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  An integrated model for monitoring Qinghai-Tibet railway deformation based on DInSAR technology and GPS observations

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Prof. Lin Hui

This project aims to enhance the capability of deformation monitoring for Qinghai-Tibet railway combining Differential Synthetic-Aperture Radar Interferometry (DInSAR) and Global Positioning System (GPS) observations.

Large scale man-made linear features, including railways, bridges, highways, dams, metro-lines and pipes, play an essential role in physical distribution and energy transportation in modern society. Qinghai-Tibet railway, as a typical large scale man-made linear feature, passes through permafrost regions with its length of more than 550 km. Surface deformations caused by seasonally freezing bulge and thawing subsidence have a great influence on the railway stability, resulting in hazards to engineering construction and maintenance in permafrost regions. Hence, surface deformation along the railway shall be carefully monitored to ensure safety and effective operations.

PS-InSAR derived Surface motion along the embankment of Qinghai-Tibet Railway using C- and L- band SAR data

DInSAR and GPS observations are both capable of providing ground deformation measurements up to millimetric accuracy. However, such technologies confront challenges in the Qinghai-Tibet railway monitoring. The difficulties are two-fold, firstly, point-based GPS observation is not cost-effective to monitor the 550km-long-railway with reasonably high density. Secondly, although advanced DInSAR techniques can provide promising performance for area-based ground deformation monitoring, its capability in linear-based features is still questionable.

The corner reflector installed in the study site

In this project, we have developed an integrated model which combines the advantages of DInSAR and GPS techniques in application like Qinghai-Tibet railway deformation monitoring. The conventional DInSAR method was enhanced to analyze linear geometric features. On the other hand, selected GPS-derived deformations are incorporated into the model to enhance the monitoring accuracy. With the aid of our developed model, an early warning on structural damages can be issued to avoid/mitigate severe consequences.

A section of the railway near Beilu River, Qinghai was selected as our study site. Results of this study can be generalized and applied to the monitoring of other similar large-scale man-made linear features monitoring, for example, the high speed railway and large-scale bridges in Hong Kong.

Research team of this project won Shunji Murai Award – the Best Paper Award of the 2011 annual conference of Asian Association on Remote Sensing (AARS 2011).

Prof Lin Hui
Institute of Space and
Earth Information Science
The Chinese University of Hong Kong