Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization
Autor(a) principal: | |
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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=S2448-167X2021000200199 |
Resumo: | Abstract Non-linear geostatistical methods are known to deal appropriately with the geological and geometrical complexity of gold deposits. This article reports the results related to an investigation to improve the gold content estimate based on restricted ore modeling to honor the structural aspects that control the mineralization. The grade domains are defined by using structural measurements to guide the indicator kriging (IK) estimator. Relevant grade intervals are chosen as indicators. Kriging the indicators provides a measure of the grade uncertainty at the sample support. The probability indicator modeling relies on thresholding the estimates which are represented by cumulative distribution functions (cdf) at the unsampled locations. The implicit concept of probability means that the chance of an estimated node belonging to a given grade domain is as big as the estimated IK value. The geological consistency of IK models requires a proper definition of some key parameters: The probability thresholds and indicator variogram models must honor the structural features and stationarity conditions of grade intervals. The geological representativeness of these models depends heavily on thresholding the estimates. For instance, extremely permissive estimates may produce overrepresented ore domains. The decision of the optimal indicator probability for defining the ore boundaries is made by iterative comparison. Several thresholds were applied to kriged maps and the results reconciled to the most sampled areas until achieving reasonable geological adherence. The mineralization continuity often varies according to local structural features and so dynamic anisotropy is used to control the variogram direction and search ellipse to consider the significant scale trend and small-scale fold geometries. A case study based on a real gold deposit dataset was performed and the method was discussed. The IK models can define precisely the mineralization bounds in the most detailed areas. However, the results presented some limitations on reproducing the geological expectation in regions of wide drilling spacing. The lack of information in some areas led to an excessive number of small sub-zones. The method allows a faster and efficient modeling of structurally complex geometries and provides an uncertainty assessment which may be useful to support exploratory and short-term decisions. |
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Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralizationstructural geologygeological modellingIndicator KriginggeostatisticsAbstract Non-linear geostatistical methods are known to deal appropriately with the geological and geometrical complexity of gold deposits. This article reports the results related to an investigation to improve the gold content estimate based on restricted ore modeling to honor the structural aspects that control the mineralization. The grade domains are defined by using structural measurements to guide the indicator kriging (IK) estimator. Relevant grade intervals are chosen as indicators. Kriging the indicators provides a measure of the grade uncertainty at the sample support. The probability indicator modeling relies on thresholding the estimates which are represented by cumulative distribution functions (cdf) at the unsampled locations. The implicit concept of probability means that the chance of an estimated node belonging to a given grade domain is as big as the estimated IK value. The geological consistency of IK models requires a proper definition of some key parameters: The probability thresholds and indicator variogram models must honor the structural features and stationarity conditions of grade intervals. The geological representativeness of these models depends heavily on thresholding the estimates. For instance, extremely permissive estimates may produce overrepresented ore domains. The decision of the optimal indicator probability for defining the ore boundaries is made by iterative comparison. Several thresholds were applied to kriged maps and the results reconciled to the most sampled areas until achieving reasonable geological adherence. The mineralization continuity often varies according to local structural features and so dynamic anisotropy is used to control the variogram direction and search ellipse to consider the significant scale trend and small-scale fold geometries. A case study based on a real gold deposit dataset was performed and the method was discussed. The IK models can define precisely the mineralization bounds in the most detailed areas. However, the results presented some limitations on reproducing the geological expectation in regions of wide drilling spacing. The lack of information in some areas led to an excessive number of small sub-zones. The method allows a faster and efficient modeling of structurally complex geometries and provides an uncertainty assessment which may be useful to support exploratory and short-term decisions.Fundação Gorceix2021-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2021000200199REM - International Engineering Journal v.74 n.2 2021reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672020740034info:eu-repo/semantics/openAccessAfonseca,Bruno de DeusCosta,João Felipe Coimbra Leiteeng2021-03-25T00:00:00Zoai:scielo:S2448-167X2021000200199Revistahttps://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 |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
title |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
spellingShingle |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization Afonseca,Bruno de Deus structural geology geological modelling Indicator Kriging geostatistics |
title_short |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
title_full |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
title_fullStr |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
title_full_unstemmed |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
title_sort |
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization |
author |
Afonseca,Bruno de Deus |
author_facet |
Afonseca,Bruno de Deus Costa,João Felipe Coimbra Leite |
author_role |
author |
author2 |
Costa,João Felipe Coimbra Leite |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Afonseca,Bruno de Deus Costa,João Felipe Coimbra Leite |
dc.subject.por.fl_str_mv |
structural geology geological modelling Indicator Kriging geostatistics |
topic |
structural geology geological modelling Indicator Kriging geostatistics |
description |
Abstract Non-linear geostatistical methods are known to deal appropriately with the geological and geometrical complexity of gold deposits. This article reports the results related to an investigation to improve the gold content estimate based on restricted ore modeling to honor the structural aspects that control the mineralization. The grade domains are defined by using structural measurements to guide the indicator kriging (IK) estimator. Relevant grade intervals are chosen as indicators. Kriging the indicators provides a measure of the grade uncertainty at the sample support. The probability indicator modeling relies on thresholding the estimates which are represented by cumulative distribution functions (cdf) at the unsampled locations. The implicit concept of probability means that the chance of an estimated node belonging to a given grade domain is as big as the estimated IK value. The geological consistency of IK models requires a proper definition of some key parameters: The probability thresholds and indicator variogram models must honor the structural features and stationarity conditions of grade intervals. The geological representativeness of these models depends heavily on thresholding the estimates. For instance, extremely permissive estimates may produce overrepresented ore domains. The decision of the optimal indicator probability for defining the ore boundaries is made by iterative comparison. Several thresholds were applied to kriged maps and the results reconciled to the most sampled areas until achieving reasonable geological adherence. The mineralization continuity often varies according to local structural features and so dynamic anisotropy is used to control the variogram direction and search ellipse to consider the significant scale trend and small-scale fold geometries. A case study based on a real gold deposit dataset was performed and the method was discussed. The IK models can define precisely the mineralization bounds in the most detailed areas. However, the results presented some limitations on reproducing the geological expectation in regions of wide drilling spacing. The lack of information in some areas led to an excessive number of small sub-zones. The method allows a faster and efficient modeling of structurally complex geometries and provides an uncertainty assessment which may be useful to support exploratory and short-term decisions. |
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=S2448-167X2021000200199 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2021000200199 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0370-44672020740034 |
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_ |
1754734691878961152 |