https://www.um.edu.mt/library/oar/handle/123456789/86230 Sat, 11 Apr 2026 01:33:08 GMT 2026-04-11T01:33:08Z https://www.um.edu.mt/library/oar/handle/123456789/96424 Title: High-frequency ground motion scaling and ground shaking scenarios for earthquakes in central Greece Abstract: Assessing seismic hazard is important to consider whilst studying the seismology of a region. For seismologists and structural engineers, earthquake ground motion prediction is a crucial aspect of their work. Latest national hazard maps facilitate the planning and design of earthquake resistant infrastructure. These maps are produced after a precise calibration of ground motion predictive relationships, which are calculated as a function of distance from the source, magnitude, and frequency for the region using various mathematical and data processing techniques such as regression analysis. The aim of this study is to provide a complete description of the characteristic of the ground-motion for the Corinth Gulf region, for which this has not been done so far. Waveforms from around 297 events were obtained from 65 three-component stations around central Greece, all part of the Hellenic Unified Seismic Network. For this region, we employed a general form for a predictive relationship including the source excitation term, an attenuation operator and an operator to account for the site effect. The functional form of the crustal attenuation term depends principally on the attenuation parameter and on the geometrical spreading. Excitation terms are modelled by using a Brune spectral model. Simulations are carried out using EXSIM and ground motion scenarios (in terms of peak ground acceleration and peak ground velocity as a function of magnitude and distance) are computed for the study area. Furthermore, it is envisaged that the results obtained can later be used for upgrading seismic hazard maps and for engineering designs as well as implementing tools like ShakeMap®, as well as to be used for implementing evacuation plans and risk mitigation strategies. Description: Ph.D.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/96424 2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/91973 Title: Enhancing citizen science campaigns through artificial intelligence methods Abstract: Many studies show that citizen science initiatives are a very useful tool for data collection and a way to overcome limitations of time and resources. The first part of this study focuses on the creation of a national database that formally documents all the marine alien species found within Maltese waters, including the sighting date and time, location, photographic evidence, name of the species, and other information. The second part is dedicated to the applicability of machine learning methods for marine species identification. Hundreds of photos that were submitted to the “spot the Alien” initiative were used to train a region-based, convolution neural network. The main aim was to develop a model that can classify and distinguish between the eight most recorded marine alien species within Maltese waters: 'Abudefdud saxalitis', 'Acanthus monroviae', 'Staphanolepis diaspros', 'Portunus segnis', 'Seriola fasciata', 'Siganus luridus', 'Aplysia dactyomela', 'Lagocephalus sceleratus'. A number of metrics were calculated to quantify the reliability of the model. The use of the model can reduce or even eliminate, the need for human expert intervention in validating citizen science reports and will provide prompt feedback to the citizen scientist submitting the report. In addition, a web portal with visualization tools to help display the information in database, was implemented. This point of reference allows users to upload images of marine alien species, which can automatically be classified by the R-CNN. The reports will continue to populate the national database. This work will enhance citizen science campaigns that have been running for several years and that target the monitoring of the influx of alien fish into Maltese waters. Description: M.Sc.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/91973 2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/91968 Title: Data-Driven and theory-guided pseudo-spectral seismic imaging using deep neural network architectures Abstract: Full Waveform Inversion seeks to achieve a high-resolution model of the sub-surface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technology, FWI has limitations related to the choice of the appropriate solver for the forward problem in challenging environments requiring complex assumptions, and very wide angle and multi-azimuth data necessary for full reconstruction are often not available. Deep Learning techniques have emerged as excellent optimization frameworks. These sit on a spectrum between data and theory-guided methods. Data-driven methods impose no physical model of wave propagation and are not exposed to modelling errors. At the opposite end of the spectrum there are deterministic models governed by the laws of physics. In between, there are theory-guided methods which have some fixed parameters able mimic physical processes. This enables more intelligibility