Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil

Detalhes bibliográficos
Autor(a) principal: Fonteles, Helano
Data de Publicação: 2022
Outros Autores: Veríssimo, César Ulisses
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/40955
Resumo: Geological modeling is the primary task of any exploratory geological investigation, and it is performed even during the mine development. In the study area, the Serra dos Quintos Formation hosts banded iron formations represented by assorted itabirites. The database used in this work was exploited from a geological databank which was structured to gather data and information depicted from an exhaustive exploratory drilling program. A survey was performed throughout the entire databank to collect the available thickness data values (measured in meters) from drill core logs that intersected the itabirite. Since structural heterogeneities can occur within these rocks, this study aims to identify such features. Geostatistical estimation and simulation methods were employed to map folded itabiritic beds based on thickness data accurately. Kriging estimators are often used for practical reasons; however, sometimes, the estimates can be smoothed and do not represent the entire original data range. Simulation algorithms can yield several stochastic images, but local accuracy cannot always be guaranteed. Simulated annealing was performed by adjusting the global statistics and preserving the local accuracy. We demonstrated that the banded iron formations’ thicker areas might correspond to the antiform fold as the dominant tectonic feature in the study area. Finally, we show that the simulated thickness map discloses the thicker mineralized spots. Meanwhile, the thinner ones may unveil intrinsic structural heterogeneities mainly observed at the limbs of the Bonito fold, where intensive deformation within the itabiritic layer was higher than expected. Concerning the mining issues, information obtained from the simulated thickness map could provide ancillary data to improve mining planning in the study area. 
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spelling Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern BrazilItabirites; Conditional simulation; Serra dos Quintos FormationGeological modeling is the primary task of any exploratory geological investigation, and it is performed even during the mine development. In the study area, the Serra dos Quintos Formation hosts banded iron formations represented by assorted itabirites. The database used in this work was exploited from a geological databank which was structured to gather data and information depicted from an exhaustive exploratory drilling program. A survey was performed throughout the entire databank to collect the available thickness data values (measured in meters) from drill core logs that intersected the itabirite. Since structural heterogeneities can occur within these rocks, this study aims to identify such features. Geostatistical estimation and simulation methods were employed to map folded itabiritic beds based on thickness data accurately. Kriging estimators are often used for practical reasons; however, sometimes, the estimates can be smoothed and do not represent the entire original data range. Simulation algorithms can yield several stochastic images, but local accuracy cannot always be guaranteed. Simulated annealing was performed by adjusting the global statistics and preserving the local accuracy. We demonstrated that the banded iron formations’ thicker areas might correspond to the antiform fold as the dominant tectonic feature in the study area. Finally, we show that the simulated thickness map discloses the thicker mineralized spots. Meanwhile, the thinner ones may unveil intrinsic structural heterogeneities mainly observed at the limbs of the Bonito fold, where intensive deformation within the itabiritic layer was higher than expected. Concerning the mining