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