Minimum/maximum autocorrelation factors applied to grade estimation
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | REM. Revista Escola de Minas (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672014000200013 |
Resumo: | It is frequent to face estimation problems when dealing with mineral deposits involving multiple correlated variables. The resulting model is expected to reproduce data correlation. However, is not guaranteed that the correlation observed among data will be reproduced by the model, if the variables are estimated independently, and this correlation is not explicitly taken into account. The adequate geostatistical approach to address this estimation problem is co-kriging which requires cross and direct covariance modeling of all variables, satisfying the LMC. An alternative is to decorrelate the variables and estimate each independently, using for instance, the minimum/maximum autocorrelation factors (MAF) approach, which uses a linear transformation on the correlated variables, transforming them to a new uncorrelated set. The transformed data can be estimated through kriging. Afterwards, the estimates are back-transformed to the original data space. The methodology is illustrated in a case study where three correlated variables are estimated using the MAF method combined with kriging and through co-kriging, used as a benchmark. The results show less than a 2% deviation between both methodologies. |
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Minimum/maximum autocorrelation factors applied to grade estimationminimum/maximum autocorrelations factorsgeostatisticskrigingIt is frequent to face estimation problems when dealing with mineral deposits involving multiple correlated variables. The resulting model is expected to reproduce data correlation. However, is not guaranteed that the correlation observed among data will be reproduced by the model, if the variables are estimated independently, and this correlation is not explicitly taken into account. The adequate geostatistical approach to address this estimation problem is co-kriging which requires cross and direct covariance modeling of all variables, satisfying the LMC. An alternative is to decorrelate the variables and estimate each independently, using for instance, the minimum/maximum autocorrelation factors (MAF) approach, which uses a linear transformation on the correlated variables, transforming them to a new uncorrelated set. The transformed data can be estimated through kriging. Afterwards, the estimates are back-transformed to the original data space. The methodology is illustrated in a case study where three correlated variables are estimated using the MAF method combined with kriging and through co-kriging, used as a benchmark. The results show less than a 2% deviation between both methodologies.Escola de Minas2014-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672014000200013Rem: Revista Escola de Minas v.67 n.2 2014reponame:REM. Revista Escola de Minas (Online)instname:Escola de Minasinstacron:ESCOLA DE MINAS10.1590/S0370-44672014000200013info:eu-repo/semantics/openAccessSilva,Camilla Zacché daCosta,João Felipe Coimbra Leiteeng2014-07-25T00:00:00Zoai:scielo:S0370-44672014000200013Revistahttp://www.scielo.br/remhttps://old.scielo.br/oai/scielo-oai.phpeditor@rem.com.br1807-03530370-4467opendoar:2014-07-25T00:00REM. Revista Escola de Minas (Online) - Escola de Minasfalse |
dc.title.none.fl_str_mv |
Minimum/maximum autocorrelation factors applied to grade estimation |
title |
Minimum/maximum autocorrelation factors applied to grade estimation |
spellingShingle |
Minimum/maximum autocorrelation factors applied to grade estimation Silva,Camilla Zacché da minimum/maximum autocorrelations factors geostatistics kriging |
title_short |
Minimum/maximum autocorrelation factors applied to grade estimation |
title_full |
Minimum/maximum autocorrelation factors applied to grade estimation |
title_fullStr |
Minimum/maximum autocorrelation factors applied to grade estimation |
title_full_unstemmed |
Minimum/maximum autocorrelation factors applied to grade estimation |
title_sort |
Minimum/maximum autocorrelation factors applied to grade estimation |
author |
Silva,Camilla Zacché da |
author_facet |
Silva,Camilla Zacché da Costa,João Felipe Coimbra Leite |
author_role |
author |
author2 |
Costa,João Felipe Coimbra Leite |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Silva,Camilla Zacché da Costa,João Felipe Coimbra Leite |
dc.subject.por.fl_str_mv |
minimum/maximum autocorrelations factors geostatistics kriging |
topic |
minimum/maximum autocorrelations factors geostatistics kriging |
description |
It is frequent to face estimation problems when dealing with mineral deposits involving multiple correlated variables. The resulting model is expected to reproduce data correlation. However, is not guaranteed that the correlation observed among data will be reproduced by the model, if the variables are estimated independently, and this correlation is not explicitly taken into account. The adequate geostatistical approach to address this estimation problem is co-kriging which requires cross and direct covariance modeling of all variables, satisfying the LMC. An alternative is to decorrelate the variables and estimate each independently, using for instance, the minimum/maximum autocorrelation factors (MAF) approach, which uses a linear transformation on the correlated variables, transforming them to a new uncorrelated set. The transformed data can be estimated through kriging. Afterwards, the estimates are back-transformed to the original data space. The methodology is illustrated in a case study where three correlated variables are estimated using the MAF method combined with kriging and through co-kriging, used as a benchmark. The results show less than a 2% deviation between both methodologies. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672014000200013 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672014000200013 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0370-44672014000200013 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola de Minas |
publisher.none.fl_str_mv |
Escola de Minas |
dc.source.none.fl_str_mv |
Rem: Revista Escola de Minas v.67 n.2 2014 reponame:REM. Revista Escola de Minas (Online) instname:Escola de Minas instacron:ESCOLA DE MINAS |
instname_str |
Escola de Minas |
instacron_str |
ESCOLA DE MINAS |
institution |
ESCOLA DE MINAS |
reponame_str |
REM. Revista Escola de Minas (Online) |
collection |
REM. Revista Escola de Minas (Online) |
repository.name.fl_str_mv |
REM. Revista Escola de Minas (Online) - Escola de Minas |
repository.mail.fl_str_mv |
editor@rem.com.br |
_version_ |
1754122198708125696 |