This project aims to develop new geophysical data processing techniques for imaging groundwater changes within unstable slopes (e.g. natural landslides and vulnerable earthworks).
Conventional monitoring methods involve examining the surface (either by people on the ground or from aerial photos) and using point sensors, like moisture content and tilt meters, which only give information in their immediate vicinity. But geophysical methods can ‘see inside’ the vulnerable slope, enabling volumetric tracking of moisture content changes and so identifying problems at a much earlier stage.
The technique used in this project is Electrical Resistivity Tomography (ERT), which is sensitive to groundwater changes since the presence of water strongly affects the electrical resistivity of the ground. By examining differences between subsequent images, changes in groundwater can be detected. But conventional ERT imaging (“inversion”) techniques rely on methods that produce smoothed images, which are not well suited to capturing the details of groundwater movements in complex, heterogeneous environments.
We therefore propose to apply variational methods to the problem of inverting ERT data, which would allow for solutions with discontinuities or sharp features, and to apply level set methods to track interfaces from time lapse data. These innovations will enable more accurate image reconstruction and consequently better estimates of hydrological parameters to inform slope-stability and groundwater models.
The student will benefit from a range of formal taught training courses by UoN and BGS. Also they will be given training in analysis and modelling of inverse problems, with a focus on geoelectrical inversion. This will include variational formulations of inverse problems, discretization, and fast solutions. They will also be given training in processing, modelling and manipulation of geophysical data sets.
There will be the opportunity to collaborate with BGS’s industrial partners to ensure that methods developed are tailored to the requirements of potential end-users and beneficiaries.
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Computer Science, Physics, Engineering, Mathematics or Natural Sciences.