245
blocks exceeds a given cut-off. The only obstacle with the methodology presented is that the
different data sets are frequently entirely heterotopic for certain elements. In other words,
the information is not twined considering the different data sets. In this situation to define
the spatial correlation at the same location can be difficult. However, two solution are presented in this document. The methodology presented in this work is illustrated in a polymetallic massive sulphide deposit, where Cu grade is estimated from exploration data (reliable
data set) and production sample (unreliable data set). It has been shown that this method
improves the quality of the estimation associated with all the available information.
REFERENCES
Bleines, C., Deraisme, J., Geffroy, F., Perseval, S., Rambert, F., Renard, D., Touffait, Y., and Wagner, L.
2018 Child Development 65(1): 13–16. Polhill, R.M. 1982. ISATIS software manual. Geovariances and
Ecole des Mines de Paris. 201–206 pp.
Chilcott, D.L., 2007, Technical Report on the Neves Corvo Mine, Southern Portugal, Wardell Armstrong International Limited, Ref: WAI/61–0465: 27–28. Portugal.
Chilès, J.P. & Delfiner, P., 2012, Geostatistics modeling spatial Uncertainty, Second Edition, Wiley series
in probability and statistics. 216–218, 339.
Deraisme, J. and Strydom, M., 2009, Estimation of iron ore resources integrating diamond and percussion drillholes Applied Physics of Condensed Matter - 15th International Conference 2–4. LiptovkÃ.
JÃąn, BystrÃą, Slovakia.
Rossi, M.E. and Deutsch, C.V., 2014, Mineral Resource Estimation Springer Dordrecht Heidelberg New
York London 171–172.
Van Den Boogaart, K.G. and Tolosana-Delgado, R., 2018, Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences. In Handbook of Mathematical Geosciences,
Springer International Publishing, 673âĂŞ686.
Wackernagel, H., 1998, Multivariate Geostatistics, N. 1997. Recommendations and prejudices, 158, 291.
Springer.
blocks exceeds a given cut-off. The only obstacle with the methodology presented is that the
different data sets are frequently entirely heterotopic for certain elements. In other words,
the information is not twined considering the different data sets. In this situation to define
the spatial correlation at the same location can be difficult. However, two solution are presented in this document. The methodology presented in this work is illustrated in a polymetallic massive sulphide deposit, where Cu grade is estimated from exploration data (reliable
data set) and production sample (unreliable data set). It has been shown that this method
improves the quality of the estimation associated with all the available information.
REFERENCES
Bleines, C., Deraisme, J., Geffroy, F., Perseval, S., Rambert, F., Renard, D., Touffait, Y., and Wagner, L.
2018 Child Development 65(1): 13–16. Polhill, R.M. 1982. ISATIS software manual. Geovariances and
Ecole des Mines de Paris. 201–206 pp.
Chilcott, D.L., 2007, Technical Report on the Neves Corvo Mine, Southern Portugal, Wardell Armstrong International Limited, Ref: WAI/61–0465: 27–28. Portugal.
Chilès, J.P. & Delfiner, P., 2012, Geostatistics modeling spatial Uncertainty, Second Edition, Wiley series
in probability and statistics. 216–218, 339.
Deraisme, J. and Strydom, M., 2009, Estimation of iron ore resources integrating diamond and percussion drillholes Applied Physics of Condensed Matter - 15th International Conference 2–4. LiptovkÃ.
JÃąn, BystrÃą, Slovakia.
Rossi, M.E. and Deutsch, C.V., 2014, Mineral Resource Estimation Springer Dordrecht Heidelberg New
York London 171–172.
Van Den Boogaart, K.G. and Tolosana-Delgado, R., 2018, Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences. In Handbook of Mathematical Geosciences,
Springer International Publishing, 673âĂŞ686.
Wackernagel, H., 1998, Multivariate Geostatistics, N. 1997. Recommendations and prejudices, 158, 291.
Springer.