This research investigates the occurrence of EM emissions from compressed rock and assesses their value as precursors to earthquakes. It is understood that electromagnetic emissions are accompanied by crack generation in the Earth's crust, and effort has been targeted on the analysis of electromagnetic signals preceding seismic events. There is a need for a robust Data Acquisition System for the reliable collection of such signals. The design and deployment of a novel system form part of this research. The EM data collected by the Data Acquisition System is subsequently analysed and correlations are made with natural phenomena. The design of the Data Acquisition System is presented and meets a specification which includes accuracy, robustness, power consumption, remote configurability achieved by the development of a novel architecture for flash memories which significantly increases the live span of these devices. The measuring of electromagnetic emissions should be performed by reliable systems, using devices that fully correspond to the specifications set by the needs of this research. This type of systems is not fully covered by existing commercial devices. These prototype VHF field stations (ground base - electromagnetic variation monitors in VHF band) are located around the Hellenic Are. This region is one of the most seismically active regions in western Eurasia due to subduction of the oceanic African lithosphere beneath the Eurasian plate. After approximately two years of electromagnetic VHF data collection, the final stage of this project took place. In this stage, possible correlation between naturally occurring electromagnetic emissions in VHF band and seismic events within a predefined radius around the observation location is investigated. Supplementary, effects of alternative electromagnetic sources, such as solar activity, is considered. Whilst EM emissions from compressed rocks can be demonstrated in the laboratory, it was found from a two-year evaluation that no reliable correlation with earthquake events could be established. However, significant patterns of activity were detected in EM spectrum and it was shown that these correlate strongly with other naturally occurring phenomena such as solar flares. The Data Acquisition System as developed in this thesis has related applications in long term and remote sensing operations including meteorology, environmental analysis and surveillance.

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Full Waveform Inversion (FWI) is a depth imaging technique that takes advantage of the full information contained in recorded seismic data. FWI provide high resolution images of subsurface properties, usually seismic velocities or related parameters, although in theory it could image any property used to formulate the wave equation. The computational cost of the methodology has historically limited its application to 3D acoustic approximations but recent developments in hardware capabilities have increased computer power to the point that more realistic approximations are viable. In this work the traditional acoustic approximation is extended to include elastic effects by introducing the elastic wave equation as the governing law that describes wave propagation. I have developed a software based on finite-differences to solve the elastic wave equation in 3D, which I applied in the development of a full-waveform inversion algorithm. The software is fully parallelised for both distributed and shared-memory systems. The first level of parallelisation distributes seismic sources across cluster nodes. Each node solves the 3D elastic wave equation in the whole computational domain. The second level of parallelisation takes advantage of present multi-core computer processor units (CPU) to decompose the computational domain into different volumes that are solved independently by each core. Such parallel design allows the algorithm to handle models of realistic sizes, increasing the computational times only a factor of two compared to those of 3D acoustic full-waveform inversion on the same mesh. I have also implemented a perfectly matched layer absorbing boundary condition to reproduce a semi-infinite model geometry and prevent spurious reflections from the model boundaries from contaminating the modelled wavefields. The inversion algorithm is based upon the adjoint-state method, which I reformulated for the wave equation that I implemented, which was based on particle-velocities and stresses, providing a comparison and demonstration of equivalence with previous developments. To examine the performance of the code, I have inverted several synthetic problems of increasing realism. I have principally used only pressure sources and receivers to assess the potential of the method's application to the most common industry surveys: streamer data for offshore and vertical geophones (only one component) for onshore exploration surveys. The results show that the imaged properties increase with the heterogeneity of the models, due to the increase in P-S-P conversions which provides the main source of information to invert shear-wave velocity models from pressure sources and receivers. It remains to demonstrate the inversion of field datasets and my future research project will focused on achieving this goal.

This thesis examines the ground motion prediction component of earthquake loss estimation (ELE) frameworks and is based on the assertion that reducing the uncertainty in ground motion prediction will result in improved accuracy of loss estimates. The objective is to obtain improved ground motion predictions by identifying and quantifying the sources of uncertainty in the predictions, with particular focus on the portion of the uncertainty that can be reduced. The work presented in this thesis starts with an examination of ground motion measures commonly used in ELE and their relative utility. The ground motion measure Arias Intensity is identified as well-suited to application in a number of problems in earthquake engineering and this along with the lack of a robust equation for its prediction, leads to the development of a new predictive equation for Arias Intensity. Next, the prediction of Arias Intensity at spatially separated locations is studied in order to develop a model for the spatial correlation of Arias Intensity so that loss estimates for spatially distributed portfolios may be obtained. Thirdly, the sources of uncertainties in the predicted values of Arias Intensity are investigated and the uncertainties are characterised and quantified in order to establish whether or not they may be reduced. The impacts of these uncertainties on the new predictive equation for Arias Intensity are also examined. The final part of the thesis focusses on the use of GIS to display the information described in the previous sections on ground motion prediction. Particular attention is given to enhancing the display of uncertainties in ground motion predictions. This thesis demonstrates that the impacts of uncertainty on ground motion predictions and therefore earthquake loss estimation are significant, making this research of particular importance in this field.

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This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.