issues, information obtained from the simulated thickness map could provide ancillary data to improve mining planning in the study area. Universidade Federal do Rio de JaneiroCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESFonteles, HelanoVeríssimo, César Ulisses2022-07-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/4095510.11137/1982-3908_2022_45_40955Anuário do Instituto de Geociências; Vol 45 (2022)Anuário do Instituto de Geociências; Vol 45 (2022)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/40955/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40955/17156/*ref*/Angelim, L.A.A., Medeiros, V.C. & Nesi, J.R. 2006, Mapa Geológico do Estado do Rio Grande do Norte, Scale 1:500.000. Colored. CPRM-PGB, viewed 12 may 2019, <http://www.cprm.gov.br/publique/media/geologia_basica/cartografia_regional/mapa_rio_grande_norte.pdf>./*ref*/Ayalew, L., Reyk, G. & Busch, W. 2002, ‘Characterizing weathered rock masses - a geostatistical approach’, International Journal of Rock Mechanics & Mining Sciences, vol. 39, no. 1, pp. 105-14, DOI:10.1016/S1365-1609(02)00004-7./*ref*/Barbosa, I.G. 2013, ‘Mina do Bonito - Tipologia e geoquímica dos minérios de ferro, Jucurutu/RN - Brasil’. Master Thesis, Universidade Federal do Ceará, <http://www.repositorio.ufc.br/handle/riufc/21818>./*ref*/Boufassa, A. & Armstrong, M. 1989, ‘Comparison between different kriging estimators’, Mathematical Geology, vol. 21, no. 3, pp. 311-44, DOI:10.1007/bf00893694./*ref*/Caby, R., Sial, A.N., Arthaud, M.H. & Vauchez, A. 1991, ‘Crustal evolution and the Brasiliano orogeny in northeastern Brazil’, in R.D. Dallmeyer & J.P. Lecorché (eds), The West African Orogens and Circum-Atlantic Correlations, Springer-Verlag, Berlin, pp. 373-97./*ref*/Caby, R. Arthaud, M.H. & Archanjo, C.J. 1995, ‘Lithostratigraphy and petrostructural characterization of supracrustal units in the Brasiliano belt of Northeastern Brazil: geodynamics implications’, Journal of South American Earth Sciences, vol. 8, no. 3/4, pp. 235-46, DOI:10.1016/0895-9811(95)00011-4./*ref*/Cao, A., Jing, G., Dou, L., Wu, Y. & Zhang, C. 2018, ‘Statistical analysis of distribution patterns of coal seams in fold zones in Northwest China’, International Journal of Mining Science and Technology, vol. 28, no. 5, pp. 819-28, DOI:10.1016/j.ijmst.2018.09.003./*ref*/CMRP. 2000, Geostatistical Modelling Software – GeoMS, Superior Technical Institute, University of Lisbon, viewed 13 May 2019, <https://sites.google.com/site/cmrpsoftware/geoms>./*ref*/Deutsch, C.V. & Journel, A.G. 1992, GSLIB: Geostatistical software library and user´s guide, Oxford University Press, New York./*ref*/Deutsch, C.V. 1992, ‘Annealing techniques applied to reservoir modeling and the integration of geological and engineering (well test) data’, PhD Thesis, Stanford University, <https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.330.7685&rep=rep1&type=pdf>./*ref*/Deutsch, C.V. & Cockerham, P.W. 1994, ‘Practical considerations in the application of simulated annealing to stochastic simulation’, Mathematical Geology, vol. 26, no. 1, pp. 67-82, DOI:10.1007/bf02065876>./*ref*/Fonteles, H., Pereira, H., Rocha, C. & Veríssimo, C. 2019a, ‘Mapping geochemical anomalies through principal components analysis in BIF Mines: An approach referring to the Bonito Mine, Northeastern Brazil’, in D. Doronzo, E. Schigaro, J. Armstrong-Altril, & B. Zoheir (eds), Petrogenesis and Exploration of the Earth’s Interior. Advances in Science, Technology & Innovation, Springer Nature, Cham, pp. 245-47, DOI:10.1007/978-3-030-01575-6_59./*ref*/Fonteles, H.R.N., Pereira, H.G. & Veríssimo, C.U.V. 2019b, ‘Weathering conditions evaluation of banded iron formations of Bonito mine (Northeastern Brazil) based on coupled cluster-correspondence analysis’, Anuário do Instituto de Geociências, vol. 42, no. 2, pp. 86-99, DOI:10.11137/2019_2_86_99./*ref*/Fonteles, H.R.N., Veríssimo, C.U.V., Pereira, H.G. & Barbosa, I.G. 2020, ‘Hybrid