Probing Volcanic Systems with Machine Learning for Quantitative Petrography

This project centres on the development of machine learning techniques for the quantification of igneous rock properties from optical and electron microscopy. Following on from our recent successes in conversion of SEM maps into textural properties and quantification of a crystallisation timescale for the gigantic Laki eruption in Iceland (Neave et al., 2017), we plan to further explore the possibilities for machine learning in image analysis of igneous rocks (Andrew, 2018 - one of our partners from Zeiss).

We have also recently demonstrated the power of Electron Backscatter Diffraction (EBSD) analysis of volcanic crystals (Wieser et al., 2020) and aim to realise the potential of this novel approach. Partners in Zeiss are also exploring merging 3D and 2D imaging in geological applications, and this may also be a focus of study.

The student will acquire and process co-registered optical and electron microscope images of volcanic rocks from Iceland. It may also be desirable for the student to carry out field sampling in Iceland to obtain suitable samples for image analysis (e.g. mushy nodules with glassy ground mass). They will then develop image processing techniques, including machine learning, to automate phase identification and crystal segmentation.

They will then develop further processing steps to extract useful textural information from the segmented images: crystal size and shape distributions, clustering analysis, quantification of contact angles, characterisation of internal deformation. These properties will then be compared with parameterisations of natural and experimental data to extract information about the physical processes involved (e.g. timescales magma rise under active volcanoes).