A multiple algorithm approach to the analysis of GNSS time series for detecting anomalous behaviours, geohazards and meteorological eventsTools Habboub, Mohammed (2019) A multiple algorithm approach to the analysis of GNSS time series for detecting anomalous behaviours, geohazards and meteorological events. PhD thesis, University of Nottingham.
AbstractIn this study, a multiple algorithm approach to the analysis of GNSS time series for detecting anomalous behaviours, geohazards and meteorological events is proposed. This multiple algorithm approach includes the novel use of temporal analysis, spatial analysis and a combined (temporal and spatial) analysis. The main concept of this approach is to first model the normal and expected behaviour of the temporal and spatial dependencies of initial GNSS time series. Then, to assess the differences between the modelled temporal and spatial dependencies and new GNSS time series. More specifically, in the temporal analysis algorithm, an Artificial Neural Network is used to extract the temporal dependency of the GNSS station coordinate or troposphere time series. On the other hand, the spatial autoregressive model is used for the spatial analysis algorithm, assuming that the GNSS coordinate or troposphere time series from a network of stations are spatially dependent. Finally, for the combined analysis algorithm, the two above methods of the temporal and the spatial analysis algorithms are adjusted and combined. This multiple algorithm approach was examined using: (i) the long-term, daily GPS coordinate time series of the BIGF network of stations in the British Isles; (ii) the 1Hz GPS coordinate time series of the GEONET stations in Japan for the Tohoku-Oki 2011 Mw9.0 earthquake; (iii) the long-term, hourly GPS troposphere time series of the BIGF network of stations in the British Isles. It was demonstrated in these case studies that the temporal analysis algorithm proved to be effective in detecting rapid changes in GNSS time series of varying magnitudes. These changes can be site-specific (e.g. an offset) or of a large scale (e.g. an earthquake or a meteorological event). On the other hand, the spatial analysis algorithm is more suitable for spatially uncorrelated, slow-paced and small magnitude changes in GNSS time series, while the combined analysis algorithm seems to be less beneficial in the detection of anomalous behaviours, but may be useful to diagnose and clarify the reasons that lead to the detected anomalous behaviours. The research suggests that the multiple algorithm approach could be very useful in supporting the operation of existing GNSS networks and in the analysis of GNSS coordinate and troposphere time series.
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