multivariate typological model for the banded iron formations from the Bonito mine, Northeastern Brazil’, Applied Geochemistry, vol. 123, pp. 1-13. DOI:10.1016/j.apgeochem.2020.104779./*ref*/Geman, S. & Geman, D. 1984, ‘Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images’, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-41, DOI:10.1109/tpami.1984.4767596./*ref*/Goovaerts, P. 1997, Geostatistics for Natural Resources. Oxford University Press, New York./*ref*/Goovaerts, P. 1998, Accounting for estimation optimality criteria in simulated annealing. Mathematical Geology, vol. 30, no. 5, pp. 511-34, DOI:10.1023/a:1021738027334./*ref*/Grijp, Y. & Minnitt, R.C.A. 2015, ‘Application of direct sampling multi-point statistic and sequential Gaussian simulation algorithms for modeling uncertainty in gold deposits’, The Journal of The Southern African Institute of Mining and Metallurgy, vol. 115, pp. 73-85, <http://www.scielo.org.za/pdf/jsaimm/v115n1/14.pdf>/*ref*/Hackspacher, P.C., Dantas, E.L., Brito Neves, B.B. & Legrand, J.M. 1997, ‘Northwestern overthrusting and related lateral escape during Brasiliano orogeny north of the Patos lineament, Borborema Province, Northeastern Brazil’, International Geology Reviews, vol. 39, no. 7, pp. 609-20, DOI:10.1080/00206819709465291./*ref*/Isaaks, E.H. & Srivastava, R.M. 1989, An Introduction to Applied Geostatistics. Oxford University Press, New York./*ref*/Jiafu, T., Fu, H. & Yu, Z. 1987, ‘Stratigraphy, type, formation conditions of the Late Precambrian banded iron ores in South China’, Chinese Journal of Geochemistry, vol. 6, no. 4, pp. 331-41, DOI:10.1007/BF02872262./*ref*/Journel, A.G. 1974, ‘Geostatistics for conditional simulation of ore bodies’, Economic Geology, vol. 69, no. 5, pp. 673-87, DOI:10.2113/gsecongeo.69.5.673./*ref*/Journel, A.G. & Huijbregts, C.J. 1978, Mining Geostatistics, Academic Press, London./*ref*/Journel, A.G. 1994, ‘Modeling uncertainty: Some conceptual thoughts’, in R. Dimitrakopoulos (ed.), Geostatistics for the Next Century, Kluwer Academic, Dordrecht, pp. 30-43, DOI:10.1007/978-94-011-0824-9_5./*ref*/Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. 1983, ‘Optimization by simulated annealing’, Science, vol. 220, no. 4598, pp. 671-80, DOI:10.1126/science.220.4598.671./*ref*/Krige, D.G. 1951, ‘A statistical approach to some basic mine valuation problems on the Witwatersrand’, Journal of Chemical Metallurgy and Mineralogical Society of South Africa, vol. 52, no. 6, pp. 119-39, <https://hdl.handle.net/10520/AJA0038223X_4792>/*ref*/Leuangthong, O., Mclennan, J.A. & Deutsch, C.V. 2004, ‘Minimum acceptance criteria for geostatistical realizations’, Natural Resources Research, vol. 13, no. 3, pp. 131-41, DOI:10.1023/b:narr.0000046916.91703.bb./*ref*/Mallet, J.L. 2002, Geomodelling. Oxford University Press, New York./*ref*/Matheron, G. 1963, Traité de Géoestatisque Appliquée: Tome II – Le Krigeage. Bulletin du Bureau de Recherches Géologique et Minières. Editions Technip, Paris./*ref*/Matheron, G. 1965, Les Variables Régionalisées et leur Estimation. Masson et Cie Éditeurs, Paris./*ref*/Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. & Teller, E. 1953, ‘Equation of state calculations by fast machines’, The Journal of Chemical Physics, vol. 21, pp. 1087-92, DOI:10.1063/1.1699114./*ref*/Paravarzar, S., Emery, X. & Madani, N. 2015, ‘Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits’, Comptes Rendus Geoscience, vol. 347, no. 2, pp. 84-93, DOI:10.1016/j.crte.2015.05.008./*ref*/Soares, A. 2001, 'Direct sequential simulation and cosimulation, Mathematical Geology, vol. 33, no. 8, pp. 911-926, DOI:10.1023/a:1012246006212./*ref*/Sá, E.F.J. 1984, ‘Geologia da região do Seridó: Reavaliação dos dados’, Simpósio De Geologia do Nordeste 1984, Natal, RN, pp. 278-96./*ref*/Van Schmus, W. R., Brito Neves, B.B., Williams, I.S., Hackspacher, P.C., Fetter, A.H., Dantas, E.L. & Babinski, M. 2003, ‘The Seridó Group of NE Brazil, a late