End-to-End Mineral Exploration with Artificial Intelligence and Ambient
  Noise Tomography
Jack Muir Gerrit Olivier Anthony Reid
This paper presents an innovative end-to-end workflow for mineral
exploration, integrating ambient noise tomography (ANT) and artificial
intelligence (AI) to enhance the discovery and delineation of mineral resources
essential for the global transition to a low carbon economy. We focus on copper
as a critical element, required in significant quantities for renewable energy
solutions. We show the benefits of utilising ANT, characterised by its speed,
scalability, depth penetration, resolution, and low environmental impact,
alongside artificial intelligence (AI) techniques to refine a continent-scale
prospectivity model at the deposit scale by fine-tuning our model on local
high-resolution data. We show the promise of the method by first presenting a
new data-driven AI prospectivity model for copper within Australia, which
serves as our foundation model for further fine-tuning. We then focus on the
Hillside IOCG deposit on the prospective Yorke Peninsula. We show that with
relatively few local training samples (orebody intercepts), we can fine tune
the foundation model to provide a good estimate of the Hillside orebody
outline. Our methodology demonstrates how AI can augment geophysical data
interpretation, providing a novel approach to mineral exploration with improved
decision-making capabilities for targeting mineralization, thereby addressing
the urgent need for increased mineral resource discovery.