Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
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=S2448-167X2021000200269 |
Resumo: | Abstract Most mining decisions are based on models estimated/simulated given the information obtained from samples. During the exploration stage, samples are commonly taken using diamond drill holes which are accurate and precise. These samples are considered hard data. In the production stage, new samples are added. These last are cheaper and more abundant than the drill hole samples, but imprecise and are here named as soft data. Usually hard and soft data are not sampled at the same locations, they form a heterotopic dataset. This article proposes a framework for geostatistical simulation with completely heterotopic soft data. The simulation proceeds in two steps. First, the variable of interest at the locations where soft data are available is simulated. The local conditional distributions built at these locations consider both hard and soft data and are obtained using simple cokriging with the intrinsic coregionalization model. Second, the variable of interest in the entire simulation grid using the original and previously simulated values at soft data locations is simulated. The results show that the information from soft data improved both the accuracy and precision of the simulated models. The proposed framework is illustrated by a case study with data obtained from an underground copper mine. |
id |
FG-1_d59f79727a4fe2eb7b25c1409e0f5f15 |
---|---|
oai_identifier_str |
oai:scielo:S2448-167X2021000200269 |
network_acronym_str |
FG-1 |
network_name_str |
REM - International Engineering Journal |
repository_id_str |
|
spelling |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalizationlocal probability distributioncompletely heterotopicgeostatistical simulationsdata integrationAbstract Most mining decisions are based on models estimated/simulated given the information obtained from samples. During the exploration stage, samples are commonly taken using diamond drill holes which are accurate and precise. These samples are considered hard data. In the production stage, new samples are added. These last are cheaper and more abundant than the drill hole samples, but imprecise and are here named as soft data. Usually hard and soft data are not sampled at the same locations, they form a heterotopic dataset. This article proposes a framework for geostatistical simulation with completely heterotopic soft data. The simulation proceeds in two steps. First, the variable of interest at the locations where soft data are available is simulated. The local conditional distributions built at these locations consider both hard and soft data and are obtained using simple cokriging with the intrinsic coregionalization model. Second, the variable of interest in the entire simulation grid using the original and previously simulated values at soft data locations is simulated. The results show that the information from soft data improved both the accuracy and precision of the simulated models. The proposed framework is illustrated by a case study with data obtained from an underground copper mine.Fundação Gorceix2021-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2021000200269REM - International Engineering Journal v.74 n.2 2021reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672020740075info:eu-repo/semantics/openAccessAraújo,Cristina da PaixãoBassani,Marcel Antônio ArcariKoppe,Vanessa CerqueiraCosta,João Felipe Coimbra LeiteSoares,Amílcar de Oliveiraeng2021-03-25T00:00:00Zoai:scielo:S2448-167X2021000200269Revistahttps://www.rem.com.br/?lang=pt-brPRIhttps://old.scielo.br/oai/scielo-oai.php||editor@rem.com.br2448-167X2448-167Xopendoar:2021-03-25T00:00REM - International Engineering Journal - Fundação Gorceix (FG)false |
dc.title.none.fl_str_mv |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
title |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
spellingShingle |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization Araújo,Cristina da Paixão local probability distribution completely heterotopic geostatistical simulations data integration |
title_short |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
title_full |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
title_fullStr |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
title_full_unstemmed |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
title_sort |
Geostatistical simulations with heterotopic hard and soft data without modeling the linear model of coregionalization |
author |
Araújo,Cristina da Paixão |
author_facet |
Araújo,Cristina da Paixão Bassani,Marcel Antônio Arcari Koppe,Vanessa Cerqueira Costa,João Felipe Coimbra Leite Soares,Amílcar de Oliveira |
author_role |
author |
author2 |
Bassani,Marcel Antônio Arcari Koppe,Vanessa Cerqueira Costa,João Felipe Coimbra Leite Soares,Amílcar de Oliveira |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Araújo,Cristina da Paixão Bassani,Marcel Antônio Arcari Koppe,Vanessa Cerqueira Costa,João Felipe Coimbra Leite Soares,Amílcar de Oliveira |
dc.subject.por.fl_str_mv |
local probability distribution completely heterotopic geostatistical simulations data integration |
topic |
local probability distribution completely heterotopic geostatistical simulations data integration |
description |
Abstract Most mining decisions are based on models estimated/simulated given the information obtained from samples. During the exploration stage, samples are commonly taken using diamond drill holes which are accurate and precise. These samples are considered hard data. In the production stage, new samples are added. These last are cheaper and more abundant than the drill hole samples, but imprecise and are here named as soft data. Usually hard and soft data are not sampled at the same locations, they form a heterotopic dataset. This article proposes a framework for geostatistical simulation with completely heterotopic soft data. The simulation proceeds in two steps. First, the variable of interest at the locations where soft data are available is simulated. The local conditional distributions built at these locations consider both hard and soft data and are obtained using simple cokriging with the intrinsic coregionalization model. Second, the variable of interest in the entire simulation grid using the original and previously simulated values at soft data locations is simulated. The results show that the information from soft data improved both the accuracy and precision of the simulated models. The proposed framework is illustrated by a case study with data obtained from an underground copper mine. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-01 |
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=S2448-167X2021000200269 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2021000200269 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0370-44672020740075 |
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.74 n.2 2021 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_ |
1754734691892592640 |