This data package, completed as part of Geoscience Australia’s National Groundwater Systems (NGS) Project, presents results of the second iteration of the 3D Great Artesian Basin (GAB) and Lake Eyre Basin (LEB) (Figure 1) geological and hydrogeological models (Vizy & Rollet, 2023) populated with volume of shale (Vshale) values calculated on 2,310 wells in the Surat, Eromanga, Carpentaria and Lake Eyre basins (Norton & Rollet, 2023). This provides a refined architecture of aquifer and aquitard geometry that can be used as a proxy for internal, lateral, and vertical, variability of rock properties within each of the 18 GAB-LEB hydrogeological units (Figure 2). These data compilations and information are brought to a common national standard to help improve hydrogeological conceptualisation of groundwater systems across multiple jurisdictions. This information will assist water managers to support responsible groundwater management and secure groundwater into the future. This 3D Vshale model of the GAB provides a common framework for further data integration with other disciplines, industry, academics and the public and helps assess the impact of water use and climate change. It aids in mapping current groundwater knowledge at a GAB-wide scale and identifying critical groundwater areas for long-term monitoring. The NGS project is part of the Exploring for the Future (EFTF) program—an eight-year, $225 million Australian Government funded geoscience data and precompetitive information acquisition program. The program seeks to inform decision-making by government, community, and industry on the sustainable development of Australia's mineral, energy, and groundwater resources, including those to support the effective long-term management of GAB water resources. This work builds on the first iteration completed as part of the Great Artesian Basin Groundwater project (Vizy & Rollet, 2022; Rollet et al., 2022), and infills previous data and knowledge gaps in the GAB and LEB with additional borehole, airborne electromagnetic and seismic interpretation. The Vshale values calculated on additional wells in the southern Surat and southern Eromanga basins and in the whole of Carpentaria and Lake Eyre basins provide higher resolution facies variability estimates from the distribution of generalised sand-shale ratio across the 18 GAB-LEB hydrogeological units. The data reveals a complex mixture of sedimentary environments in the GAB, and highlights sand body development and hydraulic characteristics within aquifers and aquitards. Understanding the regional extents of these sand-rich areas provides insights into potential preferential flow paths, within and between the GAB and LEB, and aquifer compartmentalisation. However, there are limitations that require further study, including data gaps and the need to integrate petrophysics and hydrogeological data. Incorporating major faults and other structures would also enhance our understanding of fluid flow pathways. The revised Vshale model, incorporating additional boreholes to a total of 2,310 boreholes, contributes to our understanding of groundwater flow and connectivity in the region, from the recharge beds to discharge at springs, and Groundwater Dependant Ecosystems (GDEs). It also facilitates interbasinal connectivity analysis. This 3D Vshale model offers a consistent framework for integrating data from various sources, allowing for the assessment of water use impacts and climate change at different scales. It can be used to map groundwater knowledge across the GAB and identify areas that require long-term monitoring. Additionally, the distribution of boreholes with gamma ray logs used for the Vshale work in each GAB and LEB units (Norton & Rollet, 2022; 2023) is used to highlight areas where additional data acquisition or interpretation is needed in data-poor areas within the GAB and LEB units. The second iteration of surfaces with additional Vshale calculation data points provides more confidence in the distribution of sand bodies at the whole GAB scale. The current model highlights that the main Precipice, Hutton, Adori-Springbok and Cadna-owie‒Hooray aquifers are relatively well connected within their respective extents, particularly the Precipice and Hutton Sandstone aquifers and equivalents. The Bungil Formation, the Mooga Sandstone and the Gubberamunda Sandstone are partial and regional aquifers, which are restricted to the Surat Basin. These are time equivalents to the Cadna-owie–Hooray major aquifer system that extends across the Eromanga Basin, as well as the Gilbert River Formation and Eulo Queen Group which are important aquifers onshore in the Carpentaria Basin. The current iteration of the Vshale model confirms that the Cadna-owie–Hooray and time equivalent units form a major aquifer system that spreads across the whole GAB. It consists of sand bodies within multiple channel belts that have varying degrees of connectivity' i.e. being a channelised system some of the sands will be encased within overbank deposits and isolated, while others will be stacked, cross-cutting systems that provide vertical connectivity. The channelised systemtransitions vertically and laterally into a shallow marine environment (Rollet et al., 2022). Sand-rich areas are also mapped within the main Poolowanna, Brikhead-Walloon and Westbourne interbasinal aquitards, as well as the regional Rolling Downs aquitard that may provide some potential pathways for upward leakage of groundwater to the shallow Winton-Mackunda aquifer and overlying Lake Eyre Basin. Further integration with hydrochemical data may help groundtruth some of these observations. This metadata document is associated with a data package including: • Seventeen surfaces with Vshale property (Table 1), • Seventeen surfaces with less than 40% Vshale property (Table 2), • Twenty isochore with average Vshale property (Table 3), • Twenty isochore with less than 40% Vshale property (Table 4), • Sixteen Average Vshale intersections of less than 40% Vshale property delineating potential connectivity between isochore (Table 5), • Sixteen Average Vshale intersections of less than 40% Vshale property delineating potential connectivity with isochore above and below (Table 6), • Seventeen upscaled Vshale log intersection locations (Table 7), • Six regional sections showing geology and Vshale property (Table 8), • Three datasets with location of boreholes, sections, and area of interest (Table 9).

