3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Comparison of kriging neighbourhood analysis (KNA) results from two different software packages: a case study(2018) Amwaama, Martha Munageni NemupeweThe quality and outcome of any kriging estimation and conditional simulation exercises are dependent on the definition of the kriging neighbourhood parameters that are applied in these processes. It is necessary to minimise the conditional bias that arises from the application of the kriging estimation methodology. This minimisation can be addressed by a kriging neighbourhood analysis (KNA), whereby the optimum kriging neighbourhood parameters are identified prior to executing the actual block kriging estimation. Conditional bias presents itself in the reality that the real mining blocks grade averages are more variable than the estimated block grades, which is a consequence of the smoothing effect of kriging. The motivation for this research into the KNA outcomes using different software stems from recognising the importance of minimising conditional bias in the application of kriging in resource estimation. Whilst studies on the comparison of software packages have been done in the fields of computer science, bio-technology, geology, and other areas, no evidence was found in the literature review for a comparison of KNA results being carried out using different software packages. This research was embarked on to establish whether the KNA procedures proposed by different software providers would result in the same kriging neighbourhood parameters being selected for the kriging estimation process. To do the research from a practical aspect, a case study on Rossing Uranium Mine was considered. The results from the KNA using SUPERVISOR geostatistical software and those from using SURPAC geology and mine planning software is compared in this research. The conditional bias measures that need to be optimised are: the slope of regression; kriging efficiency, and the number of negative weights. This is done by analysing the impact of varying the following parameters in the kriging neighbourhood, namely the estimation block size, the minimum and maximum number of samples to be considered, the search range and the configuration of the discretisation points of the block to be estimated; which all have an influence on the afore mentioned conditional bias measures. The same input data, geological model and block configuration, semi-variogram parameters as well as test locations were used to ensure that the outcomes of the application of the software packages would be comparable, valid and not user introduced. The two block configurations tested are for well-informed blocks and for poorly- informed blocks. This research study concluded that there is no significant difference between the KNA results produced by SUPERVISOR and SURPAC; the two software packages considered, there are however other differences between the software packages which are not related to KNA. For well-informed blocks the optimised conditional bias measures identified using SUPERVISOR and SURPAC are the same with slight differences in the conditional bias measures for poorly-informed blocks. Differences identified are related to the manner in which the individual Software packages select specific samples for inclusion in the kriging neighbourhoodItem Application of indicator kriging and conditional simulation in assessment of grade uncertainty in Hunters road magmatic sulphide nickel deposit in Zimbabwe(2017) Chiwundura, PhillipThe assessment of local and spatial uncertainty associated with a regionalised variable such as nickel grade at Hunters Road magmatic sulphide deposit is one of the critical elements in the resource estimation. The study focused on the application of Multiple Indicator Kriging (MIK) and Sequential Gaussian Simulation (SGS) in the estimation of recoverable resources and the assessment of grade uncertainty at Hunters Road’s Western orebody. The Hunters Road Western orebody was divided into two domains namely the Eastern and the Western domains and was evaluated based on 172 drill holes. MIK and SGS were performed using Datamine Studio RM module. The combined Mineral Resources estimate for the Western orebody at a cut-off grade of 0.40%Ni is 32.30Mt at an average grade of 0.57%Ni, equivalent to 183kt of contained nickel metal. SGS results indicated low uncertainty associated with Hunters Road nickel project with 90% probability of an average true grade above cut-off, lying within +/-3% of the estimated block grade. The estimate of the mean based on SGS was 0.55%Ni and 0.57% Ni for the Western and Eastern domains respectively. MIK results were highly comparable with SGS E-type estimates while the most recent Ordinary Kriging (OK) based estimates by BNC dated May 2006, overstated the resources tonnage and underestimated the grade compared to the MIK estimates. It was concluded that MIK produced better estimates of recoverable resources than OK. However, since only E-type estimates were produced by MIK, post processing of “composite” conditional cumulative distribution function (ccdf) results using