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

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-autor)
, Ricardo Jose Hundelshaussen Rubio (Co-autor)
, Luciana Arnt Abichequer (Co-autor)
, Vanessa Cerqueira Koppe (Co-autor)
, João Felipe Coimbra Leite Costa (Co-autor)

Producción científica: Contribución a una revista científica › Artículo en revista científica indexada › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Páginas (desde-hasta)	1-17
Número de páginas	17
Publicación	Revista de Gestao Social e Ambiental
Volumen	20
N.º	2
DOI	https://doi.org/10.24857/rgsa.v20n2-003
Estado	Publicada - 9 feb. 2026

Palabras clave

Resource Classification
Maximum Estimation Error
Superkriging
Sampling Grids

Tipos de Productos Minciencias

Artículos de divulgación

Acceder al documento

10.24857/rgsa.v20n2-003

Citar esto

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title = "Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies",

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\}",

year = "2026",

month = feb,

day = "9",

doi = "10.24857/rgsa.v20n2-003",

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

volume = "20",

pages = "1--17",

journal = "Revista de Gestao Social e Ambiental",

issn = "1981-982X",

publisher = "ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao",

number = "2",

}

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-autor); Hundelshaussen Rubio, Ricardo Jose (Co-autor) et al.
En: Revista de Gestao Social e Ambiental, Vol. 20, N.º 2, 09.02.2026, p. 1-17.

Producción científica: Contribución a una revista científica › Artículo en revista científica indexada › revisión exhaustiva

TY - JOUR

T1 - Uncertainty Assessment in Coal Resource Classification: a Comparison of Maximum Estimation Error and Superkriging Methodologies

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

Y1 - 2026/2/9

N2 - 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.

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

M3 - Artículo en revista científica indexada

SN - 1981-982X

VL - 20

SP - 1

EP - 17

JO - Revista de Gestao Social e Ambiental

JF - Revista de Gestao Social e Ambiental

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