Faculty of Science (ETDs)
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Item First principle study of inorganic metal halide perovskites for solar cells application(University of the Witwatersrand, Johannesburg, 2024-08) Maleka, Prettier Morongoa; Maphanga, Regina R.; Ntwaeaborwa, Odireleng MartinAll-inorganic halide perovskites have received significant attention as semiconductor materials due to their outstanding opto-electronic properties, which have achieved power conversion efficiency (PCE) of up to 25% in perovskite solar cells. Their exceptional characteristics include long diffusion lengths for electrons and holes, tuneable band gap, high absorption coefficients, small effective masses, high carrier mobility, and simple reproducible process. Despite these excellent properties, metal halide perovskites have drawbacks that negatively affect the PCE and stability of the perovskite solar cell devices. This study investigated all-inorganic halide perovskite, CsPbI3, by employing the first-principle density functional theory (DFT) method. Firstly, the effect of mixing halides on X-site was investigated to probe the structural stability and opto-electronic properties. The structural, electronic, optical, mechanical and thermodynamics properties of CsPbI3 – xBrx were investigated using three exchange correlation functionals, namely, LDA, GGA-PBE and SCAN meta-GGA. The findings revealed that mixed halide perovskites have an ideal direct energy band gap for suitable photovoltaic applications. For GGA-PBE and SCAN meta GGA exchange correlation functionals, the determined energy band gap ranges from 1.33 eV and 1.877 eV, whereas the LDA band gap ranges between 0.960 eV and 1.137 eV. The electronic band gaps predicted by GGA-PBE and SCAN meta GGA exchange correlation, which offer better precision compared to LDA suggest that Br-doped CsPbI3 – xBrx perovskite is suitable for photons absorption from near-infrared to visible regions of the spectrum. The modification of the band gap is an essential feature of photovoltaics, as it enables the optimization of solar cell performance. In addition, the systems CsPbI3, CsPbI2Br, CsPbIBr2, and CsPbBr3 exhibited exceptional mechanical and thermodynamic stability. Secondly, perovskites that are considered for photovoltaic applications contain toxic element lead (Pb) on the B-site, which limits application of these perovskites in photovoltaic devices. In this study, substitution of toxic Pb with a smaller percentage of selected transition metals was investigated in order to alleviate the toxicity problems. Thus, CsPbI3 doped with 12.5 % concentration of transition metal, Mn, Fe, Ni and Zn was investigated using DFT. The results showed that transition metal doped-CsPbI3 perovskites enhanced the absorption of this material, although they are all indirect band gaps semiconductors. All the materials were found to be mechanically stable. Lastly, cluster expansion which is a method that is capable of describing the concentration dependent thermodynamic properties of materials while maintaining DFT accuracy, was used to predict new (CsPbI/Br)3 structures. The cluster expansion method generated 42 new stable (CsPb)xIyBrz (where x = 1 to 3 and y and z = 1 to 8) structures and these were ranked the meta-stable structures based on their formation enthalpies. Monte Carlo calculations showed that CsPbI0.5Br0.5 composition separates into different phases at 300K, but changes to homogeneous phase at 700 K, suggesting that a different phase of CsPbI3 may exist at higher temperature. Among the 42 predicted structures, randomly selected structures around iodide rich, 50:50 and bromine rich sites were studied further by determining their electronic, optical, mechanical and thermodynamic properties using DFT. The materials possess similar properties as cubic Br doped CsPbI3 perovskites. The mechanical properties of these compounds revealed that they are ductile in nature and mechanically stable. In summary, the thesis present a novel work on introduction of impurities into CsPbI3 perovskite material as well as compositional engineering to alter its electronic and optical properties for solar cells application.Item Optimisation of Kick Latency for Enhanced Performance of Robots in the RoboCup Three-Dimensional League through Proximal Policy Optimisation (PPO)(University of the Witwatersrand, Johannesburg, 2024-07) Nekhumbe, Humbulani Colbert; Ranchod, PraveshThis study aimed to enhance the kicking ability of Nao robots in the three-dimensional RoboCup simulation by addressing a crucial challenge observed in the University of Witwatersrand RoboCup team. The focal challenge revolved around a noticeable delay and slow movement manifested by the robot during ball kicks, leading to vulnerabilities in ball possession against opposing