Development of Advanced AI-assisted Computational Routines to Infer Quantum Technology Sensors Data

"The data obtained from (QT) gravity sensors have very little value without appropriate and robust post-processing analysis and algorithms to interpret the raw data. Usually the data from sensors are converted into meaningful information using an inversion process. The existing inversion algorithms have several limitations including the lack of accuracy and efficiency especially when dealing with large and complex data patterns. In particular, the existing inversion approaches, can only be used for certain regular density anomalies - i.e. complex geometries are usually overlooked in the inversion process or it becomes computationally expensive hence not considered. Additionally, even for simple density anomalies, current inversion procedures are computationally inefficient and in majority of scenarios can take days before they can return useful results. This PhD project intends to overcome the above issues by developing a transformative approach that uses novel, and advanced computational modelling tools supported by artificial intelligence (AI) to interpret (QT) gravity sensor data. In particular, this PhD will focus on developing efficient inversion procedures by integrating mathematically conventional inversion tools with machine learning (ML) algorithms to radically reduce the inversion process. Within ML approaches, a special attention will be given to evolutionary computing techniques (namely genetic algorithm, GA) due to their robustness and efficiency to find global optimum of complex problems when suitable numerical modelling strategies are used. The use of GA has shown promising initial results in the iFEEL partnership project using gravity data and, in the GUIDE, (EP/P010415/1) project using vibrational data for the detection of buried pipes. Furthermore, the use of advanced modelling and inversion procedures will be investigated using supervised deep learning (a sub-category of ML techniques). This approach will be crucial in improving the computational efficiency of the inversion process as it will benefit (where possible) from a previously trained model to reduce the computational time.

The main application of the proposed processing algorithms will be to identify the location of underground objects in urban areas (e.g. sewers and tunnels) or density contrast under surface transportation infrastructure (e.g. to identify the presence of wash-out and sinkhole under roads and railway tracks).

The nominated candidate (Mr Winner Oni) has a background in mathematics, particularly demonstrating a strong performance in numerical modelling techniques. He would be ideally suited to drive the development of suitable inversion processes forwards. as he can use his knowledge and background in mathematics and statistics to develop novel data inferring tools for the QT sensor data.

Whilst scattered (and very few) works exist in the field of ML-based inversion of gravity data, none of them have offered a unified framework or a general guidance on how ML can be used to tackle the problem. They rather have focused on a few specific problems without offering any useful general instructions and therefore, their works have not been of much use to others.

Specifically, we envisage that this PhD facilitates, for the first time, development of a suitable ML-based tool and necessary associated guidance for geophysicists and relevant industries to infer QT gravity data. In addition to the proposed tool, a 'how to' package on the use of AI-based analysis for QT sensor data will be produced that can be used as a benchmark for future adaptation of this approach and ensuring a continuous and sustainable process for future of this research. We plan to demonstrate the usefulness of such tool via various means including through our very close relationship with RSK, as well as dissemination of the results in both public and focused events. The use of AI in data modelling and data processing can offer significant advances to alm