How ambient noise tomography and AI can solve mineral exploration’s scale reduction problem

Fleet Space Technologies, Adelaide, Australia

All geophysical and geological data collection and analysis can be viewed as driving toward a single end: scale reduction. This is the process in mineral exploration by which explorers can progressively reduce their area of focus from the region, district and tenement-scale towards the goal of identifying an economic deposit.

In this workshop we will investigate the application of two technologies Ambient Noise Tomography (ANT) and Artificial intelligence (AI) towards this end, which are aimed at speeding up and increasing confidence in the scale reduction process:

1. ANT is currently not widely considered part of the exploration toolkit, despite having two qualities that make it uniquely applicable to the problem of scale reduction: it maps the subsurface in 3D and it is scale invariant (can be applied from core sample to continents). Geologists are generally not accustomed to thinking about the subsurface in terms of seismic velocity, especially in comparison to other physical properties such as magnetism or density, which are embodied in potential field data. Nevertheless, seismic velocity is a consequence of both mineralogy and structure, which are the very rock properties that geologists are often most concerned about.
2. AI is a transformative force across diverse industries. In the realm of mineral exploration, AI’s ability to process and interpret multi-dimensional data sets opens new avenues for identifying mineral deposits with enhanced accuracy. Through its adaptability, continuous learning, and superhuman abilities in pattern recognition, AI has the potential to redefine key aspects of exploration processes.

We will describe an end-to-end approach that demonstrates how ANT and other data sets can be integrated to refine our approach to mineral prospectivity analysis. This integration allows for the fine-tuning of AI models to specific geological contexts, enhancing their predictive power for mineral deposit localization, resource estimation and future exploration planning.

The workshop will feature talks by Fleet Space geoscientists and partner organizations that will cover the scale-reduction process, the ANT method and its integration with other data sets, and the frontiers of AI that have the potential to provide an informed exploration strategy, crucial for meeting the increasing demand for minerals essential for the global transition to a sustainable energy future. The workshop will also feature a group discussion on the future roles of these and other technologies for improving area selection and scale reduction across the exploration process.