Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | REM - International Engineering Journal |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2016000400491 |
Resumo: | Abstract Data quality control programs used in the mineral industry normally define tolerance limits based on values considered as good practice or those that have previously been applied to similar deposits, although the precision and accuracy of estimates depend on a combination of geological characteristics, estimation parameters, sample spacing and data quality. This study investigates how the sample quality limits affect the estimates results. The proposed methodology is based on a series of metrics used to compare the impact on the estimates using a synthetic database with an increasing amount of error added to the original sample grades or positions, emulating different levels of precision. The proposed approach results lead to tolerance limits for the grades similar to those recommended in literature. The influence of the positional uncertainty on model estimates is at a minimum, because of the accuracy of current surveying methods that have a deviation in the order of millimeters, so its impact can be considered negligible. |
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REM - International Engineering Journal |
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Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality controlgeostatisticsdata uncertaintysensitivity analysissampling errorQA/QCAbstract Data quality control programs used in the mineral industry normally define tolerance limits based on values considered as good practice or those that have previously been applied to similar deposits, although the precision and accuracy of estimates depend on a combination of geological characteristics, estimation parameters, sample spacing and data quality. This study investigates how the sample quality limits affect the estimates results. The proposed methodology is based on a series of metrics used to compare the impact on the estimates using a synthetic database with an increasing amount of error added to the original sample grades or positions, emulating different levels of precision. The proposed approach results lead to tolerance limits for the grades similar to those recommended in literature. The influence of the positional uncertainty on model estimates is at a minimum, because of the accuracy of current surveying methods that have a deviation in the order of millimeters, so its impact can be considered negligible.Fundação Gorceix2016-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2016000400491REM - International Engineering Journal v.69 n.4 2016reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672015690159info:eu-repo/semantics/openAccessSilva,Victor MiguelCosta,Joao Felipe Coimbra Leiteeng2016-10-17T00:00:00Zoai:scielo:S2448-167X2016000400491Revistahttps://www.rem.com.br/?lang=pt-brPRIhttps://old.scielo.br/oai/scielo-oai.php||editor@rem.com.br2448-167X2448-167Xopendoar:2016-10-17T00:00REM - International Engineering Journal - Fundação Gorceix (FG)false |
dc.title.none.fl_str_mv |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
title |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
spellingShingle |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control Silva,Victor Miguel geostatistics data uncertainty sensitivity analysis sampling error QA/QC |
title_short |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
title_full |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
title_fullStr |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
title_full_unstemmed |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
title_sort |
Sensitivity analysis of ordinary kriging to sampling and positional errors and applications in quality control |
author |
Silva,Victor Miguel |
author_facet |
Silva,Victor Miguel Costa,Joao Felipe Coimbra Leite |
author_role |
author |
author2 |
Costa,Joao Felipe Coimbra Leite |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Silva,Victor Miguel Costa,Joao Felipe Coimbra Leite |
dc.subject.por.fl_str_mv |
geostatistics data uncertainty sensitivity analysis sampling error QA/QC |
topic |
geostatistics data uncertainty sensitivity analysis sampling error QA/QC |
description |
Abstract Data quality control programs used in the mineral industry normally define tolerance limits based on values considered as good practice or those that have previously been applied to similar deposits, although the precision and accuracy of estimates depend on a combination of geological characteristics, estimation parameters, sample spacing and data quality. This study investigates how the sample quality limits affect the estimates results. The proposed methodology is based on a series of metrics used to compare the impact on the estimates using a synthetic database with an increasing amount of error added to the original sample grades or positions, emulating different levels of precision. The proposed approach results lead to tolerance limits for the grades similar to those recommended in literature. The influence of the positional uncertainty on model estimates is at a minimum, because of the accuracy of current surveying methods that have a deviation in the order of millimeters, so its impact can be considered negligible. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-01 |
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=S2448-167X2016000400491 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2016000400491 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0370-44672015690159 |
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 |
Fundação Gorceix |
publisher.none.fl_str_mv |
Fundação Gorceix |
dc.source.none.fl_str_mv |
REM - International Engineering Journal v.69 n.4 2016 reponame:REM - International Engineering Journal instname:Fundação Gorceix (FG) instacron:FG |
instname_str |
Fundação Gorceix (FG) |
instacron_str |
FG |
institution |
FG |
reponame_str |
REM - International Engineering Journal |
collection |
REM - International Engineering Journal |
repository.name.fl_str_mv |
REM - International Engineering Journal - Fundação Gorceix (FG) |
repository.mail.fl_str_mv |
||editor@rem.com.br |
_version_ |
1754734690530492416 |