<b>This data package is superseded by a second iteration presenting updates on 3D geological and hydrogeological surfaces across eastern Australia that can be accessed through </b><a href="https://dx.doi.org/10.26186/148552">https://dx.doi.org/10.26186/148552</a> The Australian Government, through the National Water Infrastructure Fund – Expansion, commissioned Geoscience Australia to undertake the project ‘Assessing the Status of Groundwater in the Great Artesian Basin’ (GAB). The project commenced in July 2019 and will finish in June 2022, with an aim to develop and evaluate new tools and techniques to assess the status of GAB groundwater systems in support of responsible management of basin water resources. While our hydrogeological conceptual understanding of the GAB continues to grow, in many places we are still reliant on legacy data and knowledge from the 1970s. Additional information provided by recent studies in various parts of the GAB highlights the level of complexity and spatial variability in hydrostratigraphic units across the basin. We now recognise the need to link these regional studies to map such geological complexity in a consistent, basin-wide hydrostratigraphic framework that can support effective long-term management of GAB water resources. Geological unit markers have been compiled and geological surfaces associated with lithostratigraphic units have been correlated across the GAB to update and refine the associated hydrogeological surfaces. Recent studies in the Surat Basin in Queensland and the Eromanga Basin in South Australia are integrated with investigations from other regions within the GAB. These bodies of work present an opportunity to link regional studies and develop a revised, internally consistent geological framework to map geological complexity across the GAB. Legacy borehole data from various sources, seismic and airborne electromagnetic (AEM) data were compiled, then combined and analysed in a common 3D domain. Correlation of interpreted geological units and stratigraphic markers from these various data sets are classified using a consistent nomenclature. This nomenclature uses geological unit subdivisions applied in the Surat Cumulative Management Area (OGIA (Office of Groundwater Impact Assessment), 2019) to correlate time equivalent regional hydrogeological units. Herein we provide an update of the surface extents and thicknesses for key hydrogeological units, reconciling geology across borders and providing the basis for a consistent hydrogeological framework at a basin-wide scale. The new surfaces can be used for facilitating an integrated basin systems assessment to improve our understanding of potential impacts from exploitation of sub-surface resources (e.g., extractive industries, agriculture and injection of large volumes of CO2 into the sub-surface) in the GAB and providing a basis for more robust water balance estimates. This report is associated with a data package including (Appendix A – Supplementary material): • Nineteen geological and hydrogeological surfaces from the Base Permo-Carboniferous, Top Permian, Base Jurassic, Base Cenozoic to the surface (Table 2.1), • Twenty-one geological and hydrogeological unit thickness maps from the top crystalline basement to the surface (Figure 3.7 to Figure 3.27), • The formation picks and constraining data points (i.e., from boreholes, seismic, AEM and outcrops) compiled and used for gridding each surface (Table 3.8).

There is a growing recognition that lithospheric structure places first-order controls on the distribution of resources within the upper crust. While this structure is increasingly imaged using geophysical techniques, there is a paucity of geological constraints on its morphology and temporal evolution. Cenozoic intraplate volcanic rocks along Australia’s eastern seaboard provide a significant opportunity to constrain mantle conditions at the time of their emplacement and thereby benchmark geophysical constraints. This volcanic activity is subdivided into two types: age-progressive provinces generated by the passage of mantle plumes beneath the plate; and age-independent provinces, which may arise from edge-driven convection at a lithospheric step. In this study, we collected and analysed 78 igneous rock samples from both types of volcanoes across Queensland. We combined these analyses with previous studies to create and augment a comprehensive database of Australian Cenozoic volcanism. Geochemical modelling techniques were used to estimate mantle temperatures and lithospheric thicknesses beneath each province. Our results show that melting occurred at depths of 45–70 km across eastern Australia. Mantle temperatures are inferred to be ~50–100 °C higher beneath age-progressive provinces than beneath age-independent provinces. These results agree with geophysical observations used to aid resource assessments and indicate that upper mantle temperatures have varied over Cenozoic times. <b>Citation:</b> Ball, P.W., Czarnota, K., White, N.J. and Champion, D.C. 2020. Exploiting Cenozoic volcanic activity to quantify upper mantle structure beneath eastern Australia. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

