The Spatial Data Dictionary is a specification for the capture of geoscientific spatial data. It describes fields for each feature type in a database, containing the themes currently created from Geoscience Australia's databases. It forms a foundation for the production of geoscientific spatial data by specifying rules regarding the structure of such data. The dictionary covers such matters as allowable coverage names, feature types, and attribute values. A theme is a set of spatial objects. Some of the themes in this data dictionary have associated look-up tables. Look-up tables store an additional array of attributes that may be linked to the primary attribute table of a theme. Object type, feature definition, field type, attribute case, compulsion for data entry, a list of valid values and any rules or comments regarding the feature are also given in this data dictionary. The Data Dictionary consists of four modules: • Module 1: Definitions, Rules and Terminology • Module 2: Geology, Geophysical, Geochemistry and Geochronology Themes • Module 3: Mineral Deposits and Mineral Potential Assessment Themes, Surveys and Field Observations Themes • Module 4: Urban Infrastructure Themes, Terrain Physiography Themes, Cartographic Themes

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