Abstract
Abstract Non-linear geostatistical methods are known to deal appropriately with the geological and geometrical complexity of gold deposits. This article reports the results related to an investigation to improve the gold content estimate based on restricted ore modeling to honor the structural aspects that control the mineralization. The grade domains are defined by using structural measurements to guide the indicator kriging (IK) estimator. Relevant grade intervals are chosen as indicators. Kriging the indicators provides a measure of the grade uncertainty at the sample support. The probability indicator modeling relies on thresholding the estimates which are represented by cumulative distribution functions (cdf) at the unsampled locations. The implicit concept of probability means that the chance of an estimated node belonging to a given grade domain is as big as the estimated IK value. The geological consistency of IK models requires a proper definition of some key parameters: The probability thresholds and indicator variogram models must honor the structural features and stationarity conditions of grade intervals. The geological representativeness of these models depends heavily on thresholding the estimates. For instance, extremely permissive estimates may produce overrepresented ore domains. The decision of the optimal indicator probability for defining the ore boundaries is made by iterative comparison. Several thresholds were applied to kriged maps and the results reconciled to the most sampled areas until achieving reasonable geological adherence. The mineralization continuity often varies according to local structural features and so dynamic anisotropy is used to control the variogram direction and search ellipse to consider the significant scale trend and small-scale fold geometries. A case study based on a real gold deposit dataset was performed and the method was discussed. The IK models can define precisely the mineralization bounds in the most detailed areas. However, the results presented some limitations on reproducing the geological expectation in regions of wide drilling spacing. The lack of information in some areas led to an excessive number of small sub-zones. The method allows a faster and efficient modeling of structurally complex geometries and provides an uncertainty assessment which may be useful to support exploratory and short-term decisions.
Highlights
The resource evaluation of deposit is composed of two steps: definition of the boundaries of tthe various geological domains and estimation or simulation of grades for each unit. (Chilès et al, 2004)In mining, 3D ore models are routinely supported by computational tools, being that the general workflow: (i) ore/waste contour lines are interpreted based on cross sections; (ii) the outlined contacts are linked to each other to create three-dimensional solids (Abzalov, 2016)
Implicit functions are interpolated by implementing a method known as radial basis function (RBF)
The indicator modelling supported by dynamic anisotropy was tested on data of an orogenic lode gold deposit located in the Quadrilátero Ferrífero (MG, Brazil) metallogenetic district
Summary
The resource evaluation of deposit is composed of two steps: definition of the boundaries of tthe various geological domains and estimation or simulation of grades for each unit. (Chilès et al, 2004). The traditional modelling may be a cumbersome task and sometimes based on subjective criteria of interpretation. Common implicit algorithms interpolate distance functions between categorized samples. Surface corresponding to specific isovalues of estimated distance functions (Rollo, 2017). Implicit functions are interpolated by implementing a method known as radial basis function (RBF). While kriging uses covariance calculated from data, RBF interpolator instead, uses pre-defined interpolant functions (e.g. spheroidal, linear) to improve the processing time. Despite the singularities of how data are weighted, both RBF interpolants and indicator models are based on kriging mathematical formulation. Abzalov (2003) and Oliveira (2011) discussed IK domaining of mineral deposits. The indicator modelling supported by dynamic anisotropy was tested on data of an orogenic lode gold deposit located in the Quadrilátero Ferrífero (MG, Brazil) metallogenetic district
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