123
Often overlooked local variability of metal grades, when allocating blocks to resource categories, can potentially be used as an additional criterion for an assignment to an Indicated
category. A resource geologist could simply calculate coefficients of variations of grades in
large panels and link them to blocks located in those panels. Blocks originally assigned to an
Inferred category and located in low grade variability areas may be re-assigned to an Indicated category. On the other hand, blocks located in the high grade variability environment
can be considered for re-assignment to an Inferred category.
There are more advanced approaches than those presented here. For example, Pinto (2015,
2016) considered the calculation of confidence intervals of the panel as a moving window
formulation, thereby allowing for smoother boundaries between classification categories.
Silva et al. (2018) presented a framework to place additional drill holes subject to physical
constraints (location, drilling angles, depth limitations) and budget considerations. These
approaches, while more advanced and sophisticated, have yet to become common practice in
the mining industry. They also require a degree of comfort with advanced geostatistics and
the development of prototype software for implementation, that a general practitioner in the
mining industry may find them to be inaccessible at this time.
6 CONCLUSIONS
In general, drill hole spacing is most often used by resource geologists for an assignment of
resources to categories. The categories are rarely based on more than drill hole spacing and
ranges of continuities defined from variogram models. Potential commercial development of
a deposit relies on consecutive drill campaigns that focus on resources assigned to an Inferred
category. Application of the approaches discussed in this paper could lead to substantial savings, with the potential to reduce the number of holes drilled.
The geostatistical tools discussed here are an improvement on a typical approach applied
by resource geologists, because they take into account the local data variability. A seasoned
resource geologist instinctively knows that in areas with lower grade variability drill hole
spacing can be wider than in higher grade variability for an assignment to an Indicated category. Simulating grades in large panels and translating those results to yearly production
may result in larger areas assigned to an Indicated category than on drill hole spacing alone.
REFERENCES
Arik, A., Comparison of resource classification methodologies with a new approach, APCOM 2002:
30th International Symposium on the Application of Computers and Operations Research in the
Mineral Industry Phoenix, Arizona, 2002, pp. 57–64.
Chilès, J.P., and Delfiner, P., Geostatistics: Modeling Spatial Uncertainty, John Wiley & Sons, New
York, 1999.
Deutsch, C.V., Geostatistical Reservoir Modeling, Oxford University Press, New York, 2002.
Dohm, C., “Quantifiable Mineral Resource Classification: A Logical Approach”, in O. Leuangthong
and C. Deutsch, eds., Geostatistics Banff 2004, vol. 1, Springer Science+Business Media,
pp. 333–342.
Goovaerts, P., Geostatistics for Natural Resources Evaluation, Oxford University Press, New York,
1997.
Isaaks, E.H., The Application of Monte Carlo Methods to the Analysis of Spatially Correlated Data,
PhD Thesis, Stanford University, Stanford, CA, 1990.
Journel, A.G., Recoverable reserves estimation—the geostatistical approach, Mining Engineering, 1985,
Vol. 37 (6), pp. 563–568.
Journel, A.G., Nonparametric-estimation of spatial distributions, Journal of International Association
for Mathematical Geology, 1983, Vol. 15 (3), pp. 445–468.
Journel, A.G., Modeling Uncertainty: Some Conceptual Thoughts, Geostatistics For the Next Century,
Kluwer Academic Publications, 1993, p. 30–43.
Leuangthong, O. and Nowak, M., Dealing with high-grade data in resource estimation, SAIMM
Journal, January 2015, Vol. 115, pp. 27–36.
Often overlooked local variability of metal grades, when allocating blocks to resource categories, can potentially be used as an additional criterion for an assignment to an Indicated
category. A resource geologist could simply calculate coefficients of variations of grades in
large panels and link them to blocks located in those panels. Blocks originally assigned to an
Inferred category and located in low grade variability areas may be re-assigned to an Indicated category. On the other hand, blocks located in the high grade variability environment
can be considered for re-assignment to an Inferred category.
There are more advanced approaches than those presented here. For example, Pinto (2015,
2016) considered the calculation of confidence intervals of the panel as a moving window
formulation, thereby allowing for smoother boundaries between classification categories.
Silva et al. (2018) presented a framework to place additional drill holes subject to physical
constraints (location, drilling angles, depth limitations) and budget considerations. These
approaches, while more advanced and sophisticated, have yet to become common practice in
the mining industry. They also require a degree of comfort with advanced geostatistics and
the development of prototype software for implementation, that a general practitioner in the
mining industry may find them to be inaccessible at this time.
6 CONCLUSIONS
In general, drill hole spacing is most often used by resource geologists for an assignment of
resources to categories. The categories are rarely based on more than drill hole spacing and
ranges of continuities defined from variogram models. Potential commercial development of
a deposit relies on consecutive drill campaigns that focus on resources assigned to an Inferred
category. Application of the approaches discussed in this paper could lead to substantial savings, with the potential to reduce the number of holes drilled.
The geostatistical tools discussed here are an improvement on a typical approach applied
by resource geologists, because they take into account the local data variability. A seasoned
resource geologist instinctively knows that in areas with lower grade variability drill hole
spacing can be wider than in higher grade variability for an assignment to an Indicated category. Simulating grades in large panels and translating those results to yearly production
may result in larger areas assigned to an Indicated category than on drill hole spacing alone.
REFERENCES
Arik, A., Comparison of resource classification methodologies with a new approach, APCOM 2002:
30th International Symposium on the Application of Computers and Operations Research in the
Mineral Industry Phoenix, Arizona, 2002, pp. 57–64.
Chilès, J.P., and Delfiner, P., Geostatistics: Modeling Spatial Uncertainty, John Wiley & Sons, New
York, 1999.
Deutsch, C.V., Geostatistical Reservoir Modeling, Oxford University Press, New York, 2002.
Dohm, C., “Quantifiable Mineral Resource Classification: A Logical Approach”, in O. Leuangthong
and C. Deutsch, eds., Geostatistics Banff 2004, vol. 1, Springer Science+Business Media,
pp. 333–342.
Goovaerts, P., Geostatistics for Natural Resources Evaluation, Oxford University Press, New York,
1997.
Isaaks, E.H., The Application of Monte Carlo Methods to the Analysis of Spatially Correlated Data,
PhD Thesis, Stanford University, Stanford, CA, 1990.
Journel, A.G., Recoverable reserves estimation—the geostatistical approach, Mining Engineering, 1985,
Vol. 37 (6), pp. 563–568.
Journel, A.G., Nonparametric-estimation of spatial distributions, Journal of International Association
for Mathematical Geology, 1983, Vol. 15 (3), pp. 445–468.
Journel, A.G., Modeling Uncertainty: Some Conceptual Thoughts, Geostatistics For the Next Century,
Kluwer Academic Publications, 1993, p. 30–43.
Leuangthong, O. and Nowak, M., Dealing with high-grade data in resource estimation, SAIMM
Journal, January 2015, Vol. 115, pp. 27–36.