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Moreno, W. Emilio G.; Leães, Áttila; Bassani, Marcel Antonio Arcari; Marques, Diego; Costa, João Felipe Coimbra Leite (2025) Machine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models. Mining, Metallurgy & Exploration, 42 (5). doi:10.1007/s42461-025-01335-9

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Reference TypeJournal (article/letter/editorial)
TitleMachine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models
JournalMining, Metallurgy & Exploration
AuthorsMoreno, W. Emilio G.Author
Leães, ÁttilaAuthor
Bassani, Marcel Antonio ArcariAuthor
Marques, DiegoAuthor
Costa, João Felipe Coimbra LeiteAuthor
Year2025 (October)Volume42
Issue5
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s42461-025-01335-9Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID19088000Long-form Identifiermindat:1:5:19088000:4
GUID0
Full ReferenceMoreno, W. Emilio G.; Leães, Áttila; Bassani, Marcel Antonio Arcari; Marques, Diego; Costa, João Felipe Coimbra Leite (2025) Machine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models. Mining, Metallurgy & Exploration, 42 (5). doi:10.1007/s42461-025-01335-9
Plain TextMoreno, W. Emilio G.; Leães, Áttila; Bassani, Marcel Antonio Arcari; Marques, Diego; Costa, João Felipe Coimbra Leite (2025) Machine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models. Mining, Metallurgy & Exploration, 42 (5). doi:10.1007/s42461-025-01335-9
In(2025, October) Mining, Metallurgy & Exploration Vol. 42 (5). Springer Science and Business Media LLC

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These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

Lipton I, Horton JA (2014) Measurement of bulk density for resource estimation – methods, guidelines and quality control. Miner Resour Ore Reserve Estim.AusIMM Guide Good Pract
Arseneau G (2013) Estimating bulk density for mineral resource reporting.https://www.srk.com/en/publications/estimating-bulk-density-for-mineral-resource-reporting. Accessed 14 Mar 2023
African Rainbow Minerals (2020) Mineral resources and reserves-2020
Fortescue Metals Group Ltd (2022) Mineral resources and ore reserves
Red Hill Iron (2015) Mineral resource estimate for Red Hill Iron Ore.
RioTinto (2023) Mineral resources and ore reserve updates: annual report 2023
AngloAmerican (2023) Ore reserves (and saleable product) and mineral resources
VALE (2021) Technical report summary - Serra Azul
DMT Consulting (2020) Mineral resource estimate
Not Yet Imported: - journal-article : 10.1080/25726668.2021.1876481

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Sinclair AJ, Blackwell GH (2006) Applied mineral inventory estimation. Cambridge University Press
Pevely S (2001) Ore reserve, grade control and mine/mill reconciliation practices at McArthur River Mine, NT. Miner. Resour. Ore Reserve Estim. – AusIMM Guide Good Pract
Braga D de M (2019) Técnicas de análises de densidade e porosidade de minério de ferro por cálculo normativo mineralógico, microtomografia computadorizada, permoporosimetria e picnometria clássica : um estudo comparativo entre os métodos. Iron ore density and porosity analysis techniques by mineralological normative calculation, computer microtomography, permoporosimetry and classical picnometry : a comparative study between methods
Journel A, Huijbregts CJ (1978) Mining geostatistics / A.G. Journel and Ch.J. Huijbregts. Academic Press
Not Yet Imported: - edited-book : 10.1093/oso/9780195115383.001.0001

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1007/s10462-023-10500-9

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Cotrina Teatino MAC, Marquina Araujo JJM, Mamani-Quispe JN (2025) Application of artificial neural networks for the categorization of mineral resources in a copper deposit in Peru. World J Eng. https://doi.org/10.1108/WJE-01-2025-0004
Not Yet Imported: Mathematical Geosciences - journal-article : 10.1007/s11004-021-09971-9

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Avalos S, Ortiz JM (2019) Geological modeling using a recursive convolutional neural networks approach. https://doi.org/10.48550/arXiv.1904.12190
Not Yet Imported: - journal-article : 10.1007/s11004-021-09969-3