teams. To surmount this challenge, the implementation of Proximal Policy Optimisation (PPO), a methodology pioneered by OpenAI, was advocated. The precise objective was to optimise kick parameters, with a primary emphasis on curtailing kick latency. This optimisation aimed to ensure swift and accurate execution across various kicking scenarios, encompassing actions like propelling the ball into the opponent’s territory to bolster ball possession and thwart adversary manoeuvres. Harnessing the iterative advancements embedded in PPO, the successor to Trust Region Policy Optimisation (TRPO), the endeavour was to refine the kicking behaviour of Nao robots. This optimisation process significantly reduced the observed kick delay, and this made the robot more agile and effective at competing in the complex three-dimensional RoboCup simulation environment. The study’s outcomes highlighted substantial progress in reducing kick latency and improving the adaptability of robotic soccer players, opening up possibilities for further exploration in reinforcement learning for autonomous agents.Item Search for high-mass resonances in the Zgamma channel and Quality assurance of Scintillation detector modules of Tile Calorimeter Phase-I Upgrade of the ATLAS detector(University of the Witwatersrand, Johannesburg, 2024-09) Mokgatitswane, Gaogalalwe; Ruan, Xifeng; Solovyanov, Oleg; Mellado, BruceThis thesis presents a search for narrow, high-mass resonances decaying to a Z boson and a photon (Zy) in the final state. The analysis utilizes the full Run 2 dataset collected by the ATLAS experiment at the CERN Large Hadron Collider (LHC), corresponding to an integrated luminosity of 140 fb-1 of proton-proton col- lisions at a center-of-mass energy of ps = 13 TeV. The search focuses on a mass range of 220 GeV and 3400 GeV, aiming to identify deviations from the expected background arising from Standard Model processes. A small excess is observed at 250 GeV within the area of interest, with a combined significance of 2.1 standard deviations, indicating the need for further investigation with more data. Upper limits are set on the production cross-section times branching ratio for resonances decaying to Zy across the investigated mass range. When considering spin-0 resonances produced through gluon-gluon fusion, the observed limits at a 95% confidence level range from 65.5 fb to 0.6 fb. For spin-2 resonances produced via gluon-gluon fusion (with quark-antiquark initial states), the limits vary between 77.4 (76.1) fb and 0.6 (0.5) fb. The thesis also highlights the successful Phase-I upgrade of the Tile Calorimeter in the ATLAS detector, ensuring its continued performance. This involved the replacement of degraded Gap-Crack scintillators and Minimum Bias Trigger Scintillators (MBTS) with non-irradiated ones, along-side optimising their geometry, all in preparation for data taking during LHC Run 3. These upgrade endeavors encompassed the design of new Gap-Crack and MBTS counters, including extensions to higher rapidity, the assembly of these counters, their rigorous qualification, and characterization using radioactive sources (90Sr and 137Cs), along with their seamless integration onto the ATLAS detector.Item The Impact of the Just Energy Transition on Job Creation in South Africa: A Case of the Wind Energy Sector(University of the Witwatersrand, Johannesburg, 2024-10) Zondi, Nomvula Beryl; Ngubevana, LwaziSouth Africa's ratification of the Paris Agreement, an international accord established during the Conference of the Parties (COP21) in December 2015, underscores its dedication to confronting climate change and enacting measures to counteract human-induced global warming. Aligned with numerous other nations, South Africa is actively promoting a just energy transition that prioritises the Sustainable Development Goals (SDGs) established by the United Nations General Assembly on September 25, 2015. This transition entails a shift from reliance on carbon-intensive fuels like coal towards renewable energy sources. Given South Africa's substantial coal reserves—ranking sixth globally coal mining and processing has historically played a central role in the nation's energy sector, industrialisation, and economy, contributing significantly to its GDP and providing employment for up to 150,000 individuals throughout the coal value chain. However, the imperative for an energy transition to mitigate climate change and fulfil the country's National Determined Contributions necessitates structural changes that will inevitably impact socio-economic dynamics. Notably, disruptions in the coal value chain will have profound repercussions on both direct and indirect employment and the communities and local economies reliant on the coal industry. The Integrated Resource Plan (IRP) 2010-2030 