Neoproterozoic pre- to syn-collisional basin in West Gondwana: Insights from SHRIMP U-Pb detrital zircon ages and Sm-Nd crustal residence (TDM) ages’, Precambrian Research, vol. 127, no. 4, pp 287-327, DOI:10.1016/s0301-9268(03)00197-9./*ref*/Yamamoto, J.K. 2000, ‘An alternative measure of the reliability of ordinary kriging estimates’, Mathematical Geology, vol. 32, no. 4, pp. 489-509, DOI:10.1023/a:1007577916868./*ref*/Yamamoto, J.K. 2008, ‘Estimation or simulation? That is the question’, Computational Geosciences, vol. 12, pp. 573-91, DOI:10.1007/s10596-008-9096-8>./*ref*/Zhao, S., Zhou, Y., Wang, M., Xin, X. & Chen, C. 2014, ‘Thickness, porosity, and permeability prediction: Comparative studies and application of the geostatistical modeling in an oil field’, Environmental Systems Research, vol. 3, no. 1, pp. 7-24, DOI:10.1186/2193-2697-3-7.Copyright (c) 2022 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2022-12-28T20:46:28Zoai:www.revistas.ufrj.br:article/40955Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2022-12-28T20:46:28Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
title Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
spellingShingle Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
Fonteles, Helano
Itabirites; Conditional simulation; Serra dos Quintos Formation
title_short Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
title_full Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
title_fullStr Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
title_full_unstemmed Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
title_sort Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil
author Fonteles, Helano
author_facet Fonteles, Helano
Veríssimo, César Ulisses
author_role author
author2 Veríssimo, César Ulisses
author2_role author
dc.contributor.none.fl_str_mv Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
dc.contributor.author.fl_str_mv Fonteles, Helano
Veríssimo, César Ulisses
dc.subject.por.fl_str_mv Itabirites; Conditional simulation; Serra dos Quintos Formation
topic Itabirites; Conditional simulation; Serra dos Quintos Formation
description Geological modeling is the primary task of any exploratory geological investigation, and it is performed even during the mine development. In the study area, the Serra dos Quintos Formation hosts banded iron formations represented by assorted itabirites. The database used in this work was exploited from a geological databank which was structured to gather data and information depicted from an exhaustive exploratory drilling program. A survey was performed throughout the entire databank to collect the available thickness data values (measured in meters) from drill core logs that intersected the itabirite. Since structural heterogeneities can occur within these rocks, this study aims to identify such features. Geostatistical estimation and simulation methods were employed to map folded itabiritic beds based on thickness data accurately. Kriging estimators are often used for practical reasons; however, sometimes, the estimates can be smoothed and do not represent the entire original data range. Simulation algorithms can yield several stochastic images, but local accuracy cannot always be guaranteed. Simulated annealing was performed by adjusting the global statistics and preserving the local accuracy. We demonstrated that the banded iron formations’ thicker areas might correspond to the antiform fold as the dominant tectonic feature in the study area. Finally, we show that the simulated thickness map discloses the thicker mineralized spots. Meanwhile, the thinner ones may unveil intrinsic structural heterogeneities mainly observed at the limbs of the Bonito fold, where intensive deformation within the itabiritic layer was higher than expected. Concerning the mining issues, information obtained from the simulated thickness map could provide ancillary data to improve mining planning in the study area. 