The Layered Geology of Australia web map service is a seamless national coverage of Australia’s surface and subsurface geology. Geology concealed under younger cover units are mapped by effectively removing the overlying stratigraphy (Liu et al., 2015). This dataset is a layered product and comprises five chronostratigraphic time slices: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic. As an example, the Mesozoic time slice (or layer) shows Mesozoic age geology that would be present if all Cenozoic units were removed. The Pre-Neoproterozoic time slice shows what would be visible if all Neoproterozoic, Paleozoic, Mesozoic, and Cenozoic units were removed. The Cenozoic time slice layer for the national dataset was extracted from Raymond et al., 2012. Surface Geology of Australia, 1:1 000 000 scale, 2012 edition. Geoscience Australia, Canberra.

The Layered Geology of Australia web map service is a seamless national coverage of Australia’s surface and subsurface geology. Geology concealed under younger cover units are mapped by effectively removing the overlying stratigraphy (Liu et al., 2015). This dataset is a layered product and comprises five chronostratigraphic time slices: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic. As an example, the Mesozoic time slice (or layer) shows Mesozoic age geology that would be present if all Cenozoic units were removed. The Pre-Neoproterozoic time slice shows what would be visible if all Neoproterozoic, Paleozoic, Mesozoic, and Cenozoic units were removed. The Cenozoic time slice layer for the national dataset was extracted from Raymond et al., 2012. Surface Geology of Australia, 1:1 000 000 scale, 2012 edition. Geoscience Australia, Canberra.

