Electronic Theses and Dissertations (Masters)

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Subject: search.filters.subject.SDG-9: Industry, innovation and infrastructure

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    Machine Learning Algorithms-Based Classification of Lithology using Geophysical Logs: ICDP DSeis Project Boreholes, South Africa
    (University of the Witwatersrand, Johannesburg, 2024-09) Atita, Obehi Chapet; Durrheim, Raymond; Saffou, Eric
    One of the most significant geosciences tasks is the accurate classification of lithologies for metal and mineral resources exploration, characterization of oil/gas reservoir(s), and the planning and management of mining operations. With the availability of abundant, huge and multidimensional datasets, machine learning-based data-driven methods have been widely adopted to assist in solving geoscientific problems such as the efficient evaluation and interpretation of large datasets. The adoption of machine learning-based methods aims to improve lithological identification accuracy and extract information required for accurate and objective decision-making with respect to activities such as exploration, drilling, mine planning and production. Practically, this helps to reduce working time and operating costs. We aim to evaluate the feasibility of machine learning-based algorithms application to geophysical log data for the automated classification of lithologies based on the stratigraphic unit at the formation level for the purpose of distinguishing and correlating the quartzites between boreholes, and mapping key radioactive zones within the mining horizon. This study implemented four different machine learning algorithms: gradient boosting decision trees, random forest, support vector machine, and K-means clustering models. Analyzed features and labelled datasets are multivariate downhole geophysical and lithology logs from the two ICDP DSeis project boreholes drilled in the Klerksdorp gold field, respectively. To mitigate misclassification error and avoid model overfitting/underfitting, the optimal combination sets and optimal values for each implemented supervised model’s hyperparameters were obtained using the Grid search and 10-fold cross-validation optimization methods. The input dataset was randomly split automatedly into training and testing subsets that made up 80% and 20% of the original dataset, respectively. The models were trained and cross-validated using the training subset, and their performances were assessed using the testing subset. The classification performance of each model was evaluated using F1 scores and visualized using confusion matrices. The best supervised classification model for our study area was selected based on the testing subset F1 scores and computational cost of training models. The testing subset results shows that Random Forest and Support Vector Machine classifier models performed much better relative to the Gradient Boosting Decision Trees classifier model, with F1 scores over 0.80 in borehole A and B. In borehole A and B, Random Forest classifier has the least computational training time of about 14- and 6- hours, respectively. The feature importance results demonstrate that the logging feature P-wave velocity (Vp) is the highest predicting feature to the lithology classification in both boreholes. We find that the quartzite classes at different stratigraphic positions in each borehole are similar and they are correlated between the DSeis boreholes. The K-means clustering revealed three clusters in this study area and effectively map the radioactive zones. This study illustrates that geophysical log data and machine learning-based algorithms can improve the task of data analysis in the geosciences with accurate, reproducible and automated prediction of lithologies, correlation and mapping of radioactive zones in gold mine. This study outputs can serve as quality control measures for future similar studies both in the academic and industry. We identified that availability of large data is the major factor to high accuracy performance of machine learning-based algorithms for classification problems.
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    Hydrogeological assessments and investigation of inflow sources at Lumwana Copper Mine, Zambia
    (University of the Witwatersrand, Johannesburg, 2023) Mbilima, Mike; Abiye, Tamiru
    This Research Report presents results of integrated field and desktop-based hydrogeological investigations at the Lumwana Mine, Zambia. Groundwater occurrence in the mine poses challenges with effective mining operations and slope stability. The primary aim of this study was to establish the sources of groundwater inflows and to establish the nature of surface water and groundwater interaction within the Lumwana Mine hydro-geotechnical units. The Lumwana hydrogeological investigation has been achieved through the integration of multi-disciplinary data types, which include geology, structures, hydrochemistry, meteorological data (rainfall, temperature, humidity and evapotranspiration), environmental isotopes, dewatering pumping records, groundwater level monitoring, water temperature, general hydrogeological data and surface hydrology. The investigation has confirmed the presence of hydraulic connections between different surface water bodies such as dams, diversion channels, streams and open pit excavation, and has proven to be a useful approach in tracing the source of mine inflows. Rainfall, groundwater and surface water samples have similar δ18O and δ2H isotopic signatures thus lamenting the existence of a hydraulic link between groundwater and surface water. Recharge estimation through Water Table Fluctuation method (WTF) determined 8% of mean annual precipitation (MAP). The dominant hydrochemical facies are Ca-Mg-HCO3 and Ca-Mg-SO4. The local geology and geochemistry of the tailings are the main controllers of groundwater chemistry through rock-water interaction. The geology of the study area consists of older metamorphosed gneisses, schists, migmatites, amphibolites and granitoids. Integrated assessment of the Lumwana hydrogeological environment has enabled the development of the Lumwana Mine hydrogeological conceptual model. In the shallow, highly to moderately weathered zones, groundwater flows from south towards low topographic regions in the northwest mimicking the general topography. The hydraulic test conducted at Lumwana Mine has revealed the saprock units have higher hydraulic conductivity by several orders compared to the saprolites and the fresh bedrock, where groundwater flow is mainly controlled by the occurrence and distribution of the fracture network.