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A combined framework of machine learning and full waveform inversion for electromagnetic sensing of infrastructure (Advert Reference: SF19/EE/MCE/WARREN)


Project Description

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 interpretation of data 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 – can 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; and FWI is incredibly computationally demanding requiring many forward simulations to converge to a solution. Recently, however, much progress has been made both on the capabilities and performance (through GPU execution) of the forward solver, as well as the use of machine learning. It has been demonstrated that deep neural networks can be trained to simulate data in the same way as a forward model, and execute using a fraction of computational resource.

The project will combine numerical modelling, using open source electromagnetic modelling software gprMax (http://www.gprmax.com), full waveform inversion, and machine learning. An inversion framework will be developed that is based on a machine learning approach, 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.

This project is supervised by Dr Craig Warren.

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]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

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. SF19/EE/MCE/WARREN) will not be considered.

Start Date: 1 March 2020 or 1 October 2020

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 holds 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.

References

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
• 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
• 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

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