<p>Iron oxide copper-gold (IOCG) deposits are consequences of lithospheric- to deposit-scale earth processes, and form where there was a coincidence of ore-forming processes in space and time. Building on previous conceptualisations we view a ‘mineral system’ as an ore-forming geological system in which four components are required to have operated efficiently and coincidentally, namely: (1) available sources of ore metals (i.e., copper, gold, uranium, rareearth elements) and hydrothermal fluids; (2) energy sources to drive fluids in the ore-forming system; (3) active crustal and mantle lithospheric architecture, representing hydrothermal fluid and/or magma flow pathways; and (4) physico-chemical gradients along which ore metals were deposited to form ore bodies. <p>This holistic multi-scale mineral systems framework has been used to develop a practical, knowledge-based yet data-rich, prospectivity mapping method applicable at regional to continental scales for hydrothermal and orthomagmatic ore-forming systems. We demonstrate how the mineral system components can be translated into mappable criteria and show how maps of mineral potential are generated by integrating diverse and rich input data sets. The method enables prediction of mineral potential not only in brownfields areas but also in greenfields and covered terranes with no previously known mineralisation. Here we report the application of this methodology in regional-scale mapping of the potential for iron oxide Cu-Au (IOCG) deposits in Australia, using examples from five studies over the last decade in northern Queensland, eastern South Australia, and southern and central-eastern Northern Territory. Uncertainties in the results arising from assignment of weightings to input data layers were investigated by the application of Monte Carlo-type probabilistic simulations. The results of 500 iterations using randomly assigned weightings overall support the deterministic results but also show that modelled prospectivity is controlled mainly by variations in intrinsic values of the input geoscientific data sets (e.g. highs and lows of gravity values) rather than by the weightings. <p>The results of the knowledge-driven data-rich analyses of IOCG potential have been validated against known IOCG deposits (not used directly in the analysis). We find in all five studies (Queensland, South Australia and Northern Territory) a good spatial correspondence, with few exceptions. Statistical analysis of prospectivity mapping results from the Tennant Creek – Mt Isa study area demonstrate that 15 of 16 IOCG deposits occur in the highest modelled prospectivity areas within 4.2% of the study area, representing an area reduction of 95.8%. Moreover, several new discoveries of Cu-Au mineralisation have been made within areas previously highlighted as highly prospective. This success and validation support the utility of Geoscience Australia’s approach as a decision-support tool to assist exploration companies and governments in cratonto regional-scale area selection for discovery of IOCG and other mineral systems.

<div>A national compilation of airborne electromagnetic (AEM) conductivity–depth models from AusAEM (Ley-Cooper et al. 2020) survey line data and other surveys (see reference list in the attachments) has been used to train a conductivity model prediction for the 0-4 m and 30 m depth intervals. Over 460,000 training points/measurements were used in a 5 K-Fold training and validation split. A further 28,626 points/measurements were used to assess the out of sample performance (OOS; i.e. points not used in the model validation). Modelling of the conductivity values (i.e. measurements along the AEM survey lines) was performed using the gradient boosted (GB) tree algorithm. The GB model is a machine learning (ML) ensemble technique used for both regression and classification tasks (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html). Samples along the flight-line were thinned to approximately one sample per 300 m. This avoided the situation where we could have more than one sample per pixel (i.e. features or covariates used in the model prediction have a cell or pixel size of 80 m) that could otherwise lead to over fitting. In addition, out of sample set used label clusters or groups to minimise overfitting. Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th respectively) to measure the model uncertainty. Grids show conductivity (S/m) in log 10 units. The methodology used to generate these conductivity grids are overall similar to that described by Wilford, et al. 2022.</div><div>&nbsp;</div><div>Reported out-of-sample r-squares for the 0-4 m and 3 m depths are 0.76 and 0.74, respectively. The ML approach allows estimation of conductivity into areas where we do not have airborne electromagnetic survey coverage. Hence these model have a national extent. Where we do not have AEM survey coverage the model is finding relationships with the covariates and making informed estimates of conductivity in those areas. Where those relationships are not well understood (i.e. where we see a departure in the feature space characteristics from what the model can ‘see’) the model prediction is likely to be less certain. Differences in the features and their corresponding values ‘seen’ and used in the model versus the full feature space covering the entire continent are captured in the covariate shift map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map.</div><div>&nbsp;</div><div>Datasets in this data package include:</div><div>&nbsp;</div><div>1. 0_4m_conductivity_prediction_median.tif</div><div>2. 0_4m_conductivity_lower_percentile_5th.tif</div><div>3. 0_4m_conductivity_upper_percentile_95th.tif</div><div>4. 30m_conductivity_prediction_median.tif</div><div>5.30m_conductivity_lower_percentile_5th.tif</div><div>6. 30m_conductivity_upper_percentile_95th.tif</div><div>7. National_conductivity_model_shift.tif</div><div>8. Full list of referenced AEM survey datasets used to train the model (word document)</div><div>9. Map showing the distribution of training and out-of-sample sites</div><div><br></div><div>All the Geotiffs (1-6) are in log (10) electrical conductivity siemens per metre (S/m).</div><div>&nbsp;</div><div>This work is part of Geoscience Australia’s Exploring for the Future program which provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div><div><br></div><div><br></div><div><strong>Reference:</strong></div><div><br></div><div>Ley-Cooper, A. Y., Brodie, R.C., and Richardson, M. 2020. AusAEM: Australia’s airborne electromagnetic continental-scale acquisition program, Exploration Geophysics, 51:1, 193-202, DOI: 10.1080/08123985.2019.1694393</div><div><br></div><div>Wilford, J., LeyCooper, Y., Basak, S., Czarnota, K. 2022. High resolution conductivity mapping using regional AEM survey and machine learning. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146380</div>

Geoscience Australia’s Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. The name ‘Birrindudu Basin’ was first introduced by Blake et al. (1975) and Sweet (1977) for a succession of clastic sedimentary rocks and carbonates, originally considered to be Paleoproterozoic to Neoproterozoic in age, and overlain by the Neoproterozoic Victoria Basin (Dunster et al., 2000), formerly known as the Victoria River Basin (see Sweet, 1977).