Deep Time Data Mining
Deep time data miningWhat I offer
Extraction and integration of deep-time geological datasets from plate tectonic reconstructions, combined with machine-learning workflows to identify prospective regions for mineral exploration. This includes spatiotemporal feature engineering from palaeogeographic data and predictive modelling of mineralisation potential.
Who this is for
Mining and exploration companies seeking data-driven approaches to greenfield targeting, research groups working on mineral systems, and government geological surveys building prospectivity frameworks.
Tools and methods
- GPlately and pyGPlates for plate reconstruction interrogation
- Spatiotemporal feature extraction pipelines
- Gradient-boosted and deep-learning classifiers for prospectivity mapping
- Integration with geochemical and geophysical datasets
Get in touch
Interested in working together? Get in touch to discuss your project.
I am an ARC Industry Research Fellow in the School of Geography, Earth and Atmospheric Sciences at The University of Melbourne. I am an expert in fusing Earth evolution models with data to understand how groundwater moves critical minerals through the landscape. Related research interests include the cycling of volatiles within the Earth, probabilistic thermal models of the lithosphere to unravel past tectonic and climatic events, and understanding the how enigmatic volcanoes form.
I am a vocal advocate for the integral role of geoscience in responding to challenges we face in transitioning to the carbon-neutral economy. As an expert in my field, I have been interviewed in national and international print media, TV, and radio on a wide variety of subjects including earthquakes, volcanoes, groundwater, and critical minerals.