ETD Collection

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Subject: search.filters.subject.Geology--Statistical methods

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Now showing 1 - 6 of 6
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    Characterisation of rock mass rating (RMR) parameters by geostatistical analysis Orapa Mine Botswana
    (2019) Kgomanyane, Joel Thabo
    The focus of this research project is on the application of geostatistics to evaluate the rock mass rating (RMR) parameters for Orapa AK1 kimberlite pipe. The RMR parameters evaluated are Uniaxial Compressive Strength (UCS), Fracture Frequency per Metre (FFPM), Rock Quality Designation (RQD) and Dry Density (DD). When applied appropriately, these RMR parameters have the potential to enhance and inform conventional geological models, blast designs and metallurgical plant performances. Compared with other assessment methods such as using the global mean of the RMR parameters, the geostatistical estimates resulted in a more accurate and robust assessment of the geotechnical variables studied herein. Ordinary Kriging has been applied to estimate the RMR values at unsampled locations for the different rocktypes of the Orapa AK1 Kimberlite pipe. Variogram models were generated for the above RMR parameters within the different rocktypes both in the horizontal and vertical directions including an estimate for the nugget effect. The resulting block estimates were compared with sample data for all RMR parameters and bench plots for each rocktype were generated and analysed. Furthermore, geostatistics revealed that, RMR parameters have spatial correlation and these are strongly influenced by the geological environment of the AK1 Kimberlite at Orapa mine in Botswana. It is concluded that the evaluation of the rock mass rating (RMR) parameters using geostatistics is an important future requirement for the success of any mining project. It is recommended that geological and geotechnical data processing and interpretation should be coupled with geostatistical modelling at project pre-feasibility studies to enhance the conventional methods used in geoscience mining projects. The geostatistical estimation approach provides a more reliable and accurate method (low kriging variance) by taking into account the spatial continuity of variables under study as compared to simple averaging of geotechnical parameters for a given volume of rock mass.
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    Improving reconciliations through geostatistical resource model updates of Phoenix deposits, Tati Nickel mine
    (2019-09-27) Ntshole, Mothusi
    Periodic resource model updates are necessary to bridge the reconciliation variances between the resource model estimates and actual ore mined. As a tool, Mineral Resources reconciliation focuses on identifying, analysing and managing variance between estimated Mineral Resources and actual ore mined. The aim is to minimize the business risk associated with poor resource model estimate performance against actual ore mined at Phoenix Mine. The Phoenix Mine Mineral Resource model update research project incorporates historical and recently acquired drillhole data, other relevant geological information in the form of geological pit floor and face maps to update the Phoenix Mineral Resource model. Employing appropriate geostatistical estimation methods and improved modelling procedures can highlight and overcome some of the causes of observed reconciliation variances. Each of the five domains of the Phoenix resource was estimated through ordinary kriging and indicator kriging as principal methods. Nearest neighbour (NN) and inverse power of distance (IPD) methods were used as a check and where the above geostatistical methods proved inappropriate. The comparison between model estimates from these various estimation techniques and raw drill hole data was undertaken. The results indicate areas of both good and poor correlation across the different methods and sections of the resource. Areas where there is good correlation coincides with good sampling coverage where as poor correlation coincides mostly with portions of the resource where there is paucity of sampling data. Subjecting the individual domains’ resource estimates from the various estimation methods to a validation check against the sampling data assisted in selecting the estimate that honours the sampling data the most. Such Estimate was selected as the most suitable and reported as the Estimated Resources. Indicator Kriging produced better results compared to the rest of the techniques. In domain four geostatistical methods were unsuccessful thus Inverse power of distance method was used.
