performance. A target area of 27,54 Km2 (3,78% of the alkaline complex) included 27 mineral occurrences, whereas each previous model encompassed only 24 in an approximately similar area. We also assessed the correlation of mineral occurrences in relation to the potential classes for the different scenarios. The high and medium potential classes of the bayesian scenario showed posterior probabilities 6.69 and 8,39 higher than the prior probabilities. These values were, respectively, 12.6 and 4.97 for the same classes defined by the Weighted Means based model. For the potential class of the Boolean model this value was 5.78. The Bayesian method showed an interesting approach for the spatial analysis. Perhaps being the major advantage, the possibility of applying statistical parameters to calculate the weights of the models evidences.
ÁreaSRE
TipoGeologia / Geology
Arranjo 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Modelamento de dados...
Arranjo 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Modelamento de dados...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/ltid.inpe.br/sbsr/2002/11.15.10.48
URL dos dados zipadoshttp://urlib.net/zip/ltid.inpe.br/sbsr/2002/11.15.10.48
IdiomaPortuguese
Arquivo Alvo08_242.pdf
Grupo de Usuáriosadministrator
erich@sid.inpe.br
Visibilidadeshown
Detentor da CópiaSID/SCD
Permissão de Leituraallow from all
5. Fontes relacionadas
Repositório Espelhodpi.inpe.br/marte@80/2007/10.17.19.59
Unidades Imediatamente Superiores8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
Divulgação<E>
Acervo Hospedeirodpi.inpe.br/banon/2003/12.10.19.30
6. Notas
Campos Vaziosarchivingpolicy archivist callnumber contenttype copyright creatorhistory descriptionlevel documentstage doi edition identifier issn label lineage mark nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype url versiontype volume


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