Imaging Mineral Systems in Space and Time: New Data-Fusion Methods and Their Potential for Exploration

The global push for sustainable energy and critical mineral resources is driving remarkable advancements in geoscience, including the widespread adoption of machine learning, numerical simulations and data fusion techniques, as well as the acquisition of large geophysical and geochemical datasets worldwide. These developments, coupled with recent advances in ultra-fast computational solvers,  are unlocking the potential for large data-driven simulations and joint inversions for the complete physical state of the Earth's lithosphere that were traditionally considered impractical. These developments are blurring the traditional boundaries between geodynamics, geochemistry, and inverse geophysical theory, steering in a new generation of multi-scale and multi-observable exploration tools.

 

In this presentation, we will discuss the concept of Multi-Observable Thermochemical Tomography (MTT), a powerful technique that integrates multi-scale joint inversion of multiple datasets, machine learning, and numerical modeling to obtain probabilistic models of the lithosphere's thermochemical structure with unprecedented resolution and confidence. MTT serves as a unifying data-fusion platform, providing critical proxies that enable the application of the mineral systems approach in exploration. These two methodologies are inherently complementary, reinforcing each other to enhance predictive targeting and resource discovery. We will showcase recent advances in MTT and related techniques that illustrate the potential of integrating MTT with the concept of mineral systems to enhance predictive targeting and resource discovery during greenfield and brownfield exploration of critical minerals.