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    Mineral resource evaluation of a platinum tailings resource: a case study
    (2017) Muthavhine, Mashudu Innocent
    The project investigated the application of geostatistical techniques in evaluating a mechanically deposited platinum tailings resource. The project was undertaken on one of the Anglo American Platinum tailings dams, the identity of which cannot be revealed, due to the agreement in place or permission given. Remnant unrecovered minerals of economic potential still exist in tailings dams. These unrecovered minerals have influenced several mining companies to turn their attention to the economic potential that still exists in tailings, making them a key strategic component of their resources and reserves. Geostatistics has been developed and thoroughly tested or improved to address challenges experienced in estimating in situ geological ore bodies. The main aim of this Research Project is to test whether these fundamental principles and theories of geostatistics are relevant and appropriate in evaluating man-made ore bodies, such as a Platinum tailings dam, without any significant changes needed on the underlying principles or estimation algorithms. The findings on the Case Study tailings resource can be applied in the evaluation of other tailings dams, as well as any other man-made structures such as low grade rock dumps, muck piles, with related characteristics. A standard approach (methodology) was followed to evaluate the Case Study tailings resource. Drilling and sampling was conducted through sonic drilling. It is a dry drilling technique that is suitable for sampling unconsolidated particles such as tailings. Thereafter, 2 samples were sent to the laboratory to establish grade (concentration) of Platinum Group Metals (Platinum, Palladium and Rhodium), Gold and Base Metals (Copper and Nickel). Density was also measured, and comprehensively analysed as part of variables of interest in this research. Statistical analyses were performed on all variables of interest contained in the dam: which are Platinum (Pt), Palladium (Pd), Gold (Au), 3E (two PGMs plus Gold), Copper (Cu), Nickel (Ni) and Density. The underlying statistical distributions of all metals and density were found to be non-symmetrical and slightly positive skewed. The skewness of the distributions was established to be marginal. Differences between raw data (untransformed) averages and the log-normal estimates were analysed and found to be insignificant. As such Ordinary Kriging of untransformed data was concluded to be the appropriate geostatistical technique for Case Study tailings resource. Analysis of mineralisation continuity (variography), a pre-requisite for geostatistical techniques such as Ordinary Kriging applied on the case study tailings resource, was also performed. Reasonable and sufficient mineralisation continuity was established to exist in the Case Study tailings resource. Although characterised by high nugget effect, these spatial correlations were established to be continuous with ranges of influence well beyond 450 m in all variables. Anisotropic variograms were modelled for all variables and are comprised of nested structures with two to three spherical models. Resource estimation was conducted through Ordinary Kriging in Datamine. All the seven variables were successfully interpolated into each cell of the 5m x 5m x 5m block model. Rigorous validation of the resource model was performed to establish the quality and reliability of the estimation carried out. Estimated resource model was analysed against the original borehole data, through comparison of grade profiles, statistical analysis, QQ Plots and histograms. The grade profile was recognised to be similar between boreholes (5 m composites) and the adjacent cells that have been estimated. Furthermore, statistical analyses revealed minimal differences between means of the estimated model and the original borehole data: the highest difference being 1.7% realised on 3E, followed by 1.1% on Density and Gold (Au). The rest of the variables (Pt, Pd, Cu, and Ni) have differences that are below 1%. 3 QQ plots and histogram were plotted from resource model with 5m x 5m x 5m cells and 5 m composited boreholes. Although these data sets are of different (slightly incompatible) supports, the intended purpose of comparing distributions was achieved. QQ plots and histograms revealed approximately identical shaped distributions of the two data sets, with some minor deviations noticeable in graphs of only two variables (Au and Density) that are underlain by two populations. The validation process carried out gave a compelling assurance on the quality and reliability of the resource model produced. The Case Study tailings resource therefore is successfully estimated by Ordinary Kriging. The results achieved on the Case Study tailings dam has successfully proved that geostatistical principles and theories can confidently be applied, in their current form or understanding, to any man-made tailings resource
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    Application of stochastic orebody simulation techniques to assess geological volume and grade uncertainty for gold reef deposits
    (2017) Chanderman, Lisa