publishDate 2022
dc.date.none.fl_str_mv 2022-07-15
dc.type.none.fl_str_mv

dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/40955
10.11137/1982-3908_2022_45_40955
url https://revistas.ufrj.br/index.php/aigeo/article/view/40955
identifier_str_mv 10.11137/1982-3908_2022_45_40955
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/40955/pdf
https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40955/17156
/*ref*/Angelim, L.A.A., Medeiros, V.C. & Nesi, J.R. 2006, Mapa Geológico do Estado do Rio Grande do Norte, Scale 1:500.000. Colored. CPRM-PGB, viewed 12 may 2019, <http://www.cprm.gov.br/publique/media/geologia_basica/cartografia_regional/mapa_rio_grande_norte.pdf>.
/*ref*/Ayalew, L., Reyk, G. & Busch, W. 2002, ‘Characterizing weathered rock masses - a geostatistical approach’, International Journal of Rock Mechanics & Mining Sciences, vol. 39, no. 1, pp. 105-14, DOI:10.1016/S1365-1609(02)00004-7.
/*ref*/Barbosa, I.G. 2013, ‘Mina do Bonito - Tipologia e geoquímica dos minérios de ferro, Jucurutu/RN - Brasil’. Master Thesis, Universidade Federal do Ceará, <http://www.repositorio.ufc.br/handle/riufc/21818>.
/*ref*/Boufassa, A. & Armstrong, M. 1989, ‘Comparison between different kriging estimators’, Mathematical Geology, vol. 21, no. 3, pp. 311-44, DOI:10.1007/bf00893694.
/*ref*/Caby, R., Sial, A.N., Arthaud, M.H. & Vauchez, A. 1991, ‘Crustal evolution and the Brasiliano orogeny in northeastern Brazil’, in R.D. Dallmeyer & J.P. Lecorché (eds), The West African Orogens and Circum-Atlantic Correlations, Springer-Verlag, Berlin, pp. 373-97.
/*ref*/Caby, R. Arthaud, M.H. & Archanjo, C.J. 1995, ‘Lithostratigraphy and petrostructural characterization of supracrustal units in the Brasiliano belt of Northeastern Brazil: geodynamics implications’, Journal of South American Earth Sciences, vol. 8, no. 3/4, pp. 235-46, DOI:10.1016/0895-9811(95)00011-4.
/*ref*/Cao, A., Jing, G., Dou, L., Wu, Y. & Zhang, C. 2018, ‘Statistical analysis of distribution patterns of coal seams in fold zones in Northwest China’, International Journal of Mining Science and Technology, vol. 28, no. 5, pp. 819-28, DOI:10.1016/j.ijmst.2018.09.003.
/*ref*/CMRP. 2000, Geostatistical Modelling Software – GeoMS, Superior Technical Institute, University of Lisbon, viewed 13 May 2019, <https://sites.google.com/site/cmrpsoftware/geoms>.
/*ref*/Deutsch, C.V. & Journel, A.G. 1992, GSLIB: Geostatistical software library and user´s guide, Oxford University Press, New York.
/*ref*/Deutsch, C.V. 1992, ‘Annealing techniques applied to reservoir modeling and the integration of geological and engineering (well test) data’, PhD Thesis, Stanford University, <https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.330.7685&rep=rep1&type=pdf>.
/*ref*/Deutsch, C.V. & Cockerham, P.W. 1994, ‘Practical considerations in the application of simulated annealing to stochastic simulation’, Mathematical Geology, vol. 26, no. 1, pp. 67-82, DOI:10.1007/bf02065876>.
