Inversion and Uncertainty Quantification
Inversion and uncertainty quantificationWhat I offer
Probabilistic inversion frameworks that transform noisy geophysical and geological observations into subsurface property models with rigorously quantified uncertainty. This spans gravity, magnetics, seismic, and thermal data, individually or jointly inverted within a Bayesian framework.
Who this is for
Exploration and resource companies that need to quantify risk in subsurface models, consultancies delivering geophysical interpretation, and research groups developing next-generation inversion methodologies.
Tools and methods
- Bayesian inversion with Markov-chain Monte Carlo sampling
- Deterministic gradient-based inversion with regularisation
- Joint and constrained inversion of multi-physics datasets
- Ensemble methods for uncertainty propagation through forward models
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.