Integrating Prospectivity

Mineral Systems, Machine Learning and Sterilisation/Enhancement from Previous Exploration

The presentation outlines innovative approaches to regional prospectivity modelling including integrating knowledge-driven and data-driven methodologies in a way the best aspects of each can be captured. The presentation details SRK Exploration’s Bayesian framework methodology to combine the two approaches. This framework is unique in that it also integrates our understanding of terrane endowment and sterilisation, or prospectivity enhancement, from prior exploration activities. Thus, explorers can be directed not just to areas of raw geological potential but also away from areas fully or partially sterilised by previous exploration activity, or conversely towards areas enhanced by previous exploration results.

Using West Australian gold as an example the presentation covers how a regional prospectivity model can be developed that integrates the best of our exploration knowledge and data. Rather than a relativistic output, the model uses Bayesian statistics to estimate the probability of discovering deposits within a given timeframe, an output we term ‘Absolute Prospectivity’. The output is a probability distribution of discovery of different size class deposits in each cell that can then be combined in areas of interest and used for exploration decision-making. As the output is not only the probability for any discovery but also the potential deposit size, it can also be used to ensure regional greenfield exploration portfolios are adequate for exploration goals in terms of discovery rate and size.

The presentation explains the full prospectivity workflow, which includes the construction of mineral systems, machine learning, terrane endowment and exploration intensity models. It shows how these disparate elements can be unified algorithmically within a flexible Bayesian framework. In doing so it demonstrates how both geological understanding and data science can be complimentary and how the pitfalls of both approaches can be avoided.

Overall, the generative prospectivity workflow offers a comprehensive and quantitative approach to prospectivity, integrating advanced modelling techniques to optimize exploration strategies and achieve discovery goals. This approach provides a competitive advantage by producing repeatable and defendable targets, quantifying exploration risks, and improving project quality and efficiency.

Niall has 20 years’ experience in mineral exploration, from establishing field campaigns in remote locations, to valuation of exploration properties and technical reporting. His experience spans projects ranging from reconnaissance through to bankable feasibility, though with a particular focus on early-stage exploration. Niall conducts evaluations and valuations of exploration properties from greenfield to brownfield, is CP/QP for technical reporting of early-stage exploration and specialises in the construction of prospectivity models and the use of statistical methods to value & manage exploration projects.  

Niall is currently a Principal Exploration Geologist at SRK Exploration, and has held previous positions with Couloir Capital Ltd, Block Energy plc, Goldcrest Resources plc, Alecto Minerals plc and Rio Tinto Mining & Exploration. He is a Chartered Geologist and holds a BSc in Exploration and Resource Geology from Cardiff University, a MSc in Mining Geology from Camborne School of Mines and a MSc in Metals and Energy Finance from Imperial College London.

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