Montería. Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies

Adriano Gomes  Morais de Oliveira; Luis Eduardo de Souza; Ricardo Jose Hundelshaussen Rubio; Luciana  Arnt Abichequer; Vanessa Cerqueira Koppe; João Felipe Coimbra Leite Costa

doi:10.24857/rgsa.v20n2-003

Montería. Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies

Adriano Gomes Morais de Oliveira
, Luis Eduardo de Souza (Co-author)
, Ricardo Jose Hundelshaussen Rubio (Co-author)
, Luciana Arnt Abichequer (Co-author)
, Vanessa Cerqueira Koppe (Co-author)
, João Felipe Coimbra Leite Costa (Co-author)

Research output: Contribution to scientific journal › Article in an indexed scientific journal › peer-review

Abstract

This study evaluates the performance of two mineral resource classification methodologies, the Maximum Estimation Error (MEE) and Superkriging, to assess the uncertainty associated with thickness (m) in a coal deposit, considering annual and quarterly production volumes and different virtual sampling grids conditioned to the original data.

Original language	English
Pages (from-to)	1-17
Number of pages	17
Journal	Revista de Gestao Social e Ambiental
Volume	20
Issue number	2
DOIs	https://doi.org/10.24857/rgsa.v20n2-003
State	Published - 9 Feb 2026

Types Minciencias

Science communication articles

Access to Document

10.24857/rgsa.v20n2-003

Cite this

Morais de Oliveira, A. G., de Souza, L. E., Hundelshaussen Rubio, R. J., Arnt Abichequer, L., Koppe, V. C., & Coimbra Leite Costa, J. F. (2026). Montería. Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies. Revista de Gestao Social e Ambiental, 20(2), 1-17. https://doi.org/10.24857/rgsa.v20n2-003

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abstract = "This study evaluates the performance of two mineral resource classification methodologies, the Maximum Estimation Error (MEE) and Superkriging, to assess the uncertainty associated with thickness (m) in a coal deposit, considering annual and quarterly production volumes and different virtual sampling grids conditioned to the original data.",

keywords = "Resource Classification, Maximum Estimation Error, Superkriging, Sampling Grids",

author = "\{Morais de Oliveira\}, \{Adriano Gomes\} and \{de Souza\}, \{Luis Eduardo\} and \{Hundelshaussen Rubio\}, \{Ricardo Jose\} and \{Arnt Abichequer\}, Luciana and Koppe, \{Vanessa Cerqueira\} and \{Coimbra Leite Costa\}, \{Jo{\~a}o Felipe\}",

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language = "Ingl{\'e}s",

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Morais de Oliveira, AG, de Souza, LE, Hundelshaussen Rubio, RJ, Arnt Abichequer, L, Koppe, VC & Coimbra Leite Costa, JF 2026, 'Montería. Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies', Revista de Gestao Social e Ambiental, vol. 20, no. 2, pp. 1-17. https://doi.org/10.24857/rgsa.v20n2-003

Montería. Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies. / Morais de Oliveira, Adriano Gomes ; de Souza, Luis Eduardo (Co-author); Hundelshaussen Rubio, Ricardo Jose (Co-author) et al.
In: Revista de Gestao Social e Ambiental, Vol. 20, No. 2, 09.02.2026, p. 1-17.

Research output: Contribution to scientific journal › Article in an indexed scientific journal › peer-review

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AU - Morais de Oliveira, Adriano Gomes

AU - de Souza, Luis Eduardo

AU - Hundelshaussen Rubio, Ricardo Jose

AU - Arnt Abichequer, Luciana

AU - Koppe, Vanessa Cerqueira

AU - Coimbra Leite Costa, João Felipe

PY - 2026/2/9

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AB - This study evaluates the performance of two mineral resource classification methodologies, the Maximum Estimation Error (MEE) and Superkriging, to assess the uncertainty associated with thickness (m) in a coal deposit, considering annual and quarterly production volumes and different virtual sampling grids conditioned to the original data.

KW - Resource Classification

KW - Maximum Estimation Error

KW - Superkriging

KW - Sampling Grids

U2 - 10.24857/rgsa.v20n2-003

DO - 10.24857/rgsa.v20n2-003

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SN - 1981-982X

VL - 20

SP - 1

EP - 17

JO - Revista de Gestao Social e Ambiental

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