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1590/0370-44672016710007

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Davila L, Deutsch C (2022) Cokriging with unequally sampled data. https://geostatisticslessons.com/lessons/cokrigingunequal. Accessed 14 Jun 2024
Minnitt R, Deutsch C (2014) Cokriging for optimal mineral resource estimates in mining operations. J S Afr Inst Min Metall 114:189–203
Delhomme J (1976) Applications de la théorie des variables régionalisées dans les sciences de l’eau (variabilité spatiale des grandeurs hydroclimatiques et hydrogéologiques et précision de leur connaissance)
Not Yet Imported: - posted-content : 10.21203/rs.3.rs-2557618/v1

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Zeni MA (2019) Análise do desempenho da krigagem com variância do erro de medida na presença de erros amostrais e valores extremos
Silva VM, Coimbra Costa Leite JF, Deutsch CV (2025) Kriging data with measurement error: a review and a generalized approach. Appl Earth Sci 134:5–17
Matheron G (1969) Le krigeage universel. Cah. Cent. Morphol. Math. 1
Not Yet Imported: Land Degradation & Development - journal-article : 10.1002/ldr.998

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1002/joc.1913

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Bezzi M, Vitti A (2011) A comparison of some kriging interpolation methods for the production of solar radiation maps
Isaaks EH, Srivastava RM (1989) Applied geostatistics. Oxford University Press
Yang D, Deutsch CV (2019) Aggregating variables into a super secondary variable
Barnett RM, Manchuk JG, Deutsch CV (2014) Projection Pursuit Multivar Transform Math Geosci 46:337–359
Wilde B, Deutsch CV (2005) A short note on the comparison of kriging and the average of simulated realizations
Gallatin K, Albon C (2023) Machine learning with python cookbook. O’Reilly Media, Inc
Not Yet Imported: - book-chapter : 10.1007/978-3-031-33342-2_9

If you would like this item imported into the Digital Library, please contact us quoting Book ID 9783031333415
Not Yet Imported: - journal-article : 10.1023/A:1022627411411

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Satapathy SK, Dehuri S, Jagadev AK, Mishra S (2019) Chapter 1 - Introduction. In: Satapathy SK, Dehuri S, Jagadev AK, Mishra S (eds) EEG Brain Signal Classif. Academic Press, Epileptic Seizure Disord. Detect, pp 1–25
Abirami S, Chitra P (2020) Chapter Fourteen - Energy-efficient edge based real-time healthcare support system. In: Raj P, Evangeline P (eds) Adv. Elsevier, Comput, pp 339–368
Singhal A, Sharma DK (2023) Chapter 3 - Voice signal-based disease diagnosis using IoT and learning algorithms for healthcare. In: Chakraborty C, Pani SK, Abdul Ahad M, Xin Q (eds) Implement. Academic Press, Smart Healthc. Syst. Using AI IoT Blockchain, pp 59–81
Not Yet Imported: Data Science for Genomics - book-chapter : 10.1016/B978-0-323-98352-5.00001-X

If you would like this item imported into the Digital Library, please contact us quoting Book ID 9780323983525
Misra S, Li H (2020) Chapter 9 - Noninvasive fracture characterization based on the classification of sonic wave travel times. In: Misra S, Li H, He J (eds) Mach. Gulf Professional Publishing, Learn Subsurf Charact, pp 243–287
Not Yet Imported: Automation in Construction - journal-article : 10.1016/j.autcon.2023.104767

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Devi SS, Solanki VK, Laskar RH (2020) Chapter 6 - Recent advances on big data analysis for malaria prediction and various diagnosis methodologies. In: Balas VE, Solanki VK, Kumar R, Khari M (eds) Handb. Academic Press, Data Sci. Approaches Biomed. Eng, pp 153–184
Not Yet Imported: Fundamentals of Applied Probability and Random Processes - book-chapter : 10.1016/B978-0-12-800852-2.00012-2

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Yang X-S (2019) 2 - Mathematical foundations. In: Yang X-S (ed) Introd. Academic Press, Algorithms Data Min. Mach. Learn, pp 19–43
Babak O, Deutsch CV (2007) Comparison of cokriging with an LMC versus the intrinsic model


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