as compared to purely data driven approach. Application of seismic FWI has recently started to be investigated within Deep Learning. This has focussed on the time-domain approach, while the pseudo-spectral domain has not been yet explored. However, classical FWI experienced major break-throughs when pseudo-spectral approaches were employed. This thesis addresses the lacuna that exists in incorporating the pseudo-spectral approach within Deep Learning. This has been done by re-formulating the pseudo-spectral FWI problem as a Deep Learning algorithm for both a data-driven and a theory-guided pseudo-spectral approach. A deep neural network (DNN) and recurrent neural network (RNN) framework are derived. Either was formulated theoretically, qualitatively assessed on synthetic data, applied to a two-dimensional Marmousi dataset and evaluated against deterministic and time-based approaches. Inversion of data-driven pseudo-spectral DNN was found to outperform classical FWI for deeper and over-thrust areas. This is due to the global approximator nature of the technique and hence not bound by forward-modelling physical constraints from ray-tracing. Pseudo-spectral theory-guided FWI using RNN was shown to be more accurate with only 0.05 error tolerance and 1.45% relative percentage error. Indeed, this provides more stable convergence, able to identify faults and has more low frequency content than classical FWI. From the comparative analysis of data-driven DNN and theory-guided RNN approaches, DNN was better performing, and recovered more of the velocity contrast, whilst RNN was better at edge definition. In general, RNN was more suited in shallow and deep sections due to cleaner receiver residuals. Besides showing the improved performance of FWI formulated as a Deep Learning approach, this thesis highlighted the significant potential of such methods in other fields which have so far not been explored. New research avenues resulting from the shift in the inversion paradigm were identified and the next steps on how to continue developing these two frameworks presented. Description: Ph.D.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/91968 2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/86248 Title: Wave zonation for the Maltese islands Abstract: Malta is a small island in the middle of the Mediterranean. The sea is an integral part of our daily life and also an important economic sector. Notwithstanding all the advances in technology, the sea also poses its hazards. Studies have shown that worldwide 0.3% of all commercial vessels are lost at sea each year claiming the lives of an average of 1,000 people per year. Though not the only cause of the incidents, waves and bad weather could be the instigators for the incidents to occur. It was also found that in most cases (approximately 66%) the incidents occurred in less than extreme conditions. The weather forecasts for mariners are mainly based on Significant Wave Height Hs and the Mean Wave Period Tz. Based on this, the study focused on identifying other wave parameters that adversely affect the vessel stability. Besides Hs, wave length, wave steepness and individual wave height were identified to impact vessel stability. The Linear Wave Theory was subsequently applied to the data generated by the SWAN wave model, to provide operational and forecasted data for these parameters around the Maltese territorial waters. These two dimensional plots are easy to interpret and provide the vessel master with added information to enable calculated decisions to be taken based on forecasted operational data. To guarantee the long-term sustainability of coastal and off-shore projects, structures should be designed to withstand the most extreme wave conditions during their life time. This study carried out an Extreme Value Analyses (EVA) to determine the highest Significant Wave Height Hs that can hit the Maltese islands with a return period of N years, N being 50, 100 or more years depending on the end application. For this purpose, various distributions and extreme parameter analysis were used. Results were compared to determine the most accurate estimate of the 100-year return period significant wave height, Hs100. The Hs100 was ultimately calculated to be 8.8 m. This value compares very favourably to other estimates obtained for other areas in the Mediterranean Sea. Besides the Hs100, two methods were used to determine also the highest individual wave height h that has an annual exceedance probability of 0.01. The two methods gave different values, 15 m and 13 m, with the former estimate considered to be more reliable. Through the use of wave data, this project addressed the important issue of safety at sea. This project also carried out an assessment of the extreme wave conditions. By means of forecasts in the form of 2-D plots of significant wave height, wave length, wave steepness and individual wave height, together with the EVA, this study opens the way to new products that can be offered operationally at the service of mariners and structural engineers. Description: M.Sc.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/86248 2021-01-01T00:00:00Z