Creation of real-time gravity maps for engineering targets using advanced data processing

IIn 1500 Leonardo Da Vinci stated that we know more about the stars above us than the ground beneath our feet. That is as true today as it was then. We don’t know where our shallow buried assets (first 10 metres below the ground surface) are located, yet a complete picture is essential for civil engineering and construction projects to reduce the risk from buried assets. Existing active geophysical methods are limited by their resolution and depth penetration. To overcome this, passive geophysics (gravity, magnetometry) is used, but is again limited by the size of the feature which can be detected. Over the past 10 years, the University of Birmingham has conducted extensive research into quantum technology gravity and gravity gradient sensors and developed a cold-atom based quantum technology gravity gradiometer suitable for field measurements. However, for the new sensors to be extensively utilised in practice requires not only technological developments, but also adapted data processing, survey practices and quality control and assurance processes.

One of the challenges of measuring gravity is that this obtains an average measurement. Several data processing and inversion techniques exists to interpret the measurements. The focus of this project will be to investigate data-driven approaches that facilitate the quantum sensor operation on mobile platforms (e.g. towards use on ships, drones, and unstaffed underwater vehicles). The research will transform the operational capability of gravity surveying through efficient data rejection and enabling novel gravity survey methodologies which can be dynamically targeted, facilitating drastic reductions in information feedback to users. While this is of significant commercial interest, it is also critical for enabling gravity surveys in the defence sector where access to sites is often hazardous and time limited. Realising these advances will relax operational constraints allowing defence end users to investigate applications such as the detection of hidden voids, tunnels, and bunkers. 

This exciting and interdisciplinary project, funded by Dstl, where the successful candidate will work with people within the Schools of both Engineering and Physics at the University of Birmingham. There is also the potential of a secondment to Dstl to work with their Quantum Team. The successful applicant will be embedded within the National Buried Infrastructure Facility (www.birmingham.ac.uk/nbif). An interest or some experience in advanced data processing methods, e.g. Artificial Intelligence, Machine Learning or Neural Networks would be beneficial. The research will involve some large-scale laboratory trials and where appropriate field trials to obtain data for processing analysis to provide feedback for the sensor development. 

Applicants should have a good primary degree (First- or Second-class Honours) or MSc in engineering, geophysics, computer science, mathematics or physics with good data processing and numerical modelling skills. The successful candidate should be highly motivated, have good communication skills and must be prepared to work within a multidisciplinary team and with other PhD students. The successful candidate will also be integrated in the Dstl PhD training cohort with funding available to attend national and international conferences. 

For informal enquiries, please contact Prof Nicole Metje ([Email Address Removed]).