This service delivers the base of Cenozoic surface and Cenozoic thickness grids for the west Musgrave province. The gridded data are a product of 3D palaeovalley modelling based on airborne electromagnetic conductivity, borehole and geological outcrop data, carried out as part of Geoscience Australia's Exploring for the Future programme. The West Musgrave 3D palaeovalley model report and data files are available at https://dx.doi.org/10.26186/149152.

This service delivers the base of Cenozoic surface and Cenozoic thickness grids for the west Musgrave province. The gridded data are a product of 3D palaeovalley modelling based on airborne electromagnetic conductivity, borehole and geological outcrop data, carried out as part of Geoscience Australia's Exploring for the Future programme. The West Musgrave 3D palaeovalley model report and data files are available at https://dx.doi.org/10.26186/149152.

To meet the increasing demand for natural resources globally, industry faces the challenge of exploring new frontier areas that lie deeper undercover. Here, we present an approach to, and initial results of, modelling the depth of four key chronostratigraphic packages that obscure or host mineral, energy and groundwater resources. Our models are underpinned by the compilation and integration of ~200 000 estimates of the depth of these interfaces. Estimates are derived from interpretations of newly acquired airborne electromagnetic and seismic reflection data, along with boreholes, surface and solid geology, and depth to magnetic source investigations. Our curated estimates are stored in a consistent subsurface data repository. We use interpolation and machine learning algorithms to predict the distribution of these four packages away from the control points. Specifically, we focus on modelling the distribution of the base of Cenozoic-, Mesozoic-, Paleozoic- and Neoproterozoic-age stratigraphic units across an area of ~1.5 million km2 spanning the Queensland and Northern Territory border. Our repeatable and updatable approach to mapping these surfaces, together with the underlying datasets and resulting models, provides a semi-national geometric framework for resource assessment and exploration. <b>Citation:</b> Bonnardot, M.-A., Wilford, J., Rollet, N., Moushall, B., Czarnota, K., Wong, S.C.T. and Nicoll, M.G., 2020. Mapping the cover in northern Australia: towards a unified national 3D geological model. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

This service delivers the base of Cenozoic surface and Cenozoic thickness grids for the west Musgrave province. The gridded data are a product of 3D palaeovalley modelling based on airborne electromagnetic conductivity, borehole and geological outcrop data, carried out as part of Geoscience Australia's Exploring for the Future programme. The West Musgrave 3D palaeovalley model report and data files are available at https://dx.doi.org/10.26186/149152.

The Solid Geology of the North Australian Craton web service delivers a seamless chronostratigraphic solid geology dataset of the North Australian Craton that covers north of Western Australia, Northern Territory and north-west Queensland. The data maps stratigraphic units concealed under cover by effectively removing the overlying cover (Liu et al., 2015). This dataset comprises five chronostratigraphic time slices, namely: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic.

The Solid Geology of the North Australian Craton web service delivers a seamless chronostratigraphic solid geology dataset of the North Australian Craton that covers north of Western Australia, Northern Territory and north-west Queensland. The data maps stratigraphic units concealed under cover by effectively removing the overlying cover (Liu et al., 2015). This dataset comprises five chronostratigraphic time slices, namely: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic.

The Solid Geology of the North Australian Craton web service delivers a seamless chronostratigraphic solid geology dataset of the North Australian Craton that covers north of Western Australia, Northern Territory and north-west Queensland. The data maps stratigraphic units concealed under cover by effectively removing the overlying cover (Liu et al., 2015). This dataset comprises five chronostratigraphic time slices, namely: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic.

