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: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/204366 |
Resumo: | 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 well-designed estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model. |
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Araújo, Cristina da PaixãoCosta, Joao Felipe Coimbra LeiteKoppe, Vanessa Cerqueira2020-01-16T04:09:52Z20182448-167Xhttp://hdl.handle.net/10183/204366001105103Short-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 well-designed estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model.application/pdfengREM : international engineering journal. Ouro Preto, MG. Vol. 71, no. 1 (Jan./Mar. 2018), p. 117-122AmostragemKrigagemErro amostralBiased samplesGrade estimatesKrigingCokrigingSampling errorImproving short-term grade block models: alternative for correcting soft datainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001105103.pdf.txt001105103.pdf.txtExtracted Texttext/plain21613http://www.lume.ufrgs.br/bitstream/10183/204366/2/001105103.pdf.txta40b9e309899bce29ac0586584df8651MD52ORIGINAL001105103.pdfTexto completo (inglês)application/pdf1435867http://www.lume.ufrgs.br/bitstream/10183/204366/1/001105103.pdffd344ce20fa39b8971f0ee8d0bf74401MD5110183/2043662021-05-07 04:57:57.256993oai:www.lume.ufrgs.br:10183/204366Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-05-07T07:57:57Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.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 Amostragem Krigagem Erro amostral 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, Joao Felipe Coimbra Leite Koppe, Vanessa Cerqueira |
author_role |
author |
author2 |
Costa, Joao Felipe Coimbra Leite Koppe, Vanessa Cerqueira |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Araújo, Cristina da Paixão Costa, Joao Felipe Coimbra Leite Koppe, Vanessa Cerqueira |
dc.subject.por.fl_str_mv |
Amostragem Krigagem Erro amostral |
topic |
Amostragem Krigagem Erro amostral Biased samples Grade estimates Kriging Cokriging Sampling error |
dc.subject.eng.fl_str_mv |
Biased samples Grade estimates Kriging Cokriging Sampling error |
description |
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 well-designed estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018 |
dc.date.accessioned.fl_str_mv |
2020-01-16T04:09:52Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/204366 |
dc.identifier.issn.pt_BR.fl_str_mv |
2448-167X |
dc.identifier.nrb.pt_BR.fl_str_mv |
001105103 |
identifier_str_mv |
2448-167X 001105103 |
url |
http://hdl.handle.net/10183/204366 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
REM : international engineering journal. Ouro Preto, MG. Vol. 71, no. 1 (Jan./Mar. 2018), p. 117-122 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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UFRGS |
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Repositório Institucional da UFRGS |
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