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A combined framework of machine learning and full waveform inversion for electromagnetic sensing of infrastructure (Advert Reference: SF18/MCE/WARREN)

   Faculty of Engineering and Environment

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  Dr C Warren  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Electromagnetic sensing systems, such as Ground Penetrating Radar (GPR), are critical tools to allow us to evaluate the condition of surface and sub-surface infrastructure. They are used for a wide variety of infrastructure and geophysical applications, including: assessment of bridges, roads, and railways; location of buried utilities; ice profiling and glaciology; and groundwater and soil condition monitoring.

The final interpretation of images from such microwave sensors still relies on human experience, which often makes it subjective and error prone. Full waveform inversion (FWI) – which can produce high-resolution electromagnetic models – could greatly assist with solving this problem, but has yet to see widespread adoption. Key reasons for this are that: FWI requires an accurate forward solver that is able to model the complex environments that microwave sensors are used in; FWI is incredibly computationally demanding requiring many forward simulations and iterations to converge to a solution. Recently, however, much progress has been made both on the modelling capabilities and performance (through GPU execution) of the forward solver. In addition, it has been shown that there could be an important function for machine learning in the FWI workflow.

The project will combine numerical modelling, using open source electromagnetic modelling software gprMax (, full waveform inversion, and machine learning. A new inversion framework will be developed, and will be applied to one or more of the aforementioned application areas. It is a highly multi-disciplinary area of research, involving civil engineering, electrical engineering, geophysics, and high-performance computing.

Please note eligibility requirement:

* Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours])
in geophysics or engineering; or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS score, if required

This project is well suited to motivated and hard-working candidates with a keen interest in applied software engineering and high-performance computing. The applicant should have excellent communication skills including proven ability to write in English.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF18/MCE/WARREN) will not be considered.

Start Date: 1 March 2019 or 1 June 2019 or 1 October 2019

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University hold an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

This is an unfunded research project


• Giannakis, I., Giannopoulos, A., & Warren, C., 2018, “A Machine Learning Based Fast Forward Solver for Ground Penetrating Radar with Application to Full Waveform Inversion”, IEEE Transactions on Geoscience & Remote Sensing, (In review)
• Warren, C., Giannopoulos, A., Gray, A., Giannakis, I., Patterson, A., Wetter, L., & Hamrah, A., 2018, “A CUDA-based GPU engine for gprMax: open source FDTD electromagnetic simulation software”, Computer Physics Communications, (In review)
• Warren, C., Giannopoulos, A., & Giannakis I., 2016, “gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar”, Computer Physics Communications, 209, 163-170, 10.1016/j.cpc.2016.08.020
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