PhD defence: Generative AI meets computational material science - Characterising and reconstructing rock microstructures

The study of geomaterial microstructures is fundamental to advancing our understanding of subsurface properties that affect fluid transport, reactivity, and mechanical integrity in geological systems. This thesis combines cutting-edge deep learning techniques with statistical tools from computational material science to characterise and reconstruct complex rock microstructures.

Microstructures, specifically porosity, significantly influence the mechanical and transport properties of rocks, impacting processes ranging from geothermal energy extraction and carbon sequestration to fluid transport and mineral replacement. These processes play essential roles in diverse geoscientific and engineering applications, including modelling rock behaviour in the Earth, assessing geological hazards, and fluid-rock interactions. The primary goal of this work is to integrate deep learning, particularly generative adversarial networks (GANs), with interpretable statistical descriptors to achieve a detailed, quantifiable understanding of rock microstructures and their evolution, thereby providing enhanced tools for digital rock physics and addressing inherent limitations of common imaging techniques, specifically the trade-off between resolution and field of view.