Probing Crustal Deformation and Active Tectonics of the Continents from Satellite Observations

This exciting project aims to advance our understanding of active crustal deformation and continental tectonics using high-resolution satellite radar (SAR) interferometry. We aim to quantify the distribution of crustal strain using the latest Earth observation data and machine learning techniques. This is crucial for understanding continental deformation, mountain growth, and seismic hazard.

Project Overview

We invite a motivated candidate to join this PhD project focused on understanding how our Earth deforms. You will use space-based SAR instruments (Elliott et al., 2016) and develop quantitative methodologies to assess active tectonic rates of deformation associated with major continental faulting. The ongoing collisions of Earth’s tectonic plates drive diverse active tectonic processes across large continental regions (Watson et al., 2024). Determining deformation rates and distributions across these broad collisional zones is essential to understanding the tectonics of these areas (e.g., Fang et al., 2024) and how faulting accommodates plate convergence.

Objectives

In this project, you will apply innovative methods and harness the latest techniques to measure active tectonics, faulting, continental deformation, and seismic hazard using high-resolution Earth Observation data across the continents. The project’s specific objectives are:

1. Data Generation and Analysis: Using high-performance computing, you will generate high-resolution interferometric synthetic aperture radar (InSAR) data from Sentinel-1 (Lazecky et al., 2020), focusing initially on continental zones of interest. This builds on our extensive Big Data project and is ready for exploitation to answer the latest science questions on Earth’s deformation – COMET-LiCS portal.
2. Time-Series Deformation Analysis: You will produce dense time-series deformation data (Morishita et al., 2020) across major fault zones to quantify slip rates, detect creeping zones, and assess distributed deformation. Accounting for seasonal and atmospheric noise will be necessary, and you will use the latest deep learning techniques (Rouet-Leduc et al., 2020) to extract subtle deformation signals from this noise.
3. Regional Deformation Modelling: From this data, you will model large-scale regional deformation on bounding fault structures based initially on the GEM Global Active Faults Database (Styron & Pagani, 2020), with the potential to build your own detailed observations from optical satellite imagery and digital topography. Block modelling approaches will help identify key active structures responsible for strain distribution, with more complex Finite Element Modelling applied where distributed strain suggests a need for greater detail.
4. Extension to Broader Research Questions: Depending on your interests, you will subsequently apply this approach to other research areas. This may include measuring folding and faulting in regions to quantify uplift and subsidence rates or focusing on seismic hazard assessment for major population centres (Pagani et al., 2018).

We will tailor the focus of this project to your interests, allowing flexibility in emphasis between Earth observation, deformation rates, tectonic modelling, and seismic hazard.

Training & Support

We offer a world leading collaborative research environment, and the successful candidate will be supervised by Dr John Elliott, within the Active Tectonics group of the Institute of Geophysics & Tectonics in the School of Earth & Environment at Leeds, which comprises a large number of postgraduate researchers and postdocs. The project will be co-supervised by Prof. Tim Wright (also in IGT, SEE), and Dr Richard Styron (Global Earthquake Model Foundation). The Institute of Geophysics & Tectonics also hosts the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, http://comet.nerc.ac.uk/), which provides a large group of researchers engaged in active tectonics research with whom the student can interact. The successful PhD researcher will have access to a broad spectrum of training workshops that include an extensive range from scientific computing through to managing your degree, and preparing for your viva. The student will also have the opportunity to engage with a wider range of scientists within COMET at a number of other UK institutions who have a broad interest in problems of active tectonics. Additionally opportunities may arise for fieldwork across Central Asia in support of other COMET related activities.

Student profile

We seek an ambitious and motivated student with a keen interest in remote sensing, active tectonics problems, and a strong background in a quantitative science (earth sciences, geophysics, geology, physics, natural sciences). Coding experience (e.g., Python, Matlab), GIS skills, and experience working with large datasets (such as Earth Observation imagery) are advantageous, as well as a willingness to engage with machine learning and high-performance computing.

Global view of the Alpine-Himalayan Belt with active faults marked in red as viewed in Google Earth, illustrating the wide distribution of active tectonics and faulting across this large mountainous terrain.

References

Elliott, J. R., R. J. Walters & T. J. Wright (2016).
The role of space-based observation in understanding and responding to active tectonics and earthquakes, Nature Communications, 7, doi:10.1038/ncomms13844.

Fang, J., Wright, T.J., Johnson, K.M., Ou, Q., Styron, R., Craig, T.J., Elliott, J.R., Hooper, A. and Zheng, G., (2024).
Strain partitioning in the Southeastern Tibetan Plateau from kinematic modeling of high‐resolution Sentinel‐1 InSAR and GNSS. Geophysical Research Letters, 51(19), https://doi.org/10.1029/2024GL111199.

Lazecký, M., Spaans, K., González, P.J., Maghsoudi, Y., Morishita, Y., Albino, F., Elliott, J., Greenall, N., Hatton, E., Hooper, A., Juncu, D., McDougall., A., Walters, R. J., Watson, C. S., Weiss, J. & Wright, T. J.  (2020).
LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity. Remote Sensing, 12(15), p.2430, doi:10.3390/rs12152430.

Morishita, Y., Lazecky, M., Wright, T.J., Weiss, J.R., Elliott, J.R. and Hooper, A. (2020).
LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor. Remote Sensing, 12(3), p.424, doi:10.3390/rs12030424.

Pagani, M., Garcia-Pelaez, J., Gee, R., Johnson, K., Poggi, V., Styron, R., Weatherill, G., Simionato, M., Viganò, D., Danciu, L. & Monelli, D. (2018).
Global Earthquake Model (GEM) Seismic Hazard Map (version 2018.1–December 2018). doi:10.13117/GEM-GLOBAL-SEISMIC-HAZARD-MAP-2018.1

Rouet-Leduc, B., Jolivet, R., Dalaison, M., Johnson, P. A., & Hulbert, C. (2020).
Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning. Nature Communications 12, 6480 (2021). https://doi.org/10.1038/s41467-021-26254-3

Styron, R., & Pagani, M. (2020).
The GEM global active faults database. Earthquake Spectra, 36(1_suppl), 160-180, doi:10.1177/8755293020944182

Watson, A.R., Elliott, J.R., Lazecký, M., Maghsoudi, Y., McGrath, J.D. & Walters, R.J. (2024).
An InSAR‐GNSS velocity field for Iran. Geophysical Research Letters, 51(10), https://doi.org/10.1029/2024GL108440.