https://www.um.edu.mt/library/oar/handle/123456789/11478 2026-08-31T20:09:32Z https://www.um.edu.mt/library/oar/handle/123456789/148524 Title: Time-dependent horizontal-to-vertical spectral ratio (HVSR) analysis of ambient seismic noise Abstract: Existing local research has employed the horizontal-to-vertical spectral ratio (HVSR) for static microzonation and long-term groundwater monitoring. Building on this work, this thesis provides the first high-resolution quantification of how sub-daily meteorological fluctuations, specifically wind-driven noise and atmospheric variables, directly modulate seismic site response parameters across the stations of the Malta Seismic Network in the Maltese archipelago. By establishing these correlations, this study enhances the methodological accuracy of HVSR in the Maltese context, providing a framework to isolate meteorological noise from geological signals and enabling more reliable site response assessments for local engineering and microzonation projects. This study analyses four years (2017-2020) of continuous seismic data recorded by the Malta Seismic Network to investigate temporal variability in HVSR parameters. The research evaluates how HVSR peak frequency and amplitude evolve and examines the potential influence of meteorological variables, including wind speed, precipitation, temperature, and relative humidity. Statistical analysis is used to identify stations that exhibit sensitivity to environmental forcing and to assess the extent to which such factors may influence the stability and interpretation of HVSR measurements in the Maltese Islands. Forward modelling was also used to confirm whether the observed meteorological effects can potentially create subsurface parameter changes, such as variations in soil moisture or density, which could affect the HVSR measurements. This thesis further confirms that HVSR parameters are sensitive to wind strength and reveals that two stations (MELT and CBH9) have a heightened sensitivity to wind speed compared to other stations within the Malta Seismic Network. Hence, caution should be exercised when analysing data from these stations during windy days. Association between HVSR and other meteorological parameters, including precipitation, revealed weak effect sizes, indicating that other short-term meteorological forcing is not registered by HVSR. Description: B.Sc. (Hons)(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145690 Title: Non-commutative probability : from classical to quantum probability Abstract: In classical probability theory, it is implicitly assumed that random variables commute. However, this assumption does not necessarily hold in all mathemat ical frameworks, such as those involving matrices. In this thesis, we will explore quantum probability, a non-commutative extension of classical probability. The main aim of this thesis shall be to examine how key concepts from classical probability can be generalized in the quantum setting. Description: M.Sc.(Melit.) 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145381 Title: Innovative methods for detecting sea turtle nests : a combination of UAV photogrammetry, GPR, and artificial intelligence for non-invasive monitoring and conservation Abstract: Sea turtle nesting represents one of the most vulnerable stages in their life cycle; therefore, protecting nesting sites is essential for the long-term survival of their populations. Traditional nest detection methods are often invasive and may disturb nesting females. This study introduces a non invasive approach for detecting and monitoring sea turtle nests through the combined use of advanced technologies. Specifically, Ground Penetrating Radar (GPR) and Artificial Intelligence (AI) are employed to automatically identify turtle tracks and assist in locating potential nesting sites. As part of this study, fieldwork was conducted at Golden Bay, Malta, where a simulated nest of loggerhead turtle (Caretta caretta) was put together to evaluate how effectively and accurately GPR can find an underground chamber containing eggs. To confirm the radar data, a 3D LiDAR model was made of the internal structure of the simulated nest, thus providing a reference dataset for the interpretation of radargrams. Meanwhile, an AI algorithm was instructed to automatically recognize turtle tracks from beach photos, thus facilitating the identification of potential nesting areas. The integrative approach of these techniques demonstrates the potential of non-invasive technologies to enhance the efficiency of sea turtle nest detection and conservation. The findings contribute to the development of modern conservation strategies, particularly within small Mediterranean rookeries such as Malta, where nesting events are rare and spatially constrained. Description: M.Sc.(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145380 Title: High–resolution 3D reconstruction of sea caves in Malta through underwater photogrammetry techniques Abstract: This thesis aims to develop a high-resolution, three-dimensional photogrammetric model of a selected sea cave in the Maltese Islands, this will allow for the monitoring of geomorphic change and the rate of coastal erosion. The resulting model will provide a spatially accurate and visually detailed baseline for scientific analysis of coastal geomorphology and long-term monitoring of erosional processes with data integrated from aerial, terrestrial, and underwater sources. This model will combine data sets from terrestrial, submerged and aerial views of the cave, something that at the time of writing has yet to be done. Data collection was accomplished via the use of two GoPro 7 Black editions for the photogrammetric model and an iPhone 15 for a LiDAR model of the terrestrial component of the cave, used by hand as a team member walked the accessible regions of the cave. A GoPro 13 black edition was carried by a second team member whilst snorkelling in grid patterns at the surface of the submerged portion. Finally, a DJI Mavic 3 multispectral drone was used for the aerial components of the site, flown from a promontory above the cave site itself. The data collected was processed through Agisoft Metashape Professional v2.2.1 (Agisoft LLC, St Petersburg, Russia) with a model being created for each component of the cave. The four models once processed were integrated to form one model with scaling accuracy confirmed by the LiDAR model. The level of accuracy in the model allowed for specific measurements to be taken such as width or height, these measurements could allow for the calculation of the mass of rock likely to fall or give bathymetric data on the current submerged section. The combination of terrestrial, underwater, UAV, and LiDAR photogrammetry proved to be a robust approach for capturing both the external and internal morphology of the cave. Each method contributed complementary datasets: UAV photogrammetry effectively mapped the promontory and entrance geometry, while underwater and terrestrial images documented the cave’s internal surfaces in high detail. The integration of LiDAR scanning from the iPhone 15 enhanced the scaling accuracy of the final model, compensating for the potential geometric distortion associated with freehand image capture. This multi-platform approach aligns with recent studies that advocate for the combination of close-range photogrammetry and LiDAR to improve the geometric precision of complex natural structures (Colica et al., 2021; Furlani et al., 2023). Description: M.Sc.(Melit.) 2026-01-01T00:00:00Z