/*ref*/Fonteles, H., Pereira, H., Rocha, C. & Veríssimo, C. 2019a, ‘Mapping geochemical anomalies through principal components analysis in BIF Mines: An approach referring to the Bonito Mine, Northeastern Brazil’, in D. Doronzo, E. Schigaro, J. Armstrong-Altril, & B. Zoheir (eds), Petrogenesis and Exploration of the Earth’s Interior. Advances in Science, Technology & Innovation, Springer Nature, Cham, pp. 245-47, DOI:10.1007/978-3-030-01575-6_59.
/*ref*/Fonteles, H.R.N., Pereira, H.G. & Veríssimo, C.U.V. 2019b, ‘Weathering conditions evaluation of banded iron formations of Bonito mine (Northeastern Brazil) based on coupled cluster-correspondence analysis’, Anuário do Instituto de Geociências, vol. 42, no. 2, pp. 86-99, DOI:10.11137/2019_2_86_99.
/*ref*/Fonteles, H.R.N., Veríssimo, C.U.V., Pereira, H.G. & Barbosa, I.G. 2020, ‘Hybrid multivariate typological model for the banded iron formations from the Bonito mine, Northeastern Brazil’, Applied Geochemistry, vol. 123, pp. 1-13. DOI:10.1016/j.apgeochem.2020.104779.
/*ref*/Geman, S. & Geman, D. 1984, ‘Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images’, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-41, DOI:10.1109/tpami.1984.4767596.
/*ref*/Goovaerts, P. 1997, Geostatistics for Natural Resources. Oxford University Press, New York.
/*ref*/Goovaerts, P. 1998, Accounting for estimation optimality criteria in simulated annealing. Mathematical Geology, vol. 30, no. 5, pp. 511-34, DOI:10.1023/a:1021738027334.
/*ref*/Grijp, Y. & Minnitt, R.C.A. 2015, ‘Application of direct sampling multi-point statistic and sequential Gaussian simulation algorithms for modeling uncertainty in gold deposits’, The Journal of The Southern African Institute of Mining and Metallurgy, vol. 115, pp. 73-85, <http://www.scielo.org.za/pdf/jsaimm/v115n1/14.pdf>
/*ref*/Hackspacher, P.C., Dantas, E.L., Brito Neves, B.B. & Legrand, J.M. 1997, ‘Northwestern overthrusting and related lateral escape during Brasiliano orogeny north of the Patos lineament, Borborema Province, Northeastern Brazil’, International Geology Reviews, vol. 39, no. 7, pp. 609-20, DOI:10.1080/00206819709465291.
/*ref*/Isaaks, E.H. & Srivastava, R.M. 1989, An Introduction to Applied Geostatistics. Oxford University Press, New York.
/*ref*/Jiafu, T., Fu, H. & Yu, Z. 1987, ‘Stratigraphy, type, formation conditions of the Late Precambrian banded iron ores in South China’, Chinese Journal of Geochemistry, vol. 6, no. 4, pp. 331-41, DOI:10.1007/BF02872262.
/*ref*/Journel, A.G. 1974, ‘Geostatistics for conditional simulation of ore bodies’, Economic Geology, vol. 69, no. 5, pp. 673-87, DOI:10.2113/gsecongeo.69.5.673.
/*ref*/Journel, A.G. & Huijbregts, C.J. 1978, Mining Geostatistics, Academic Press, London.
/*ref*/Journel, A.G. 1994, ‘Modeling uncertainty: Some conceptual thoughts’, in R. Dimitrakopoulos (ed.), Geostatistics for the Next Century, Kluwer Academic, Dordrecht, pp. 30-43, DOI:10.1007/978-94-011-0824-9_5.
/*ref*/Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. 1983, ‘Optimization by simulated annealing’, Science, vol. 220, no. 4598, pp. 671-80, DOI:10.1126/science.220.4598.671.
