Although we have significantly improved our ability to map and monitor overground objects, our ability to detect and locate buried objects has not improved much in the last 20 years. Street and roadworks in general, and their associated utility strikes in particular, remain to be one of the most notorious challenges to overcome in modern-day business and societal lives. Not knowing exactly the location of our buried infrastructure significantly hampers the use of underground space and impacts any construction project. Annually, £1.2bn are spent to cover costs associated with utility strikes. Recent advancements in quantum technology (QT) gravity sensors have enabled a better means to see through the ground. However, data interpretation remains a challenge as different technologies are affected by the ground in different ways. Data fusion using advanced processing can address this gap and provide the required accuracy to improve the health and safety of breaking ground thereby reducing additional costs due to project overruns.
This project attempts to address these gaps by exploring and developing new and advanced data processing techniques using an integration of classical numerical solutions and machine-learning. Furthermore, this research attempts to look beyond this by developing efficient new procedures that can assist in the integration of QT sensor gravity data into a geospatial mapping environment. In particular, this research focuses on developing procedures that allows better integration of underground survey into a geospatial platform at Ordnance Survey with a view to updating utility records.
Furthermore, this project will examine how efficiently data from other geophysical surveying technologies e.g. magnetometer, GPR can be combined in this underground mapping and data inferencing. The project will build on work started in the InnovateUK-funded QT-MIBA project. This exciting and interdisciplinary project offers collaboration with industry in an interactive and multi-disciplinary team. It has access to a state-of-the-art large-scale testing laboratory (National Buried Infrastructure Facility). The research will transform the current workflow of QT sensor data inference and integration by developing novel analysis procedures.
The student will carry out a state-of-the-art literature review of current data processing techniques (including those based on machine learning and AI) to infer gravity modelling to identify gaps in this area, leading to identification of suitable analysis approaches. To better understand the data, the student will conduct a number of geophysical surveys using a range of sensing technologies. This survey data will be used to test the data processing approaches against laboratory testing or ground truth where available. Emphasis will be on assessing the accuracy of the geospatial data and the best data formats for integration into geospatial software. Additionally, requirements & opportunities for updating utility records will be explored. There will be opportunities to receive training in the following areas: use of geophysical sensors (UoB), geospatial software, including a secondment of up to 3 months (OS), numerical modelling including AI and ML (UoB), critical writing, presentations and EDI in research.
Applicants should have a good primary degree (First- or Second-class Honours) or MSc in an engineering, physics, geophysics or mathematics discipline 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. Some experience working in the field or the laboratory would be advantageous. To apply or to enquire further information and informal enquiries, contact Dr Asaad Faramarzi (A.Faramarzi@bham.ac.uk) and Jeremy Morley (Jeremy.Morley@os.uk). Send a copy of your CV including any publications.