Development of machine learning techniques for characterising changes in time-lapse resistivity monitoring

Ward, Wil O. C. (2018) Development of machine learning techniques for characterising changes in time-lapse resistivity monitoring. PhD thesis, University of Nottingham.

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Abstract

Electrical resistivity tomography (ERT) is a geophysical technique for modelling the properties of the shallow subsurface. The technique provides a powerful tool for a volumetric representation of the spatial properties and spatio-temporal systems below the ground by indirectly measuring electrical properties. ERT has wide-reaching applications for imaging and monitoring in fields such as mineral exploration, infrastructure, and groundwater modelling. Developing tools that can perform predictions and analysis on the resistivity models with limited intervention will allow for ERT systems to be deployed remotely so that they might serve as an alert system, for example, in areas at risk of landslides, or groundwater contamination. However, the nature of indirect observation in ERT imaging means that there is a high degree of uncertainty in the resolved models, resulting from systematic artefacts that occur in inversion processes and from the fact that the underlying structures and processes cannot be directly observed.

This thesis presents a number of developments in automating the analysis and prediction of directly and indirectly observed uncertain systems, both static and dynamic. Drawing from principles in both fuzzy logic and probability, particularly Bayesian statistics, the different representations of uncertainty are exploited and utilised to make meaningful estimates of properties and parameters in noisy systems. The key contributions of the research presented include the unique combination of fuzzy inference systems in a recursive Bayesian estimator to resolve systems under the influence of multiple uncertain dynamic processes. Furthermore, frameworks for robustly isolating features with quantified certainty and for automatically tracking tracer moments in hydrodynamic systems are proposed and applied to a number of real-world case studies.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Bai, Li
Nilsson, Henrik
Keywords: machine learning; fuzzy logic; recursive Bayesian estimation; electrical resistivity tomography; near-surface geophysics
Subjects: Q Science > QA Mathematics > QA 75 Electronic computers. Computer science
Faculties/Schools: UK Campuses > Faculty of Science > School of Computer Science
Item ID: 53373
Depositing User: Ward, Wil
Date Deposited: 19 Dec 2018 12:47
Last Modified: 19 Dec 2018 12:47
URI: http://eprints.nottingham.ac.uk/id/eprint/53373

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