    This dissertation discusses the use of stochastic orebody modelling techniques for assessing geological uncertainty associated with gold mineralisation at Geita Gold Mine in Tanzania, and proposes a practical methodology that can be applied to similar studies. As part of the pre-feasibility stage studies for underground mining at Geita, stochastic simulations were required to assess the geological uncertainty associated with isolating (modelled) high grade lenses that occur within the known low grade mineralisation currently targeted for underground mining. Two different simulation techniques are applied in this research: Sequential Indicator Simulation to generate lithofacies realisations from which to assess ore category boundaries and shapes for use in quantifying volumetric uncertainty; and Direct Block Simulations to simulate gold grade realisations from which to assess grade uncertainty. This study identified potential upside and downside mine planning scenarios for volumes and total metal content from the ore category and grade simulations respectively. The findings of the results demonstrated that the high grade zones are much more broken up and discontinuous than the currently modelled high grade shape. The current business case uses a probabilistic high grade shape based on a single grade indicator and a probability choice of 50 percent as the threshold for high grade. The results of the study consider a simulation of possible outcomes based on the same threshold grade indicator and hence quantify the uncertainty or total geological risk. This geological risk may be introduced to mine designs, production schedules and NPV predictions The stochastic workflow developed can be applied to analogous deposit types to assess the risk related to geological uncertainty. The work includes a description of practical considerations to be accounted for when applying the techniques.
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    Contextualized risk mitigation based on geological proxies in alluvial diamond mining using geostatistical techniques
    (2016) Jacob, Jana
    Quantifying risk in the absence of hard data presents a significant challenge. Onshore mining of the diamondiferous linear beach deposit along the south western coast of Namibia has been ongoing for more than 80 years. A historical delineated campaign from the 1930s to 1960s used coast perpendicular trenches spaced 500 m apart, comprising a total of 26 000 individual samples, to identify 6 onshore raised beaches. These linear beaches extend offshore and are successfully mined in water depths deeper than 30 m. There is, however, a roughly 4 km wide submerged coast parallel strip adjacent to the mostly mined out onshore beaches for which no real hard data is available at present. The submerged beaches within the 4 km coast parallel strip hold great potential for being highly diamondiferous. To date hard data is not yet available to quantify or validate this potential. The question is how to obtain sufficient hard data within the techno economic constraints to enable a resource with an acceptable level of confidence to be developed. The work presented in this thesis illustrates how virtual orebodies (VOBs) are created based on geological proxies in order to have a basis to assess and rank different sampling and drilling strategies. Overview of 4 papers Paper I demonstrates the challenge of obtaining a realistic variogram that can be used in variogram-based geostatistical simulations. Simulated annealing is used to unfold the coastline and improve the detectable variography for a number of the beaches. Paper II shows how expert opinion interpretation is used to supplement sparse data that is utilised to create an indicator simulation to study the presence and absence of diamondiferous gravel. When only the sparse data is used the resultant simulation is unsuitable as a VOB upon which drilling strategies can be assessed. Paper III outlines how expert opinion hand sketches are used to create a VOB. The composite probability map based on geological proxies is adjusted using a grade profile based on adjacent onshore data before it is seeded with stones and used as a VOB for strategy testing. Paper IV illustrates how the Nachman model based on a Negative Binomial Distribution (NBD) is used to predict a minimum background grade by considering only the zero proportions (Zp) of the grade data. v Conclusions and future work In the realm of creating spatial simulations that can serve as VOBs it is very difficult to attempt to quantify uncertainty when no hard data is available. In the absence of hard data, geological proxies and expert opinion are the only inputs that can be used to create VOBs. Subsequently these VOBs are used as a base to be analysed in order to evaluate and rank different sampling and drilling strategies based on techno economic constraints. VOBs must be updated and reviewed as hard data becomes available after which sampling strategies should be reassessed. During early stage exploration projects the Zp of sample results can be used to predict a minimum background grade and rank different targets for further sampling and valuation. The research highlights the possibility that multi point statistics (MPS) can be used. Higher order MPS should be further investigated as an additional method for creating VOBs upon which sampling strategies can be assessed.
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