a relevant change of support algorithm such as affine correction is recommended. Although SGS produced a good measure of uncertainty around nickel grades, post processing of realisations using a different software such as Isatis has been recommended together with combined simulation of both grade and tonnage.Item Comparative analysis of ordinary kriging and sequential Gaussian simulation for recoverable reserve estimation at Kayelekera Mine(2016-09-16) Gulule, Ellasy PriscillaIt is of great importance to minimize misclassification of ore and waste during grade control for a mine operation. This research report compares two recoverable reserve estimation techniques for ore classification for Kayelekera Uranium Mine. The research was performed on two data sets taken from the pit with different grade distributions. The two techniques evaluated were Sequential Gaussian Simulation and Ordinary Kriging. A comparison of the estimates from these techniques was done to investigate which method gives more accurate estimates. Based on the results from profits and loss, grade tonnage curves the difference between the techniques is very low. It was concluded that similarity in the estimates were due to Sequential Gaussian Simulation estimates were from an average of 100 simulation which turned out to be similar to Ordinary Kriging. Additionally, similarities in the estimates were due to the close spaced intervals of the blast hole/sample data used. Whilst OK generally produced acceptable results like SGS, the local variability of grades was not adequately reproduced by the technique. Subsequently, if variability is not much of a concern, like if large blocks were to be mined, then either technique can be used and yield similar results.Item Finding the optimal dynamic anisotropy resolution for grade estimation improvement at Driefontein Gold Mine, South Africa(2016) Mandava, Senzeni MaggieMineral Resource estimation provides an assessment of the quantity, quality, shape and grade distribution of a mineralised deposit. The resource estimation process involves; the assessment of data available, creation of geological and/or grade models for the deposit, statistical and geostatistical analyses of the data, as well as determination of the appropriate grade interpolation methods. In the grade estimation process, grades are interpolated/extrapolated into a two or three – dimensional resource block model of a deposit. The process uses a search volume ellipsoid, centred on each block, to select samples used for estimation. Traditionally, a global orientated search ellipsoid is used during the estimation process. An improvement in the estimation process can be achieved if the direction and continuity of mineralisation is acknowledged by aligning the search ellipsoid accordingly. The misalignment of the search ellipsoid by just a few degrees can impact the estimation results. Representing grade continuity in undulating and folded structures can be a challenge to correct grade estimation. One solution to this problem is to apply the method of Dynamic Anisotropy in the estimation process. This method allows for the anisotropy rotation angles defining the search ellipsoid and variogram model, to directly follow the trend of the mineralisation for each cell within a block model. This research report will describe the application of Dynamic Anisotropy to a slightly undulating area which lies on a gently folded limb of a syncline at Driefontein gold mine and where Ordinary Kriging is used as the method of estimation. In addition, the optimal Dynamic Anisotropy resolution that will provide an improvement in grade estimates will be determined. This will be achieved by executing the estimation process on various block model grid sizes. The geostatistical literature research carried out for this research report highlights the importance of Dynamic Anisotropy in resource estimation. Through the application and analysis on a real-life dataset, this research report will put theories and opinions about Dynamic Anisotropy to the test.Item Analysing spatial data via geostatistical methods(2006-11-16T08:23:15Z) Morgan, Craig JohnThis dissertation presents a detailed study of geostatistics. Included in this work are details of the development of geostatistics and its usefulness both in and outside of the mining industry, a comprehensive presentation of the theory of geostatistics, and a discussion of the application of this theory to practical situations. A published debate over the validity of geostatistics is also examined. The ultimate goal of this dissertation is to provide a thorough investigation of geostatistics from both a theoretical and a practical perspective. The theory presented in this dissertation is thus tested on various spatial data sets, and from these tests it is concluded that geostatistics can be effectively used in practice provided that the practitioner fully understands the theory of geostatistics and the spatial data being analyzed. A particularly interesting conclusion to come out of this dissertation is the importance of using additive regionalized variables in all geostatistical analyses.