AI-GEOSTATS: Re:Ore reserves classification
- The estimate and the subsequent classification of the resources in different classes or categories is based on different levels of risk and requires a model able to quantify this risk for evaluation and classification of mineral resources a long time ago.
All classification systems share some common aspects in terms of defining the classes of resources based on distance separating samples and on the degree of confidence or accuracy associated with the results reported. Despite of being very clear in terms of stating sample distances, all the systems of classification do not provide clear definitions on how confidence limits should be calculated.
While the ordinary kriging allows a fast response to determine tonnages, the error calculated requires a series of assumptions which in various cases are difficult to be sustained.
Care must be taken when assigning confidence intervals with a predetermined distribution of the kriging errors. In practice, estimation errors are rarely normally distributed and likewise a lognormal model is just a approximation.
Another drawback of estimation is that the interpolation algorithms tend to smooth out details of the spatial variation of the attribute, where small values are overestimated and large values are underestimated, don´t allowing a realistic evaluation of the uncertainty associated with the estimate.
_________________________________________________________Luis Eduardo de Souza, Mining Engineere-mail: esouza@..., esouzabr@...
Federal University of Rio Grande do Sul - UFRGS
Mining Engineering Department
Mineral Research and Mine Planning Laboratory
Av. Osvaldo Aranha, 99/511
Porto Alegre/RS - Brazil - CEP: 90035-190
Phones:+55 51 3316-3594 (office),+55 51 3333-8229 (home),
+55 51 9905-6587 (cellular)
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