Uncertainty estimate in resources assessment: A geostatistical contribution

For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages...

Full description

Saved in:
Bibliographic Details
Published in:Natural resources research (New York, N.Y.) N.Y.), 2004-03, Vol.13 (1), p.1-15
Main Authors: DE SOUZA, Luis Eduardo, COSTA, Joao Felipe C. L, KOPPE, Jair C
Format: Article
Language:English
Subjects:
01 COAL, LIGNITE, AND PEAT
BOREHOLES
BRAZIL
COAL DEPOSITS
COAL RESERVES
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geology
Geostatistics
KRIGING
Metallic and non-metallic deposits
Mineral deposits
MINERAL INDUSTRY
Mineral resources
MINERALIZATION
MINERALS
MINING
Mining industry
Quality
Reserves
RESOURCE POTENTIAL
Robustness (mathematics)
SIMULATION
Spatial data
STOCHASTIC PROCESSES
Uncertainty
Variance
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Altenatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit.
ISSN:1520-7439
1573-8981
DOI:10.1023/B:NARR.0000023303.03402.c8