Improving short-term grade block models: alternative for correcting soft data
Autor(a) principal: | |
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Data de Publicação: | 2018 |
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=S2448-167X2018000100117 |
Resumo: | Abstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on grade estimation and propose a method of correcting the imprecision and bias in the soft data. In addition, this paper evaluates the benefits of using soft data in mining planning. These concepts are illustrated via a gold mine case study, where two different data types are presented. The study used Au grades collected via diamond drilling (hard data) and channels (soft data). Four methodologies were considered for estimation of the Au grades of each block to be mined: ordinary kriging with hard and soft data pooled without considering differences in data quality; ordinary kriging with only hard data; standardized ordinary kriging with pooled hard and soft data; and standardized, ordinary cokriging. The results show that even biased samples collected using poor sampling protocols improve the estimates more than a limited number of precise and unbiased samples. A welldesigned estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model. |
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oai:scielo:S2448-167X2018000100117 |
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network_name_str |
REM - International Engineering Journal |
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Improving short-term grade block models: alternative for correcting soft databiased samplesgrade estimateskrigingcokrigingsampling errorAbstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on grade estimation and propose a method of correcting the imprecision and bias in the soft data. In addition, this paper evaluates the benefits of using soft data in mining planning. These concepts are illustrated via a gold mine case study, where two different data types are presented. The study used Au grades collected via diamond drilling (hard data) and channels (soft data). Four methodologies were considered for estimation of the Au grades of each block to be mined: ordinary kriging with hard and soft data pooled without considering differences in data quality; ordinary kriging with only hard data; standardized ordinary kriging with pooled hard and soft data; and standardized, ordinary cokriging. The results show that even biased samples collected using poor sampling protocols improve the estimates more than a limited number of precise and unbiased samples. A welldesigned estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model.Fundação Gorceix2018-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2018000100117REM - International Engineering Journal v.71 n.1 2018reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672016710007info:eu-repo/semantics/openAccessAraújo,Cristina da PaixãoCosta,João Felipe Coimbra LeiteKoppe,Vanessa Cerqueiraeng2018-01-09T00:00:00Zoai:scielo:S2448-167X2018000100117Revistahttps://www.rem.com.br/?lang=pt-brPRIhttps://old.scielo.br/oai/scielo-oai.php||editor@rem.com.br2448-167X2448-167Xopendoar:2018-01-09T00:00REM - International Engineering Journal - Fundação Gorceix (FG)false |
dc.title.none.fl_str_mv |
Improving short-term grade block models: alternative for correcting soft data |
title |
Improving short-term grade block models: alternative for correcting soft data |
spellingShingle |
Improving short-term grade block models: alternative for correcting soft data Araújo,Cristina da Paixão biased samples grade estimates kriging cokriging sampling error |
title_short |
Improving short-term grade block models: alternative for correcting soft data |
title_full |
Improving short-term grade block models: alternative for correcting soft data |
title_fullStr |
Improving short-term grade block models: alternative for correcting soft data |
title_full_unstemmed |
Improving short-term grade block models: alternative for correcting soft data |
title_sort |
Improving short-term grade block models: alternative for correcting soft data |
author |
Araújo,Cristina da Paixão |
author_facet |
Araújo,Cristina da Paixão Costa,João Felipe Coimbra Leite Koppe,Vanessa Cerqueira |
author_role |
author |
author2 |
Costa,João Felipe Coimbra Leite Koppe,Vanessa Cerqueira |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Araújo,Cristina da Paixão Costa,João Felipe Coimbra Leite Koppe,Vanessa Cerqueira |
dc.subject.por.fl_str_mv |
biased samples grade estimates kriging cokriging sampling error |
topic |
biased samples grade estimates kriging cokriging sampling error |
description |
Abstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on grade estimation and propose a method of correcting the imprecision and bias in the soft data. In addition, this paper evaluates the benefits of using soft data in mining planning. These concepts are illustrated via a gold mine case study, where two different data types are presented. The study used Au grades collected via diamond drilling (hard data) and channels (soft data). Four methodologies were considered for estimation of the Au grades of each block to be mined: ordinary kriging with hard and soft data pooled without considering differences in data quality; ordinary kriging with only hard data; standardized ordinary kriging with pooled hard and soft data; and standardized, ordinary cokriging. The results show that even biased samples collected using poor sampling protocols improve the estimates more than a limited number of precise and unbiased samples. A welldesigned estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-03-01 |
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=S2448-167X2018000100117 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2018000100117 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0370-44672016710007 |
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.71 n.1 2018 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_ |
1754734690915319808 |