Lindsay, Mark1,*, Pirot, Guillaume1, Jessell, Mark1,*, Giraud, Jeremie1, Scalzo, Richard2,*, Cripps, Edward3,*, Aitken, Alan1
1Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia, 2School of Mathematics and Statistics, The University of Sydney, Sydney, Australia, 3Department of Mathematics and Statistics, The University of Western Australia, Perth, Australia
* ARC Centre for Dare Analytics for Resources and Environment
Crustal 3D models provide an understanding of the tectonic history of a region and its mineral endowment. As mineral resources are now mostly discovered under sedimentary cover, geophysical data are necessary to guide exploration. Recent developments in modelling 3D uncertainty with optimisation techniques are combined to guide data acquisition to image mineral systems and identify prospective regions. Most mineral systems are difficult to image with individual geophysical techniques so it is important to understand which data combinations are most effective for each system component (architecture, fertility, depositional trap, geodynamic throttle, preservation). In the course of these model-driven studies, there are often competing choices to be made around which data should be collected in order to reduce geological uncertainty. The “GDOM” project – “Geophysical Data Optimisation for Modelling” – seeks to determine what and how much geophysical data is worth collecting, the best processing methods and application in the most economically efficient manner. The intention here is to guide government policy and industry data collection practices. The workflow aims to inform how geophysical datasets can be best used to constrain geoscientific concepts and models to reduce uncertainty and more reliably answer geological questions. Recent advances in 3D model analysis helps to place focus where required and be used within a value-of-information (VoI) proposition to help us decide where and what data to collect. Two important parameters used in a VoI calculation are the estimate of “gain” from data collection and the probability of that gain. “Gain” may be the increase in value through deposit discovery, or finding prospective mineralisation amongst a portfolio of prospects and results. Both gain and the its dependence on critical system parameters are also uncertain, and have a large influence on the VoI analysis and estimating the risk involved in an exploration project. The cost of data collection is likewise critical information for decision-makers, especially if hierarchical scenarios where additional data reduces the cost of drill targeting are considered. We propose a method that places these parameters into a hierarchical Bayesian framework to give us a clearer understanding on the uncertainties around VoI analyses, and helps us to determine the relative utility of collecting different geophysical data.
We acknowledge the support of the MinEx CRC and the Loop: Enabling Stochastic 3D Geological Modelling (LP170100985) consortia. The work has been supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government’s Cooperative Research Centre Programme. This is MinEx CRC Document 2020/xx.
Mark is a geoscientist and Senior Research Fellow at the University of Western Australia, specialising in integrated geoscientific and 3D modelling and understanding the value of geoscientific information. He also has research interests that include investigating complex mineral systems and their representations.