Monitoring and Predicting Railway Subsidence Using InSAR and Time Series Prediction Techniques ZIYI YANG A thesis submitted to The University of Birmingham for the degree of DOCTOR OF PHILOSOPHY School of Civil Engineering College of Engineering and Physical Sciences The University of Birmingham, UK March 2015 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract Abstract Improvements in railway capabilities have resulted in heavier axle loads and higher speed operations. Both of these factors increase the dynamic loads on the track. As a result, railway subsidence has become a threat to good railway performance and safe railway operation. Poor infrastructure performance requires more maintenance work, and therefore the life cycle costs of the railway will increase. In order to ensure good performance and reduce life cycle costs, railway subsidence should be monitored and predicted. The author of this thesis provides an approach for railway performance assessment through the monitoring of railway subsidence and prediction of railway subsidence based on a time series of Synthetic Aperture Radar (SAR) images. The railway is a long and relatively narrow infrastructure, which subsides continuously over a long time period. As a result, larger image coverage and long time monitoring periods are two key requirements for railway subsidence monitoring. In addition, railway subsidence monitoring should also consider the repeatability, precision and efficiency of the monitoring method, as well as labour costs. The Interferometric Synthetic Aperture Radar (InSAR) technique, which is able to monitor railway subsidence over a large area and long time period, was selected for railway subsidence monitoring by the author. In order to obtain a more reliable railway subsidence measurement result, and extension of InSAR, PS-InSAR (Permanent Scatterer InSAR), was used for the research, since it is capable of supporting a time series analysis of ground deformation. In addition to railway subsidence monitoring, future trends of railway subsidence should also be predicted using subsidence prediction models. Railway deformation records obtained by PS-InSAR can be considered as a discrete time series. As a result, three time series prediction models have been investigated in this thesis for railway subsidence prediction, which are the traditional statistical ARMA model, a neural network model based on artificial intelligence and the grey model. Case studies to monitor and predict the subsidence of ground under the High Speed One (HS1) route in Britain were carried out to assess the performance of the PS- InSAR method and the time series prediction models. A deformation profile of HS1 ii Abstract has been developed for the case study, based on deformation monitoring results obtained by PS-InSAR. The deformation profile demonstrates that no large scale subsidence has occurred on HS1 and the line is still stable after having been in operation for 10 years. However, some areas of HS1 show potential subsidence. For instance, the areas over the North Downs Tunnel and the line between Hockers Lane Overbridge and Water Lane Underbridge have subsided by up to 4.0 mm per year and 2.9 mm per year respectively. The stability of the ground under Ashford International Station was also assessed in this thesis. Based on the deformation monitoring records of HS1, railway subsidence prediction was carried out by the application of three main time series prediction models. The result of the railway prediction demonstrates that the neural network model has the best performance in predicting the subsidence of HS1. iii Acknowledgements Acknowledgements Foremost, I would like to express my gratitude to my supervisors, Prof. Felix Schmid and Prof. Clive Roberts for their patience, motivation, knowledge, support and encouragement in my PhD study. Without their supervision, I could not overcome difficulties in my research and thesis writing up. Besides my supervisors, I am grateful to David Oldroyd for his support in my research, particularly in the information gathering for the case study. I also appreciate the help of Katherine Slater. I would like to thank Prof. Andy Hooper from Leeds University for providing StaMPS software for my data processing, Delft University of Technology for providing precise radar orbit data, USGS for providing SRTM data and ESA for providing Envisat ASAR data. Moreover, I appreciate the great support and encouragement from my husband Gang Hu and my parents during my studies at the University of Birmingham. In addition, I am grateful to all the people who helped me and encouraged me during my PhD at the University of Birmingham. iv Table of Contents Table of Contents Table of Contents ......................................................................................................... v List of Figures .............................................................................................................. ix List of Tables ............................................................................................................... xi List of Abbreviations ................................................................................................. xii Chapter 1 Introduction .............................................................................................. 1 1.1 Background .................................................................................................... 1 1.2 Objectives ...................................................................................................... 3 1.3 Thesis Structure ............................................................................................. 4 Chapter 2 Review of Railway Subsidence Monitoring ........................................... 7 2.1 Introduction .................................................................................................... 7 2.2 Nature of Railway Infrastructure and Subsidence ......................................... 7 2.3 Approaches for Railway Subsidence Monitoring .......................................... 9 2.4 Development of InSAR ................................................................................ 10 2.5 Acquisition of SAR Image ........................................................................... 12 2.5.1 Designation of Radar Band ...................................................................... 12 2.5.2 Image Acquisition Modes ........................................................................ 14 2.5.3 Satellites for Image Acquisition ............................................................... 17 2.6 Applications of InSAR ................................................................................. 18 2.6.1 Topography Mapping ............................................................................... 18 2.6.2 DEM Generation ...................................................................................... 19 2.6.3 Ocean Currents Mapping ......................................................................... 20 2.6.4 Landscape Deformation Measurement .................................................... 20 2.7 Ground Deformation Monitoring by D-InSAR ........................................... 21 2.7.1 Classifications of D-InSAR ..................................................................... 22 2.7.2 Limitations of D-InSAR .......................................................................... 23 2.8 Ground Deformation Monitoring by PS-InSAR .......................................... 26 2.9 PS-InSAR in Railway Subsidence Monitoring ............................................ 27 2.9.1 SNCF High Speed Rail Network ............................................................. 27 2.9.2 Jubilee Extension Line in London ........................................................... 27 2.9.3 Jinshan Railway in China ......................................................................... 28 v Table of Contents 2.10 Summary ...................................................................................................... 29 Chapter 3 Review of Railway Subsidence Prediction ........................................... 31 3.1 Introduction .................................................................................................. 31 3.2 Time Series Prediction Model ..................................................................... 31 3.2.1 Statistical Models ..................................................................................... 32 3.2.2 Artificial Neural Network ........................................................................ 37 3.2.3 Grey Model .............................................................................................. 38 3.2.4 Comparisons between Time Series Prediction Models ........................... 39 3.3 Time Series Models in Subsidence Prediction ............................................. 41 3.3.1 ARMA Models ........................................................................................