<div> A key issue for explorers in Australia is the abundant sedimentary and regolith cover obscuring access to underlying potentially prospective rocks. &nbsp;Multilayered chronostratigraphic interpretation of regional broad line-spaced (~20&nbsp;km) airborne electromagnetic (AEM) conductivity sections have led to breakthroughs in Australia’s near-surface geoscience. &nbsp;A dedicated/systematic workflow has been developed to characterise the thickness of cover and the depth to basement rocks, by delineating contact geometries, and by capturing stratigraphic units, their ages and relationships. &nbsp;Results provide a fundamental geological framework, currently covering 27% of the Australian continent, or approximately 2,085,000&nbsp;km2. &nbsp;Delivery as precompetitive data in various non-proprietary formats and on various platforms ensures that these interpretations represent an enduring and meaningful contribution to academia, government and industry.&nbsp;The outputs support resource exploration, hazard mapping, environmental management, and uncertainty attribution.&nbsp;This work encourages exploration investment, can reduce exploration risks and costs, helps expand search area whilst aiding target identification, and allows users to make well-informed decisions. Presented herein are some key findings from interpretations in potentially prospective, yet in some cases, underexplored regions from around Australia.&nbsp;</div> This abstract was submitted & presented to the 8th International Airborne Electromagnetics Workshop (AEM2023) (https://www.aseg.org.au/news/aem-2023)

<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>

<div>In Australia, wide-spread sedimentary basin and regolith cover presents a key challenge to explorers, environmental managers and decision-makers, as it obscures underlying rocks of interest. To address this, a national coverage of airborne electromagnetics (AEM) with a 20&nbsp;km line-spacing is being acquired. This survey is acquired as part of the Exploring for the Future program and in collaboration with state and territory geological surveys. This survey presents an opportunity for regional geological interpretations on the modelled AEM data, helping constrain the characteristics of the near-surface geology beneath the abundant cover, to a depth of up to ~500&nbsp;m.</div><div> The AEM conductivity sections were used to delineate key chronostratigraphic boundaries, e.g. the bases of geological eras, and provide a first-pass interpretation of the subsurface geology. The interpretation was conducted with a high level of data integration with boreholes, potential fields geophysics, seismic, surface geology maps and solid geology maps. This approach led to the construction of well-informed geological interpretations and provided a platform for ongoing quality assurance and quality control of the interpretations and supporting datasets. These interpretations are delivered across various platforms in multidimensional non-proprietary open formats, and have been formatted for direct upload to Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database, the national repository of multidisciplinary subsurface depth estimates.</div><div> These interpretations have resulted in significant advancements in our understanding of Australia’s near-surface geoscience, by revealing valuable information about the thickness and composition of the extensive cover, as well as the composition, structure and distribution of underlying rocks. Current interpretation coverage is ~110,000 line kilometres of AEM conductivity sections, or an area &gt;2,000,000&nbsp;km2, similar to the area of Greenland or Saudi Arabia. This ongoing work has led to the production of almost 600,000 depth estimate points, each attributed with interpretation-specific metadata. Three-dimensional line work and over 300,000 points are currently available for visualisation, integration and download through the GA Portal, or for download through GA’s eCat electronic catalogue. </div><div> These interpretations demonstrate the benefits of acquiring broadly-spaced AEM surveys. Interpretations derived from these surveys are important in supporting regional environmental management, resource exploration, hazard mapping, and stratigraphic unit certainty quantification. Delivered as precompetitive data, these interpretations provide users in academia, government and industry with a multidisciplinary tool for a wide range of investigations, and as a basis for further geoscientific studies.</div> Abstract submitted and presented at 2023 Australian Earth Science Convention (AESC), Perth WA (https://2023.aegc.com.au/)