Monitoring and predicting railway subsidence using InSAR and time series prediction techniques

Improvements in railway capabilities have resulted in heavier axle loads and higher speed operations, which increase the dynamic loads on the track. As a result, railway subsidence has become a threat to good railway performance and safe railway operation. The author of this thesis provides an approach for railway performance assessment through the monitoring and prediction of railway subsidence. The InSAR technique, which is able to monitor railway subsidence over a large area and long time period, was selected for railway subsidence monitoring. Future trends of railway subsidence should also be predicted using subsidence prediction models based on the time series deformation records obtained by InSAR. Three time series prediction models, which are the ARMA model, a neural network model and the grey model, are adopted in this thesis. Two case studies which monitor and predict the subsidence of the HS1 route were carried out to assess the performance of HS1. The case studies demonstrate that except for some areas with potential subsidence, no large scale subsidence has occurred on HS1 and the line is still stable after its 10 years' operation. In addition, the neural network model has the best performance in predicting the subsidence of HS1

Spaceborne InSAR for dam stability

PhD ThesisThis study evaluates the feasibility of the use of satellite radar for dam deformation monitoring. Spaceborne Interferometric Synthetic Aperture Radar (InSAR) has long been used to monitor geohazards, including earthquakes, landslides, and volcanos. However, few studies have recently investigated its feasibility for localised deformation monitoring such as of earth dams. Here two case studies are presented of the monitoring of dams in Iraq. Mosul dam is one of the most dangerous dams in the world. Previous studies have reported that over a million human lives would be potentially at risk should dam failure occur. Therefore, investigation of its health using precise and continuous observations is crucial. This was achieved with two independent geodetic datasets from levelling and InSAR, and the results show continuous vertical displacements on the dam crest due to the dissolution of foundations. Vertical displacement rate estimates from levelling and InSAR for the period 2003-2010 are in good agreement, with a correlation of 0.93 and an RMSE of ± 1.7 mm. For the period 2014- 2017, the correlation is 0.95 and the RMSE is ± 0.9 mm. The movement of the dam was evaluated using settlement index which is not referring to critical instability of the dam. However, the spatial and temporal displacement anomalies emphasize that a careful monitoring and remedial work should continue. The continuous displacement in the dam foundation could loosen the compaction of the embankment and result in internal erosion. In a separate study, Darbandikhan dam was monitored using a global positioning system (GPS), levelling, and Sentinel-1 data to evaluate its stability after the 2017 Mw 7.3 Sarpol-e Zahab earthquake. The large gradient of the dam’s displacements on its crest hindered the estimation of co-seismic displacements using medium-resolution SAR data. However, Sentinel-1 images were sufficient to examine the dam’s stability before and after the earthquake. The results show that the dam was stable between October 2014 and November 2017, but after the earthquake continuous subsidence on the dam crest occurred between November 2017 and March 2018. For the first time the stability of the Mosul and Darbandikhan dams has been assessed using an integration of InSAR and in-situ observations. Different types of deformations were recognized, which helped in interpreting the dam’s deformation mechanismsMinistry of Higher Education (MOHE) and the State Commission of Surveys (SCOS) in Ira