Improving the accuracy of displacement measurements and earthquake hazard maps from radar interferometry

This exciting project aims to improve the accuracy of deformation measurements made from Space and apply this to improve our understanding of where earthquakes will occur. Measurements of displacement from multiple other causes will also be improved.

Radar Interferometry (InSAR) is a technique that provides measurements of surface displacement from Space, potentially with millimetric accuracy. These measurements are used in the natural hazards community for earthquake analysis and monitoring of volcanoes and landslides, as well as for monitoring of anthropogenic activities such as oil and gas extraction and drawdown of underground water storage.

An ongoing recent effort of the COMET Centre of Excellence, led from Leeds, is to use long time series of regular radar acquisitions to measure tectonic strain rates with sufficient accuracy to improve earthquake hazard maps (Elliott et al, 2016 and Figure 1). Since 1900, between 1.4 and 1.7 million people have been killed by earthquakes in continental interiors. In contrast to the narrow boundaries on the edges of oceanic plates, continental seismic belts span broad regions and earthquakes often occur on unidentified faults. Although most earthquakes appear to have no recognisable short-term precursor, all are preceded by a long, slow build-up of strain around the causative faults and maps of tectonic strain can therefore inform models of seismic hazard.

Figure 1. East-west and vertical velocities for Turkey derived from 4 years of Sentinel-1 SAR images (Courtesy Jonathan Weiss). Maps like these will feed into estimates of seismic hazard, but accuracy can be further improved.

In order to achieve millimetric accuracy there are several noise terms that need to be reduced. The most significant of these is the variability in signal propagation through the atmosphere, and much effort has been put into estimating and reducing this noise source. Although the effects cannot be completely ameliorated, the impact can be further reduced by building long time series of images. However, a further noise source, which has mostly been ignored in the past, comes from changes in the scattering properties of the ground due to changes in moisture content and vegetation (De Zan et al, 2015). This effect was previously assumed to average out over time, but it has been shown recently that this noise source can accumulate systematically when long times series are built from interferograms of shorter length, which is typical of the so-called “small baseline” approach usually adopted outside of urban areas (Hooper et al, 2012).

The soil moisture/vegetation effect can be isolated by forming and comparing deformation maps from images acquired on three different dates: the deformation map formed the first and third dates is compared to the sum of the deformation maps formed from the first and second dates and the second and third dates. The difference between the two is termed the “closure phase” (Figure 2) and will be non-zero if there is a systematic effect of soil moisture and/or vegetation change.


Figure 2. Example of closure phase for Kumamoto area, Japan (De Zan et al, 2018). a) Optical image from Google Maps and b) the closure phase, due primarily to soil moisture changes, which partially correlates with the degree and type of vegetation.

In this project the student will develop ways to estimate and reduce the systematic effect of moisture and vegetation in time series of images, and apply these methods to improve maps of tectonic strain rates, leading to better estimation of global earthquake hazard. The methods developed in this project will also be used to improve times series of deformation related to anthropogenic causes.



In this project the student will work with leading scientists at Leeds (Andy Hooper and Tim Wright) and SatSense Ltd (Karsten Spaans) to achieve the following objectives:

1) Assess recent approaches developed to estimate the systematic effect of soil moisture and vegetation change (e.g., De Zan et al., 2018 and Wang et al., 2018), based on measurements of closure phase and modelling of soil moisture distribution.

2) Develop an approach that is optimised for estimating deformation that accumulates slowly over long time scales (many months to years).

3) Apply the new method to a selected region of the Alpine-Himalayan belt, e.g. Anatolia, and estimate the strain rate and seismic hazard for this region.

4) Adapt and apply the new method to a case study of anthropogenic deformation supplied by SatSense Ltd.


Potential for high impact outcome

Deadly earthquakes continue to occur on faults that have not been identified. As well as improving seismic hazard models generally, the improved accuracy of measurements from this project could lead to the identification of previously unrecognised areas of high seismic hazard.



You will work under the supervision of Prof. Andy Hooper and Prof. Tim Wright within the School of Earth and Environmental Sciences. You will also become a member of the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET), which brings together experts from the Universities of Oxford, Cambridge, Leeds, Bristol, Reading, Liverpool and Newcastle and University College London ( Through COMET, you will have access to a range of training opportunities related to deformation monitoring and modelling, in addition to a broad spectrum of training workshops provided by the Faculty, from training in numerical modelling through to managing your degree and preparing for your viva (


Student profile

The student should have a strong background in a quantitative science (e.g. computing, maths, physics, engineering, earth sciences).



De Zan, F., Zonno, M. and López-Dekker, P., 2015. Phase inconsistencies and multiple scattering in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 53(12), pp.6608-6616.

De Zan, F. and Gomba, G., 2018. Vegetation and soil moisture inversion from SAR closure phases: First experiments and results. Remote sensing of environment, 217, pp.562-572.

Elliott, J.R., Walters, R.J. and Wright, T.J., 2016. The role of space-based observation in understanding and responding to active tectonics and earthquakes. Nature communications, 7, p.13844.

Hooper, A., Bekaert, D., Spaans, K. and Arıkan, M., 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics, 514, pp.1-13.

Wang, C., Zhang, Z., Zhang, H., Zhang, B., Tang, Y. and Wu, Q., 2018. Active Layer Thickness Retrieval of Qinghai–Tibet Permafrost Using the TerraSAR-X InSAR Technique. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11), pp.4403-4413.