The Murray Basin is a saucer-shaped basin with flat-lying Cenozoic sediments up to approximately 600 m thickness (Brown and Stephenson, 1991). Constraints on the thickness of the Murray Basin have been compiled from: drillholes, reflection seismic profile interpretations, refraction seismic profiles and depth to magnetic basement estimates (Target_type.pdf). Target depths were sourced from Geoscience Australia, the national Groundwater Information System database (Http://www.bom.gov.au/water/groundwater/ngis/), the Geological Survey of Victoria (http://earthresources.vic.gov.au/earth-resources/geology-of-victoria/geological-survey-of-victoria) and the Geological Survey of South Australia (http://www.minerals.statedevelopment.sa.gov.au/geoscience/geological_survey). In addition, some of the magnetic depth estimates used data from McLean (2010). To constrain the thickness of Cenozoic cover where sediments were either absent or very thin we generated shallow-depth values in areas with post-Cenozoic geology and high topographic relief. In all, 5436 depth estimates were compiled (Target_depths.xlsx). The input datasets have been used to generate two predictive models of the thickness of Cenozoic sediments within the Murray Basin. The first model uses kriging of the depth estimates to generate a gridded surface using a local-area linear variogram model as a means of interpolating between constraints (Murray_Basin_kriging_Cenozoic_thickness.pdf; Murray_Basin_krig.tif -floating value grid). The second model uses a machine-learning approach where correlations between 17 supplementary datasets and 5436 depth estimates are used to derive a predictive model. We used a supervised learning algorithm known as Gaussian Process (GP) to generate the integrated predictive model. Gaussian Process is a non-parametric probabilistic approach to learning. It uses kernel functions to measure the similarity between points and predict values not seen from the training data (see Read_Me_GP.rtf). The supplementary datasets used in the model are listed in Table 1 and model variable settings can be found in read_me.rtf (Murray_Basin_GP_Cenozoic_thickness.pdf; Murray_Basin_GP_model.tif -floating value grid). Both approaches delineate the overall structure, geometry and thickness of the Murray Basin. The advantage of the machine learning approach is that it learns relationships between the depth and supplementary datasets which allow predictions in areas with limited constraints. References Brown, C. M. and Stephenson, A. E., 1991, Geology of the Murray Basin, southeastern Australia, Canberra, Bureau of Mineral Resources Bulletin 235, 430 p. McLean, M.A., 2010. Depth to Palaeozoic basement of the Gold Undercover region from borehole and magnetic data. GeoScience Victoria Gold Undercover Report 21. Department of Primary Industries, Victoria. Table 1. Supplementary input datasets used in predictive estimation of Murray Basin thickness, utilising a machine learning method Covariates* Description 1 Latitude Gridded latitude values 2 Longitude Gridded longitude values 3 Elevation Terrain elevation – 90m shuttle DEM 4 Distance from bedrock Euclidean distance from outcrop geology units older than Cenozoic 5 Gravity Terrain and isostatic corrected Bouguer gravity 6 Gravity 1228 Upward continued gravity at 1228 metres 7 Gravity 2407 Upward continued gravity at 2407 metres 8 Gravity 6605 Upward continued gravity at 6605 metres 9 Gravity 18124 Upward continued gravity at 18124 metres 10 Gravity 35524 Upward continued gravity at 35524 metres 11 Gravity 49734 Upward continued gravity at 49734 metres 12 Gravity 97479 Upward continued gravity at 97479 metres 13 Gravity – 1k Isostatically corrected gravity subtracted from upward continued gravity at 1000 metres 14 Magnetics 5km Upward continued magnetic anomaly grid at 5 km 15 Magnetic 10km Upward continued magnetic anomaly grid at 10 km 16 Magnetic 5-10km Upward continued 5km magnetic anomaly grid subtracted from upward continued 10 km magnetic anomaly grid 17 Magnetic basement Depth to magnetic basement using the tilt method. *Primary datasets including gravity, magnetics and surface geology sourced from Geoscience Australia http://www.ga.gov.au/data-pubs/maps Elevation dataset used the 3 second (~90m) Shuttle Radar Topography Mission (SRTM) digital elevation model. https://pid.geoscience.gov.au/dataset/ga/72760.