Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling

Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits. The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes. In particular, knowledge-driven approaches (fuzzy logic) use a conceptual mineral systems model from which data proxies represent the critical components. These typically vary based on the scale of study and the type of mineral system being predicted. Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model. In the case study presented, the prospectivity for remobilised nickel sulphide (NiS) in the west Kimberley, Western Australia, is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios. Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered. The first technique grades the strength of structural interpretations on an individual line-segment basis. Gradings are obtained from an objective measure of feature evidence, which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure. Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence. This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty. The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems. In this approach, multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition. With a suite of prospectivity maps, the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite. This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model, robust modelling of prospectivity and significantly reducing the exploration search space for Ni. (C) 2019, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.