outlines South Africa's energy trajectory of increasing renewable energy sources into the energy mix. This study focused on wind energy deployment, with a targeted capacity of 17,742 MW by 2030. It aims to identify critical considerations essential for facilitating a just energy transition that maximises job creation in South Africa. Applying a qualitative research methodology, semi-structured interviews were conducted to gather data, utilising a deductive approach based on predetermined themes derived from existing literature on factors influencing job creation. Findings underscore the importance of addressing policy implementation gaps related to the socio-economic impact of the transition, establishing clear directives for skills development in the low-carbon economy, and prioritising local manufacturing and assembly of turbine components. Moreover, the study highlights wind energy deployment as a catalyst for fostering the development of a low-carbon ecosystem while advocating for exploring broader low-carbon value chains such as green hydrogen to enable new industries and employment opportunities. Finally, the study underscores the need for focused attention and investment in research and development of clean coal technologies, offering a potential avenue for sustainable coal utilisation devoid of adverse greenhouse gas emissions and environmental impacts.Item Incorporating complex adaptive systems concepts in ontology driven Bayesian network models : towards resolving wicked problems(University of the Witwatersrand, Johannesburg, 2024-08) Semwayo, Daniel Tembinkosi; Ajoodha, RiteshWicked problems are complex ill defined problems very difficult to solve tractably using analytical methods and interventions. They include problems like pandemics, climate change effects, traffic jams, and financial market crashes. Attempts at solving such problems using analytical methods tend to produce counter-intuitive, unpredictable pathological outcomes. Wicked problems emerge, in part from the character of complex adaptive systems, and from stakeholder disagreements on their definition and resolution. We argue that baseline Bayesian models do not have adequate constructs to provide compact, and tractable modelling support for wicked problems. Applying an iterative and rigorous abductive design science research methodology, an ontology driven Bayesian modelling framework is applied to design the Granular Niche probabilistic Bayesian model, a formal, ontologically sound, and explainable artificial intelligence model, incorporating complex adaptive systems theory concepts: context; granularity; and perspective, as constructs. Using evaluation metrics from applicable kernel theories comparative evaluation of the model is carried against baseline Bayesian models. The results indicate that the novel model out-performs baseline Bayesian models against the following evaluation criteria: i) complex adaptive systems’ representation accuracy and precision; ii) structure learning; iii) parameter estimation; iv) knowledge discovery; and v) explicitly modelling and reconciling divergent multiple stakeholder perspectives of a given wicked problem.Item Magnetic field strength estimations for the main phases of solar cycles 13-24 using magnetohydrodynamic Rossby waves in the lower tachocline(University of the Witwatersrand, Johannesburg, 2024-09) Morris, Tania Mari; Duba, ThamaThe magnetic field strength (MFS) estimates used by the existing space weather prediction models (SWPMs) are inaccurate. Consequently, it has been indicated that there is a need to find solutions to rectify the wrong assumption that the magnetic field remains constant in strength and location throughout the solar cycle. This study explores a solution to this problem by increasing the granularity and accuracy of the previous MFS estimations by calculating them for the main phases of the solar cycle (solar minimum and maximum) per hemisphere for solar cycles 13-24. A dispersion relation of the fast magnetohydrodynamic (MHD) Rossby wave was derived analytically in spherical coordinates that included a toroidal magnetic field and latitudinal differential rotation, which adequately captures the dynamics of the lower tachocline. Secondly, a change to the methodology of calculating the MFS that utilises the established connection between the observed Rieger-type periodicity (RTP) in solar activity of 150-190 days and the fast MHD Rossby wave in the lower tachocline was proposed. Furthermore, a new magnetic field profile (MFP) of Bφ = B0 sin(6Θ) was introduced to improve the model results. This MFP has a maximum and minimum value at the same latitudes associated with sunspot appearances during these extreme solar cycle phases. Consequently, the MFS values were calculated at a latitude of 29◦ (solar min) and 16◦ (solar max) using the hemispheric RTP data. The average MFS (RTP) for the solar min and solar max in the dominant hemisphere was established to be 8 kG (212 days) and 76 kG (163 days), respectively. For the non-dominant hemisphere, the average MFS was established to be 5 kG (213 days) and 50 kG (183 days) for the solar min and max, respectively. The results of this study show a significant difference in the results based on latitude. The findings have also revealed that the periodicity of increased solar activity associated with a specific MFS is affected not only by the solar cycle strength and hemispheric asymmetry but also by the solar cycle phase (or latitude) considered. Additionally, we strongly argue that this study’s MFS results represent reality more closely than previously calculated results. Therefore, we propose that the MFS estimates reported in this study should be considered for the input to various existing space weather prediction models.Item Towards Lifelong Reinforcement Learning through Temporal Logics and Zero-Shot Composition(2024-10) Tasse, Geraud Nangue; Rosman, Benjamin; James, StevenThis thesis addresses the fundamental challenge of creating agents capable of solving a wide range of tasks in their environments, akin to human capabilities. For such agents to be truly useful and be capable of assisting humans in our day-to-day lives, we identify three key abilities that general purpose agents should have: Flexibility, Instructability, and Reliability (FIRe). Flexibility refers to the ability of agents to adapt to various tasks with minimal learning; instructability involves the capacity for agents to understand and execute task specifications provided by humans in a comprehensible manner; and reliability entails agents’ ability to solve tasks safely and effectively with theoretical guarantees on their behavior. To build such agents, reinforcement learning (RL) is the framework of choice given that it is the only one that models the agent-environment interaction. It is also particularly promising since it has shown remarkable success in recent years in various domains—including gaming, scientific research, and robotic control. However, prevailing RL methods often fall short of the FIRe desiderata. They typically exhibit poor sample efficiency, demanding millions of environment interactions to learn optimal behaviors. Task specification relies heavily on hand-designed reward functions, posing challenges for non-experts in defining tasks. Moreover, these methods tend to specialize in single tasks, lacking guarantees on the broader adaptability and behavior robustness desired for lifelong agents that need solve multiple tasks. Clearly, the regular RL framework is not enough, and does not capture important aspects of what makes humans so general—such as the use of language to specify and understand tasks. To address these shortcomings, we propose a principled framework for the logical composition of arbitrary tasks in an environment, and introduce a novel knowledge representation called World Value Functions (WVFs) that will enable agents to solve arbitrary tasks specified using language. The use of logical composition is inspired by the fact that all formal languages are built upon the rules of propositional logics. Hence, if we want agents that understand tasks specified in any formal language, we must define what it means to apply the usual logic operators (conjunction, disjunction, and negation) over tasks. The introduction of WVFs is inspired by the fact that humans seem to always seek general knowledge about how to achieve a variety of goals in their environment, irrespective of the specific task they are learning. Our main contributions include: (i) Instructable agents: We formalize the logical composition of arbitrary tasks in potentially stochastic environments, and ensure that task compositions lead to rewards minimising undesired behaviors. (ii) Flexible agents: We introduce WVFs as a new objective for RL agents, enabling them to solve a variety of tasks in their environment. Additionally, we demonstrate zero-shot skill composition and lifelong sample efficiency. (iii) Reliable agents: We develop methods for agents to understand and execute both natural and formal language instructions, ensuring correctness and safety in task execution, particularly in real-world scenarios. By addressing these challenges, our framework represents a significant step towards achieving the FIRe desiderata in AI agents, thereby enhancing their utility and safety in a lifelong learning setting like the real world.Item Mapping and monitoring the impacts of climate variability on rainfed agriculture in Semi-arid North Darfur, Sudan(University of the Witwatersrand, Johannesburg, 2024-02) Altoom, Mohammed Bashar Adam; Adam, ElhadiRainfed