/*ref*/Krige, D.G. 1951, ‘A statistical approach to some basic mine valuation problems on the Witwatersrand’, Journal of Chemical Metallurgy and Mineralogical Society of South Africa, vol. 52, no. 6, pp. 119-39, <https://hdl.handle.net/10520/AJA0038223X_4792>
/*ref*/Leuangthong, O., Mclennan, J.A. & Deutsch, C.V. 2004, ‘Minimum acceptance criteria for geostatistical realizations’, Natural Resources Research, vol. 13, no. 3, pp. 131-41, DOI:10.1023/b:narr.0000046916.91703.bb.
/*ref*/Mallet, J.L. 2002, Geomodelling. Oxford University Press, New York.
/*ref*/Matheron, G. 1963, Traité de Géoestatisque Appliquée: Tome II – Le Krigeage. Bulletin du Bureau de Recherches Géologique et Minières. Editions Technip, Paris.
/*ref*/Matheron, G. 1965, Les Variables Régionalisées et leur Estimation. Masson et Cie Éditeurs, Paris.
/*ref*/Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. & Teller, E. 1953, ‘Equation of state calculations by fast machines’, The Journal of Chemical Physics, vol. 21, pp. 1087-92, DOI:10.1063/1.1699114.
/*ref*/Paravarzar, S., Emery, X. & Madani, N. 2015, ‘Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits’, Comptes Rendus Geoscience, vol. 347, no. 2, pp. 84-93, DOI:10.1016/j.crte.2015.05.008.
/*ref*/Soares, A. 2001, 'Direct sequential simulation and cosimulation, Mathematical Geology, vol. 33, no. 8, pp. 911-926, DOI:10.1023/a:1012246006212.
/*ref*/Sá, E.F.J. 1984, ‘Geologia da região do Seridó: Reavaliação dos dados’, Simpósio De Geologia do Nordeste 1984, Natal, RN, pp. 278-96.
/*ref*/Van Schmus, W. R., Brito Neves, B.B., Williams, I.S., Hackspacher, P.C., Fetter, A.H., Dantas, E.L. & Babinski, M. 2003, ‘The Seridó Group of NE Brazil, a late Neoproterozoic pre- to syn-collisional basin in West Gondwana: Insights from SHRIMP U-Pb detrital zircon ages and Sm-Nd crustal residence (TDM) ages’, Precambrian Research, vol. 127, no. 4, pp 287-327, DOI:10.1016/s0301-9268(03)00197-9.
/*ref*/Yamamoto, J.K. 2000, ‘An alternative measure of the reliability of ordinary kriging estimates’, Mathematical Geology, vol. 32, no. 4, pp. 489-509, DOI:10.1023/a:1007577916868.
/*ref*/Yamamoto, J.K. 2008, ‘Estimation or simulation? That is the question’, Computational Geosciences, vol. 12, pp. 573-91, DOI:10.1007/s10596-008-9096-8>.
/*ref*/Zhao, S., Zhou, Y., Wang, M., Xin, X. & Chen, C. 2014, ‘Thickness, porosity, and permeability prediction: Comparative studies and application of the geostatistical modeling in an oil field’, Environmental Systems Research, vol. 3, no. 1, pp. 7-24, DOI:10.1186/2193-2697-3-7.
dc.rights.driver.fl_str_mv Copyright (c) 2022 Anuário do Instituto de Geociências
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rights_invalid_str_mv Copyright (c) 2022 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
dc.source.none.fl_str_mv Anuário do Instituto de Geociências; Vol 45 (2022)
Anuário do Instituto de Geociências; Vol 45 (2022)
1982-3908
0101-9759
reponame:Anuário do Instituto de Geociências (Online)
instname:Universidade Federal do Rio de Janeiro (UFRJ)
instacron:UFRJ
instname_str Universidade Federal do Rio de Janeiro (UFRJ)
instacron_str UFRJ
institution UFRJ
reponame_str Anuário do Instituto de Geociências (Online)
collection Anuário do Instituto de Geociências (Online)
repository.name.fl_str_mv Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)
repository.mail.fl_str_mv anuario@igeo.ufrj.br||
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