Building on newly acquired airborne electromagnetic and seismic reflection data during the Exploring for the Future (EFTF) program, Geoscience Australia (GA) generated a cover model across the Northern Territory and Queensland, in the Tennant Creek – Mount Isa (TISA) area (Figure 1; between 13.5 and 24.5⁰ S of latitude and 131.5 and 145⁰ E of longitude) (Bonnardot et al., 2020). The cover model provides depth estimates to chronostratigraphic layers, including: Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic. The depth estimates are based on the interpretation, compilation and integration of borehole, solid geology, reflection seismic, and airborne electromagnetic data, as well as depth to magnetic source estimates. These depth estimates in metres below the surface (relative to the Australian Height Datum) are consistently stored as points in the Estimates of Geophysical and Geological Surfaces (EGGS) database (Matthews et al., 2020). The data points compiled in this data package were extracted from the EGGS database. Preferred depth estimates were selected to ensure regional data consistency and aid the gridding. Two sets of cover depth surfaces (Bonnardot et al., 2020) were generated using different approaches to map megasequence boundaries associated with the Era unconformities: 1) Standard interpolation using a minimum-curvature gridding algorithm that provides minimum misfit where data points exist, and 2) Machine learning approach (Uncover-ML, Wilford et al., 2020) that allows to learn about relationships between datasets and therefore can provide better depth estimates in areas of sparse data points distribution and assess uncertainties. This data package includes the depth estimates data points compiled and used for gridding each surface, for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic (Figure 1). To provide indicative trends between the depth data points, regional interpolated depth surface grids are also provided for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic. The grids were generated with a standard interpolation algorithm, i.e. minimum-curvature interpolation method. Refined gridding method will be necessary to take into account uncertainties between the various datasets and variable distances between the points. These surfaces provide a framework to assess the depth and possible spatial extent of resources, including basin-hosted mineral resources, basement-hosted mineral resources, hydrocarbons and groundwater, as well as an input to economic models of the viability of potential resource development.

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.

Geoscience Australia’s Exploring for the Future (EFTF) 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 a low emissions economy, strong 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. Further detail is available at http://www.ga.gov.au/eftf. The National Groundwater Systems (NGS) project, is part of the Australian Government’s Exploring for the Future (EFTF) program, led by Geoscience Australia (https://www.eftf.ga.gov.au/national-groundwater-systems), to improve understanding of Australia’s groundwater resources to better support responsible groundwater management and secure groundwater resources into the future. The project is developing new national data coverages to constrain groundwater systems, develop a new map of Australian groundwater systems and improve data standards and workflows of groundwater assessment to populate a consistent data discovery tool and web-based mapping portal to visualise, analyse and download hydrogeological information. While our hydrogeological conceptual understanding of Australian groundwater systems continues to grow in each State and Territory jurisdiction, in addition to legacy data and knowledge from the 1970s, new information provided by recent studies in various parts of Australia highlights the level of geological complexity and spatial variability in stratigraphic and hydrostratigraphic units across the continent. We recognise the need to standardise individual datasets, such as the location and elevation of boreholes recorded in different datasets from various sources, as well as the depth and nomenclature variations of stratigraphic picks interpreted across jurisdictions to map such geological complexity in a consistent, continent-wide stratigraphic framework that can support effective long-term management of water resources and integrated resource assessments. This stratigraphic units data compilation at a continental scale forms a single point of truth for basic borehole data including 47 data sources with 1 802 798 formation picks filtered to 1 001 851 unique preferred records from 171 367 boreholes. This data compilation provides a framework to interpret various borehole datasets consistently, and can then be used in a 3D domain as an input to improve the 3D aquifer geometry and the lateral variation and connectivity in hydrostratigraphic units across Australia. The reliability of each data source is weighted to use preferentially the most confident interpretation. Stratigraphic units are standardised to the Australian Stratigraphic Units Database (ASUD) nomenclature (https://asud.ga.gov.au/search-stratigraphic-units) and assigned the corresponding ASUD code to update the information more efficiently when needed. This dataset will need to be updated as information grows and is being revised over time. This dataset provides: 1. ABSUC_v1 Australian stratigraphic unit compilation dataset (ABSUC) 2. ABSUC_v1_TOP A subset of preferred top picks from the ABSUC_v1 dataset 3. ABSUC_v1_BASE A subset of preferred base picks from the ABSUC_v1 dataset 4. ABSUC_BOREHOLE_v1 ABSUC Borehole collar dataset 5. ASUD_2023 A subset of the Australia Stratigraphic Units Database (ASUD) This consistent stratigraphic units compilation has been used to refine the Great Artesian Basin geological and hydrogeological surfaces in this region and will support the mapping of other regional groundwater systems and other resources across the continent. It can also be used to map regional geology consistently for integrated resource assessments.

<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).

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.

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.