4. Electronic Theses and Dissertations (ETDs) - Faculties submissions
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Item A Data Science Framework for Mineral Resource Exploration and Estimation Using Remote Sensing and Machine Learning(University of the Witwatersrand, Johannesburg, 2023-08) Muhammad Ahsan, Mahboob; Celik, Turgay; Genc, BekirExploring mineral resources and transforming them into ore reserves is imperative for sustainable economic growth, particularly in low income developing economy countries. Limited exploration budgets, inaccessible areas, and long data processing times necessitate the use of advanced multidisciplinary technologies for minerals exploration and resource estimation. The conventional methods used for mineral resources exploration require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering and clean validated data to build accurate estimations. In the past few years, data science has become increasingly important in the field of minerals exploration and estimation. This study is a step forward in this field of data science and its integration with minerals exploration and estimation. The research has been conducted to develop a state-of-the-art data science framework that can effectively use limited field data with remotely sensed satellite data for efficient mineral exploration and estimation, which was validated through case studies. Satellite remote sensing has emerged as a powerful modern technology for mineral resources exploration and estimation. This technology has been used to map and identify minerals, geological features, and lithology. Using digital image processing techniques (band ratios, spectral band combinations, spectral angle mapper and principal component analysis), the hydrothermal alteration of potential mineralization was mapped and analysed. Advanced machine learning and geostatistical models have been used to evaluate and predict the mineralization using field based geochemical samples, drillholes samples, and multispectral satellite remote sensing based hydrothermal alteration information. Several machine learning models were applied including the Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Support Vector Regression (SVR), Generalized Linear Model (GLM), and Decision Tree (DT). The geostatistical models used include the Inverse Distance Weighting (IDW) and Kriging with different semivariogram models. IDW was used to interpolate data points to make a prediction on mineralization, while Kriging used the spatial autocorrelation to make predictions. In order to assess the performance of machine learning and geostatistical models, a variety of predictive accuracy metrics such as confusion matrix, a receiver operating characteristic (ROC) curve, and a success-rate curve were used. In addition, Mean Absolute Error, Mean Square Error, and root mean square prediction error were also used. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values. The experimental results also showed that there is no single method that can be used independently to predict the spatial distribution of geochemical elements in streams. Instead, a combinatory approach of IDW and kriging is advised to generate more accurate predictions. For the case study of copper prediction, the results showed that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. The evaluation results have showed that the data science framework is able to deliver highly accurate results in minerals exploration and estimation. The results of the research were published through several peer reviewed journal and conference articles. The innovative aspect of the research is the use of machine learning models to both satellite remote sensing and field data, which allows for the identification of highly prospective mineral deposits. The framework developed in this study is cost-effective and time-saving and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up- to-date mineral information.Item Factors enabling copper beneficiation in Botswana(University of the Witwatersrand, Johannesburg, 2023) Coetzee, R. J.; Schaling, EricBotswana’s Kalahari copper belt (KCB), which extends into the North-West of Botswana, is a prospective zone that offers the opportunity for a large copper production sector to provide diversity and growth for mining in Botswana. Despite good quality orebodies with good grades, the disadvantage of long and expensive logistical routes has softened project financial returns in the past and hampered the development of new mines on the KCB. The Botswanan government identified the need to investigate the creation of in-country, downstream processing (beneficiation) facilities to improve the global competitiveness of in-country producers and provide future opportunities for job growth. While in-country beneficiation has been explored over the years by various stakeholders, the low quantity of primary copper sources and an uncertain outlook on exploration deterred further investment. The study further seeks out to improve the potential stakeholders’ understanding of the relevant enabling factors and underlying risks of in-country beneficiation through practical discounted cash flow modelling. Should Botswana want to drive in-country processing and enact a beneficiation legislation framework, it would need to explore options such as incentivising with tax structures and developing associated infrastructure to ensure electrification, water supply, rail network availability and other needs such as housing. Tax incentives from the government are recommended to stimulate foreign direct investment. Viable government incentives such as i) granting special economic zone status, or more relevant ii) to divert 1% of the mineral royalties payable by miners to the government to this new facility in exchange for a proportionate minority equity state. Four viable scenarios for in-country beneficiation were developed where the NPV7.7%,real 2022 of a new copper processing facility could range between US$ 358 – 422 million. Furthermore, the total value generated by the facility, undiscounted over life of operation, was estimated to be US$3.6 billion. 10.1% of the total project value generated would be recovered by the Botswanan government through corporate tax, dividend withholding tax, personal income tax and value added tax. The value added to the Botswanan economy would contribute an additional 2.2% to Botswana’s gross domestic product. The total value generated towards nation building was calculated to be at least three times larger than the profit generated by the facility owners.