Single-sample unmixing with machine learning: a rock magnetic frontier

Magnetic rock-forming minerals can record important information about Earths magnetic field and climatic changes. In rock magnetism, we seek to quantify magnetic property variations in geological materials. Existing quantification methods are limited and provide bulk characterisation of all magnetic particles in a material rather than diagnostic information concerning individual mineral components. This Project aims to develop a machine-learning framework to unmix and quantify each magnetic mineral component in single natural samples, and will unlock a new quantitative era in rock magnetism. It is expected to have impact beyond Earth science by enabling magnetic characterisation in physics, materials science, and industry.