This animation shows how Airborne Electromagnetic Surveys Work, when conducted by a rotary wing (helicopter) aircraft. It is part of a series of Field Activity Technique Engagement Animations. The target audience are the communities that are impacted by our data acquisition activities. There is no sound or voice over. The 2D animation includes a simplified view of what AEM equipment looks like, what the equipment measures and how the survey works.

<div>In July 2022 an airborne electromagnetic (AEM) survey was flown over and around the proposed site of the National Radioactive Waste Management Facility near the township of Kimba in South Australia.&nbsp;The survey was commissioned by the Australian Radioactive Waste Agency, and was project managed by Geoscience Australia. The survey has Geoscience Australia airborne survey project number P5008.</div><div><br></div><div>The survey was flown by Skytem Australia Pty Ltd using its SkyTEM312Fast AEM system.&nbsp;The survey was conducted on east-west lines at 500 m spacing, with a smaller central focus area of 100 m spaced lines, acquiring a total of 2,545 line kilometres of data. Skytem Australia Pty Ltd also processed the data.</div><div><br></div><div>This data package includes the acquisition and processing report, the final processed AEM data and the results of the 1D laterally constrained inversion of the data to conductivity-depth estimates that was carried out by the contractor.</div>

<div>Long-period magnetotelluric (MT) data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP), collected as part of Geoscience Australia’s Exploring for the Future program with contributions from the Northern Territory Geological Survey and the Geological Survey of Queensland, provide important first-order information for resolving large-scale lithospheric architecture and identifying the broad footprint of mineral systems in northern Australia. Large-scale crust/mantle conductivity anomalies map pathways of palaeo-fluid migration which is an important element of several mineral systems. For example, the Carpentaria conductivity anomaly east of Mount Isa and the Croydon, Georgetown to Greenvale conductivity anomaly are highly conductive lithospheric-scale structures, and show spatial correlations with major suture zones and known mineral deposits. These results provide evidence that some mineralisation occurs at the gradient of or over highly conductive structures at lower crustal and lithospheric mantle depths, which may represent fertile source regions for mineral systems. These observations provide a powerful means of highlighting prospective greenfield areas for mineral exploration in under-explored and covered regions.</div><div><br></div><div>Higher resolution scale-reduction MT surveys refine the geometry of some conductive anomalies from AusLAMP data, and investigate whether these deep conductivity anomalies link to the near surface. These links may act as conduits for crustal/mantle scale fluid migration to the upper crust, where they could form mineral deposits. For example, data reveals a favourable crustal architecture linking the deep conductivity anomaly or fertile source regions to the upper crust in the Cloncurry region. In addition, high-frequency MT data help to characterise cover and assist with selecting targets for drilling and improve the understanding of basement geology.</div><div><br></div><div>These results demonstrate that integration of multi-scale MT surveys is an effective approach for mapping lithospheric-scale features and selecting prospective areas for mineral exploration in covered terranes with limited geological knowledge.</div><div><br></div><div>Some models in this presentation were produced on the National Computational Infrastructure, which is supported by the Australian government. Abstract presented to the Australian Institute of Geoscientists – ALS Friday Seminar Series: Geophysical and Geochemical Signatures of Queensland Mineral Deposits October 2023 (https://www.aig.org.au/events/aig-als-friday-seminar-series-geophysical-and-geochemical-signatures-of-qld-mineral-deposits/)

This animation shows how Airborne Electromagnetic Surveys Work. It is part of a series of Field Activity Technique Engagement Animations. The target audience are the communities that are impacted by our data acquisition activities. There is no sound or voice over. The 2D animations include a simplified view of what AEM equipment looks like, what the equipment measures and how the survey works.

This animation shows how Magnetotelluric (MT) Surveys Work. It is part of a series of Field Activity Technique Engagement Animations. The target audience are the communities that are impacted by our data acquisition activities. There is no sound or voice over. The 2D animation includes a simplified view of what magnetotelluric (MT) stations and equipment looks like what the equipment measures and how the survey works.

