Using multiple random walk simulation in short-term grade models
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/163915 |
Resumo: | Geostatistical simulation comprises a variety of techniques which can help on the decision-making process for uncertainties. They allow the uncertainty assessment of function responses (which depend on the simulated inputs) commonly through a non-linear relationship (net present value, interest tax return, geometallurgical ore recovery…). However, one of their limitations is that running the simulation can take considerable processing time to be executed in large deposits or large grids. Herein is presented an attempt to solve this problem in short-term modeling cases, via the use of Multiple Random Walk Simulation. This algorithm combines kriging with the simulation of independent random walks in order to generate simulated scenarios much faster than via traditional simulation algorithms. A case study is presented to illustrate the application of the method in an iron mine. The Multiple Random Walk Simulation models were properly built, respecting the reproduction of both histogram and variograms. Also, the speed-up was compared with standard methods of geostatistical simulation and there was a considerable speed gain with Multiple Random Walk Simulation (3.39 to 5.65 times faster than the others). |
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Caixeta, Rafael MonizRibeiro, Diniz TamantiniCosta, Joao Felipe Coimbra Leite2017-07-12T02:30:17Z20172448-167Xhttp://hdl.handle.net/10183/163915001024360Geostatistical simulation comprises a variety of techniques which can help on the decision-making process for uncertainties. They allow the uncertainty assessment of function responses (which depend on the simulated inputs) commonly through a non-linear relationship (net present value, interest tax return, geometallurgical ore recovery…). However, one of their limitations is that running the simulation can take considerable processing time to be executed in large deposits or large grids. Herein is presented an attempt to solve this problem in short-term modeling cases, via the use of Multiple Random Walk Simulation. This algorithm combines kriging with the simulation of independent random walks in order to generate simulated scenarios much faster than via traditional simulation algorithms. A case study is presented to illustrate the application of the method in an iron mine. The Multiple Random Walk Simulation models were properly built, respecting the reproduction of both histogram and variograms. Also, the speed-up was compared with standard methods of geostatistical simulation and there was a considerable speed gain with Multiple Random Walk Simulation (3.39 to 5.65 times faster than the others).application/pdfengREM : international engineering journal. Ouro Preto, MG. Vol. 70, no. 2 (Apr./June 2017), p. 209-214GeoestatísticaMineraçãoSimulação computacionalGeostatisticsConditional simulationMiningShort-term modelingUsing multiple random walk simulation in short-term grade modelsinfo: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:UFRGSORIGINAL001024360.pdf001024360.pdfTexto completo (inglês)application/pdf1878926http://www.lume.ufrgs.br/bitstream/10183/163915/1/001024360.pdf71a38725055ee50ab05868a6c4b3a389MD51TEXT001024360.pdf.txt001024360.pdf.txtExtracted Texttext/plain17651http://www.lume.ufrgs.br/bitstream/10183/163915/2/001024360.pdf.txt227aa9a55593747e8223d9a5fe687cfaMD52THUMBNAIL001024360.pdf.jpg001024360.pdf.jpgGenerated Thumbnailimage/jpeg1997http://www.lume.ufrgs.br/bitstream/10183/163915/3/001024360.pdf.jpg1ae0c703624f8ad4f37704e963e24a70MD5310183/1639152024-04-27 06:08:02.35561oai:www.lume.ufrgs.br:10183/163915Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2024-04-27T09:08:02Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Using multiple random walk simulation in short-term grade models |
title |
Using multiple random walk simulation in short-term grade models |
spellingShingle |
Using multiple random walk simulation in short-term grade models Caixeta, Rafael Moniz Geoestatística Mineração Simulação computacional Geostatistics Conditional simulation Mining Short-term modeling |
title_short |
Using multiple random walk simulation in short-term grade models |
title_full |
Using multiple random walk simulation in short-term grade models |
title_fullStr |
Using multiple random walk simulation in short-term grade models |
title_full_unstemmed |
Using multiple random walk simulation in short-term grade models |
title_sort |
Using multiple random walk simulation in short-term grade models |
author |
Caixeta, Rafael Moniz |
author_facet |
Caixeta, Rafael Moniz Ribeiro, Diniz Tamantini Costa, Joao Felipe Coimbra Leite |
author_role |
author |
author2 |
Ribeiro, Diniz Tamantini Costa, Joao Felipe Coimbra Leite |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Caixeta, Rafael Moniz Ribeiro, Diniz Tamantini Costa, Joao Felipe Coimbra Leite |
dc.subject.por.fl_str_mv |
Geoestatística Mineração Simulação computacional |
topic |
Geoestatística Mineração Simulação computacional Geostatistics Conditional simulation Mining Short-term modeling |
dc.subject.eng.fl_str_mv |
Geostatistics Conditional simulation Mining Short-term modeling |
description |
Geostatistical simulation comprises a variety of techniques which can help on the decision-making process for uncertainties. They allow the uncertainty assessment of function responses (which depend on the simulated inputs) commonly through a non-linear relationship (net present value, interest tax return, geometallurgical ore recovery…). However, one of their limitations is that running the simulation can take considerable processing time to be executed in large deposits or large grids. Herein is presented an attempt to solve this problem in short-term modeling cases, via the use of Multiple Random Walk Simulation. This algorithm combines kriging with the simulation of independent random walks in order to generate simulated scenarios much faster than via traditional simulation algorithms. A case study is presented to illustrate the application of the method in an iron mine. The Multiple Random Walk Simulation models were properly built, respecting the reproduction of both histogram and variograms. Also, the speed-up was compared with standard methods of geostatistical simulation and there was a considerable speed gain with Multiple Random Walk Simulation (3.39 to 5.65 times faster than the others). |
publishDate |
2017 |
dc.date.accessioned.fl_str_mv |
2017-07-12T02:30:17Z |
dc.date.issued.fl_str_mv |
2017 |
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/10183/163915 |
dc.identifier.issn.pt_BR.fl_str_mv |
2448-167X |
dc.identifier.nrb.pt_BR.fl_str_mv |
001024360 |
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2448-167X 001024360 |
url |
http://hdl.handle.net/10183/163915 |
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. 70, no. 2 (Apr./June 2017), p. 209-214 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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