agriculture is vital to food security and income in most parts of the world. However, one-third of the population of developing countries population lives in the less favoured rainfed agricultural regions. Around 75-82% of the total cropland areas in the world are under rainfed agriculture and produce more than 60% of the globe’s cereal grains. However, rainfed agriculture is most prominent in some regions of Africa, such as Sub-Saharan Africa, where more than 95% of the cropland is rainfed. This crucial agriculture sector usually depends on the physical environment and, most importantly, the variability and distribution of rainfall. Therefore, rainfed farming is vulnerable to climate-related hazards, and the crop yield is unreliable and difficult to predict. For instance, the spatio-temporal variability of precipitation extreme events often subjects crops to short-term water deficits, causing crop losses. Sudan heavily depends on rainfed agriculture—about 90% of arable land dominates rainfed cultivation, contributing one-third of the country’s gross domestic product (GDP). Rainfed agriculture is the primary source of livelihood for 65% of the population. Unfortunately, agriculture in North Darfur of the west Sudan is characterised by environmental hazards, e.g., frequent droughts and unpredictable low, poorly distributed, and highly variable monthly/seasonal rainfall. Therefore, using various Earth observation data, this study aimed to monitor the impacts of rainfall variability on rainfed agriculture in North Darfur State in Sudan. Firstly, the study aimed to determine the feasibility of estimating rainfall variability across North Darfur State at daily, monthly and annual timescales using six satellite precipitation products (SPPs), i.e., the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology (ARC), and Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) were evaluated using four categorical indices, i.e., probability of detection (POD), probability of false alarm (POFA), bias in detection (BID) and Heidke skill score (HSS), and four continuous indices, i.e., Pearson correlation coefficient (r), root mean square error (RMSE), per cent bias (Pbias), and Nash-Sutcliffe model efficiency coefficient (NSE) against ground rain-gauge observations. The other SPPs were Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run (GPMIMERG), Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT). Results of the statistical analysis demonstrated that 1) at the daily timescale, the SPPs underestimate daily rainfall by 6.53–17.61%, and CHIRPS was the best for detecting rainy days, while PERSIANN-CDR performed poorly; 2) monthly and annual scales performed better than daily timescale, and TAMSAT and CHIRPS portrayed better performance than the ther SPPs. Secondly, the study assessed the capability of optical Earth Observation Data (EOD), i.e., Sentinel-2 multispectral dataset, to map crop types in the heterogeneous semi-arid environment of North Darfur using machine learning classifiers in Google Earth Engine (GEE) platform. Five datasets were compared against random forest (RF) and support vector machine (SVM) classification algorithms: (1) 10 Sentinel-2 bands (comprising visible, near-infrared and shortwave infrared bands), (2) Sentinel-2 (10 bands) + 8 vegetation indices, (3) visible bands and near-infrared bands only, (4) visible and shortwave infrared bands only, and (5) 8 vegetation indices. The eight vegetation indices were normalised difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), green normalised difference vegetation index (GNDVI, weighted difference vegetation index (WDVI), red edge NDVI (NDVIre), ratio-vegetation index (RVI) and normalised difference infrared index (NDII). Results showed that the RF algorithm produced the highest classification overall accuracy (OA), i.e., 97% and Kappa coefficient (κ), 0.96, using 10 Sentinel-2 bands dataset. Producer’s (PA) and user’s accuracies (UA) were in the range of 40-97% and 40-100%, respectively. Thirdly, the spatiotemporal trend of drought events and their impact on millet production in North Darfur from 1981 to 2020 was analyzed using standardized precipitation index (SPI) and reconnaissance drought index (RDI) by employing different timescales, i.e., 3- month (June-August), 6-month (June-November), and 9-month (June-February) timescales. Drought-yield relationships were assessed using Pearson correlation coefficients (r). Results indicated that RDI is more sensitive to rainfall variabilities than SPI in detecting drought trends. Results revealed that drought events affected North Darfur over broad spatial extents, particularly in 1989, 1990, 1992, 1999, and 2001—an extreme drought event was in 2003. Correlation analysis between the SPI and RDI and the standardized variable of crop yield (SVCY) for millet grain yield showed a strong agreement between them. Moderate to extreme reductions in millet crop yield occurred in 1992, 1999, 2001, and 2003, corresponding to the moderate to extreme drought indicated by RDI. Severe crop losses were in Kabkabiya and Umm Kadadda. Fourthly, this study aimed to map and monitor spatio-temporal dynamics of rainfed agriculture in North Darfur State from 1984 to 2019 using multitemporal Landsat observation data using random forest (RF) classification algorithm. Overall, Landsat Operational Landsat Imageries (OLI) outperformed Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+) in monitoring change in agricultural land and other land use land cover (LULC) classes. Overall accuracies ranged between 94.7% and 96.9%, while kappa statistics were greater than 0.90. Results showed that Goz land used for rainfed agriculture increased by 889,622.46 ha between 1994 and 999, while it decreased by 658,568.61 ha between 2004 and 2009. Rainfed cultivation of wadi lands expanded significantly by 580,515.03 ha over the 2014–2019 period and decreased by 182,701.8 ha over the 1994–1999 period. Overall, this study enhances the understanding of spatio-temporal rainfall patterns and current drought trends, aiding in developing more effective policies and resource management strategies. Additionally, it offers crucial spatial data that is currently scarce due to ongoing conflicts, empowering decision-makers to establish sustainable land use monitoring systems. The methodologies used in this study have proved successful in mapping crop types in a fragmented highly heterogeneous fine agricultural semi-arid landscape; such mapping approaches can be applied in other environments with similar characteristics.Item Evaluation of radiation damage on lutetium-aluminium and gold for practical applications using proton irradiation as a surrogate for neutrons(University of the Witwatersrand, Johannesburg, 2024-10) Temaugee, Samuel Terungwa; Mavunda, Risimati D.; Usman, Iyabo T.An understanding of the interaction of energetic radiations from particles such as protons, neutrons, and photons, with the microstructure of materials is crucial for predicting their bulk morphological response in extreme radiation environments. Exposure to these radiation species could lead to changes in the microstructural properties that, in turn, affect the mechanical and physical properties of the macroscopic matter. This thesis investigated the resilience of materials, specifically Au and Lu-Al, to radiation damage, employing computational simulation methods and experimental techniques. The study aims to provide critical insights into the radiation damage sturdiness of Au and Lu-Al, considering their application in reactor technology and other extreme radiation environments. Monte Carlo-based methods were employed to calculate radiation damage in the samples resulting from neutron and proton irradiation, utilizing MCNP6.2 and SRIM-2013, respectively. The objective was to compare ion beam irradiation with neutrons with a view to utilizing proton irradiation as a surrogate for neutron irradiation. Three different state-of-the-art characterization techniques—X-ray diffraction (XRD), High-Resolution Transmission Electron Microscopy (HRTEM), and Flash Differential Calorimetry(F-DSC)—were employed to evaluate damage in the materials before and after proton irradiation using the CLASS Accelerator at MIT, USA. The results of the study indicated that protons within the energy range 0.1 to 1.0 MeV produced similar types of damage in the materials as would neutrons (spectrum 0< E≤20 MeV at SAFARI reactor), suggesting protons as an alternative to neutron irradiation. Defect characterization in the materials using XRD and HRTEM techniques revealed dislocation loops and lines in both Lu-Al and Au, as well as Stacking Faults Tetrahedra (SFT) in the Au material. These defects with proton irradiation were similar to those observed with neutron irradiation in Au and other aluminum alloys. The estimated defect number density ranged from 0.7 to 4.8 × 1014 m−2, showing an increase with rising displacements per atom (dpa) or proton fluence post-irradiation. Lu-Al exhibited higher defect density values than Au, consistent with Monte Carlo simulations. Furthermore, results from the Flash DSC technique revealed significant changes in the characteristics of the power-temperature profiles (melting curves) of Lu-Al as dpa increased, offering insights into radiation-induced processes such as phase transition and precipitate stability at specific defect annealing temperatures. These findings are crucial for radiation damage studies for the binary alloy and warrant further investigation. The observed microstructural defect densities were relatively high, and prolonged exposure of the