Space weather manifests in power networks as quasi‐DC currents flowing in and out of the power system through the grounded neutrals of high‐voltage transformers, referred to as geomagnetically induced currents. This paper presents a comparison of modeled geomagnetically induced currents, determined using geoelectric fields derived from four different impedance models employing different conductivity structures, with geomagnetically induced current measurements from within the power system of the eastern states of Australia. The four different impedance models are a uniform conductivity model (UC), one‐dimensional n‐layered conductivity models (NU and NW), and a three‐dimensional conductivity model of the Australian region (3DM) from which magnetotelluric impedance tensors are calculated. The modeled 3DM tensors show good agreement with measured magnetotelluric tensors obtained from recently released data from the Australian Lithospheric Architecture Magnetotelluric Project. The four different impedance models are applied to a network model for four geomagnetic storms of solar cycle 24 and compared with observations from up to eight different locations within the network. The models are assessed using several statistical performance parameters. For correlation values greater than 0.8 and amplitude scale factors less than 2, the 3DM model performs better than the simpler conductivity models. When considering the model performance parameter, P, the highest individual P value was for the 3DM model. The implications of the results are discussed in terms of the underlying geological structures and the power network electrical parameters. <b>Citation:</b> Marshall, R. A., Wang, L., Paskos, G. A., Olivares‐Pulido, G., Van Der Walt, T., Ong, C., et al. (2019). Modeling geomagnetically induced currents in Australian power networks using different conductivity models. <i>Space Weather</i>, 17. https://doi.org/10.1029/2018SW002047

These conductivity grids were generated by gridding the top 22 layers from the airborne electromagnetics (AEM) conductivity models from the Western Resource Corridor AusAEM survey (https://dx.doi.org/10.26186/147688), the Earaheedy and Desert Strip AusAEM survey (https://pid.geoscience.gov.au/dataset/ga/145265) and several industry surveys (https://dx.doi.org/10.26186/146278) from the West Musgraves. The grids resolve important subsurface features for assessing the groudnwater system including lithologial boundaires, palaeovalleys and hydrostatigraphy.

The NSW component of the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP), is a collaboration between Geoscience Australia and the Geological Survey of New South Wales which commenced in 2016. Long-period MT data have been recorded at a 55-km spacing in a rolling deployment which to date has completed 224 of a planned 320 sites in NSW. This article summarises the progress of the AusLAMP NSW program and highlights how it is contributing to our understanding of the tectonic architecture in NSW.

<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>

We present a 3‐D inversion of magnetotelluric data acquired along a 340‐km transect in Central Australia. The results are interpreted with a coincident deep crustal seismic reflection survey and magnetic inversion. The profile crosses three Paleoproterozoic to Mesoproterozoic basement provinces, the Davenport, Aileron, and Warumpi Provinces, which are overlain by remnants of the Neoproterozoic to Cambrian Centralian Surperbasin, the Georgina and Amadeus Basins, and the Irindina Province. The inversion shows conductors near the base of the Irindina Province that connect to moderately conductive pathways from 50‐km depth and to off‐profile conductors at shallower depths. The shallow conductors may reflect anisotropic resistivity and are interpreted as sulfide minerals in fractures and faults near the base of the Irindina Province. Beneath the Amadeus Basin, and in the Aileron Province, there are two conductors associated strong magnetic susceptibilities from inversions, suggesting they are caused by magnetic, conductive minerals such as magnetite or pyrrhotite. Beneath the Davenport Province, the inversion images a conductive layer from ∼15‐ to 40‐km depth that is associated with elevated magnetic susceptibility and high seismic reflectivity. The margins between the different basement provinces from previous seismic interpretations are evident in the resistivity model. The positioning and geometry of the southern margin of the crustal conductor beneath the Davenport Province supports the positioning of the south dipping Atuckera Fault as interpreted on the seismic data. Likewise, the interpreted north dipping margin between the Warumpi and Aileron Province is imaged as a transition from resistive to conductive crust, with a steeply north dipping geometry.