A Methodology for Calculating Tonnage Uncertainty in Vein-Type Deposits

One of the main sources of uncertainty in vein type deposits is found in the calculation of the tonnage. The boundary limits often applied to a vein type deposit are calculated from sparse data using deterministic methods that offer no measure of uncertainty. The most common method used to calculate the tonnage of a vein-type deposit is to convert the volume of the deposit defined by a wireframe model into a tonnage. Wireframe models are deterministic in nature being created from the interpretation of level plans and cross sections. These types of models have no provision for the calculation of tonnage uncertainty. One method of calculating the tonnage uncertainty in vein deposits is through the use of a distance function. This thesis presents a distance function (DF) approach that allows for the introduction of uncertainty into the modeling process by defining a zone or bandwidth that is quantifiable. This approach uses individual drillhole samples coded with a distance calculated by the DF rather than a wireframe model to estimate the vein tonnage resulting in considerable savings in time by skipping the wireframe modeling process. Three dimensional models are then extracted for probability intervals across the bandwidth. Through standardization, tonnages corresponding to any probability interval can be extracted. Modifying the distance function modifies the size and shape of the bandwidth. Two parameters are used to modify the distance function. The first parameter controls the bandwidth and is the uncertainty parameter. The second parameter controls position of bandwidth and is the bias correction parameter. With proper calibration, the values of the two parameters used to modify the distance function will result in models that are both accurate and precise. A method for full calibration of the uncertainty and bias correction parameters is presented. An example using synthetic models is also presented and demonstrates that the method does produce results that are accurate and precise within a defined tolerance.