materials to higher doses could lead to further changes in microstructural properties, consequently influencing the physical and mechanical properties of the macroscopic material.Item Physical property studies, tunnel numerical simulations and in-mine seismic experiments to image the gold orebody at South Deep Gold Mine(University of the Witwatersrand, Johannesburg, 2024-09) Mulanduli, Omphulusa; Manzi, MusaThe investigation endeavors to assess the physical characteristics of deep borehole cores within the Upper Elsburg Reefs (UER) of the South Deep gold mine of the West Rand goldfield. Specifically, these cores are sourced from three boreholes situated approximately 2.6 km beneath the surface within the confines of the South Deep gold mine. The focal point of this study lies in non-destructive testing methods aimed at elucidating the intrinsic attributes of these rocks, with particular attention directed towards seismic velocities and densities. These measurements hold paramount importance in conducting numerical simulations to designing the in-mine (or tunnel) seismic reflection surveys acquired at South Deep gold mine, as part of the ERA-Min3 FUTURE (Fiber-optic sensing and UAV-platform techniques for innovative mineral exploration) project. Cultivating a profound comprehension of the seismic velocities and densities across diverse rock formations can significantly augment the interpretation of seismic reflections, thereby facilitating more refined assessments of subsurface geology and structural configurations. In pursuit of this goal, our study endeavors to delve into the fundamental acoustic properties of the gold-rich UER, with the overarching aim of deepening our understanding of its seismic reflectivity. To realize this objective, a comprehensive array of physical measurements, encompassing ultrasonic velocities and bulk densities, were conducted on drill-core specimens. To accurately portray the physical attributes of the lithological units under scrutiny, a total of twenty-four samples were subjected to exhaustive analysis for density and seismic velocity utilizing a spectrum of methodologies. Density determinations were procured through a diverse set of techniques, including dimensional assessments, employment of the KT20 MagSus tool, and utilization of the SNOWREX AHW-3 Professional Weighing Scale boasting a heightened sensitivity of 0.01 g. Ultrasonic measurements were undertaken employing the Proceq Pundit PL 2000 ultrasonic pulser velocity tester, equipped with two pairs of transducers boasting a center frequency of 54 kHz. The in-mine seismic survey was acquired to delineate geological structures that crosscut and displace the orebody. The study locale encompasses three distinct rock formations: the UER, gold-bearing conglomerate units (termed reefs), basaltic lava, and dyke specimens. The UER primarily comprises quartzites, exhibiting a P-wave velocity range of 5202-5802 m/s, an S-wave range of 3037-4768 m/s, and bulk densities spanning from 2.66 - 2.71 g/cm³. Conglomerate reefs exhibit a P-wave velocity range of 4467-5970 m/s, an S-wave range of 4040-4854 m/s, and bulk densities ranging from 2.67-2.94 g/cm³. Lava samples extracted from the boreholes showcase a P-wave velocity range of 5916 - 6711 m/s, an S-wave range of 3275-5659 m/s, and bulk densities spanning from 2.75-2.90 g/cm³. Singular dyke samples were encountered, exhibiting a P-wave velocity of 5921.5 m/s, an S-wave velocity of 5385 m/s, and a density of 2.85 g/cm³. The study employed the synth-seis code to simulate 1D seismic responses based on borehole data collected from the mine, aiming to validate findings from velocity and density measurements. Analysis of the seismograms indicated notable contrasts between conglomerates and quartzites, particularly evident in density and S-wave measurements, suggesting potential for improved rock discrimination with alternative seismic sources. Additionally, 2D numerical simulations were conducted to model wave propagation in the Upper Elsburg Reef (UER), revealing discrepancies between simulated and synthetic seismogram results, indicating potential limitations in seismic imaging. Furthermore, ray tracing was used to design a seismic survey inside the mine along the tunnel floor to image VCR (Ventersdorp Contact Reef) orebody and other geological structures. The real seismic survey was finally conducted inside the tunnel (SDT1), demonstrated the value of in-mine reflection seismic surveys for mapping geological structures at significant depths, which would otherwise be costly and logistically challenging. Despite noise interference from mine operations, processing algorithms enabled extraction of reflections and structural mapping from the dataset, underscoring the importance of such surveys in mining exploration and planning.