Monitoring surface deformation in real-time help at planning and protecting infrastructures and populations, manage sensitive production (i.e. SEVESO-type) and mitigate long-term consequences of modifications implemented. We present RT-SHAKE, an algorithm developed to detect ground motions associated with landslides, sub-surface collapses, subsidences, earthquakes or rock falls. RT-SHAKE detects first transient changes in individual GPS time series before investigating for spatial correlation(s) of observations made at neighbouring GPS sites and eventually issue a motion warning. In order to assess our algorithm on fast (seconds to minute), large (from 1cm to meters) and spatially consistent surface motions, we use the 1Hz GEONET GNSS network data of the Tohoku-Oki MW9.0 2011 as a test scenario. We show the delay of detection of seismic wave arrival by GPS records is of ~10 seconds with respect to an identical analysis based on strong-motion data and this time delay depends on the level of the time-variable noise. Nevertheless, based on the analysis of the GPS network noise level and ground motion stochastic model, we show that RT-SHAKE can narrow the range of earthquake magnitude, by setting a lower threshold of detected earthquakes to MW6.5-7, if associated with a real-time automatic earthquake location system.

In order to make a rational prediction of the Dead Sea shape, data were prepared for suitability map creation using Markov Chain analysis and Multi Criteria Evaluation (MCE).

Developed tools of Remote Sensing and Geographic Information System are rapidly spread in recent years in order to manage natural resources and to monitor environmental changes. This research aims to study the spatial behavior of the Dead Sea through time. To achieve this aim, time series analysis has been performed to track this behavior. For this purpose, fifteen satellite imageries are collected from 1972 to 2013 in addition to 2011-ASTGTM-DEM. Then, the satellite imageries are radiometrically and atmospherically corrected. Geographic Information system and Remote Sensing techniques are used for the spatio-temporal analysis in order to detect changes in the Dead Sea area, shape, water level, and volume. The study shows that the Dead Sea shrinks by 2.9 km 2 /year while the water level decreases by 0.65 m/year. Consequently, the volume changes by −0.42 km 3 /year. The study has also concluded that the direction of this shrinkage is from the north, northwest and from the south direction of the northern part due to the nature of the bathymetric slopes. In contrast, no shrinkage is detected from the east direction due to the same reason since the bathymetric slope is so sharp. The use of the Dead Sea water for industrial purposes by both Israel and Jordan is one of the essential factors that affect the area of the Dead Sea. The intensive human water consumption from the Jordan and Yarmouk Rivers for other usages is another main reason of this shrinkage in the area as well.

In this study, a multiple algorithm approach to the analysis of GNSS coordinate time series for detecting geohazards and anomalies is proposed. This multiple algorithm approach includes the novel use of spatial and temporal analyses. In the spatial analysis algorithm, the spatial autoregressive model was used, assuming that the GNSS coordinate time series from a network of stations are spatially dependent. Whereas in the temporal analysis algorithm, it is assumed that the GNSS coordinate time series of a single station is temporally dependent and an artificial neural network is used to extract this dependency as a nonparametric model. This multiple algorithm approach was examined using (i) the BIGF network of GNSS stations in the British Isles and (ii) the GNSS stations of the GEONET network in Japan for the Tohoku‐Oki 2011 Mw9.0 earthquake. It was demonstrated in these case studies that this multiple algorithm approach can be used to detect the effect of a geohazard such as an earthquake on the GNSS network coordinate time series and to detect regional anomalies in the GNSS coordinate time series of a network. The spatial analysis algorithm seemed to be more suitable to detect coordinate offsets in the low‐frequency component and/or variations in the long‐term trends of the GNSS coordinate time series, while it is less reliable in detecting sudden large magnitude coordinate offsets due to earthquakes, as the effects at one station propagate to nearby stations. In contrast, the temporal analysis algorithm detects coordinate offsets in the high‐frequency component which makes it effective in detecting sudden large coordinate offsets in the GNSS coordinate time series such as those due to earthquakes. Thus, it was shown the complementary of the temporal and spatial analysis algorithms and their successful application for the magnitude and frequency content of the anomalies in the two case studies.