Although a resource model is central to the mineral resource value estimation process (Glacken and Snowden, 2001), creating it is a labour intensive task and in the end it is a limited representation of the actual orebody. Creating this resource model is a labour intensive task that requires inputs from geology, mining, metallurgical and commercial disciplines. It requires thousands of samples from hundreds of drill holes to be verified, grouped in geological domain, interpolated and then valued. Even after all this effort, a model is only an estimation; therefore further significant effort has gone into quantifying uncertainty in the model and subsequently risk around any value estimates derived from the model. Further risks to model quality are introduced through poorly conditioned data or incorrect assumptions._x000D_
This paper proposes the application of machine learning to automate the resource model development. Machine learning is applied to the traditionally manual tasks of geological formation, domain identification and validation of the block model mineralogy. Through automation the resource estimation process can be accelerated, allowing more drill holes or a larger resource body to be processed in a given time frame, or allowing the process to be more agile to changes in input data and assumptions. A case study based on drill hole data from a Western Australian iron ore deposit (Government of Western Australia, Department of Mines and Petroleum, 2015) is used to demonstrate the application of machine learning in this process._x000D_
CITATION: Oliver, S and Willingham, D, 2016. Maximise Orebody Value through the Automation of Resource Model Development Using Machine Learning, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 295-302 (The Australasian Institute of Mining and Metallurgy: Melbourne).