Rapid, efficient, and accurate prediction of mineral occurrence that takes uncertainty into 20 account is essential to optimise defining exploration targets. Traditional approaches to mineral 21 potential mapping often fail to fully appreciate spatial uncertainties of input predictors and their 22 spatial cross-correlation. In this study a stochastic technique based on multivariate 23 geostatistical simulations and ensemble tree-based learners is introduced for predicting and 24 uncertainty quantification of mineral exploration targets. The technique is tested on a synthetic 25 case inspired by the characteristics of a hydrothermal mineral system model and a real-world 26 dataset from the Yilgarn Craton in Western Australia. Results from the two cases proved the 27 superior performance and robustness of the proposed stochastic technique, especially when 28 dealing with high dimensional and large data sets. <b>Citation:</b> Talebi, H., Mueller, U., Peeters, L.J.M. et al. Stochastic Modelling of Mineral Exploration Targets. <i>Math Geosci </i>54, 593–621 (2022). https://doi.org/10.1007/s11004-021-09989-z

A scientific workshop for NESP Project D1 ¿Developing a toolbox of predictive models for the monitoring and management of KEFs and CMRs in the North and North-west regions¿ was held at Geoscience Australia 9-10 September 2015. The objectives of the workshop were to discuss future research priorities for the North and North-West regions and to define current knowledge gaps by consolidating existing datasets from AIMS, GA and UWA. Several robust datasets for the North and North-West region were identified which may be used to validate, refine, or extend existing models, particularly in the Oceanic Shoals CMR and along the North-west coastline, including the Kimberley CMR. There are still large regions for which very little scientific information exists, notably the Argo Rowley Terrace CMR and other deep-sea areas. However, when balanced against stakeholder interests and marine management priorities, data-poor CMRs closer to the coast such as the Kimberley and 80 Mile Beach CMRs are the most likely candidates for future research. <b>Citation:</b> Przeslawski, R, Miller, KJ, Nichol, SL, Bouchet, PJ, Huang, Z, Kool, JT, Radford, B, Thums, M., 2015, <i>Developing a toolbox of predictive models for the monitoring and management of KEFs and CMRs in the North and North-west regions - Scientific Workshop Report</i>, Report in Marine Biodiversity Hub, National Environmental Science Programme (NESP)