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 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.

<p>Geoscience Australia completed a regional assessment of the geological carbon dioxide (CO2) storage potential and petroleum prospectivity of the Browse Basin, offshore northwest Australia. This dual-purpose basin analysis study provided a new understanding of the basin’s Cretaceous succession based on new information regarding basin evolution, sequence stratigraphy, structural architecture and petroleum systems. The basin’s tectonostratigraphic framework was updated, and the integration of revised and recalibrated biostratigraphic data with well log and seismic interpretations has enabled an improved understanding of variations in depositional facies and the spatial distribution of reservoir, seal, and source rock sections. The outputs include models and maps of environments of deposition, play fairways, common risk element maps for regional-scale assessment of CO2 storage potential and petroleum systems model (Abbott et al., 2016; Edwards et al., 2015, 2016; Grosjean et al., 2015; Palu et al., 2017a and b; Rollet et al., 2016b, 2017a,b, 2018).<p> <p>This data pack includes 12 Cretaceous and Cenozoic horizons, and the regional fault maps produced from this study. This interpretation is based on data from 60 wells (Table 1) and 26 regional 2D and 3D seismic reflection surveys (Table 2) (Rollet et al., 2016a). Surfaces were converted from TWT to depth and integrated in a 3D geological model as input into a petroleum systems model (Palu et al., 2017a, b). <p>Data layers include: <p>12 regional depth surface grids and arcmap files generated for key Cretaceous and Cenozoic horizons (Figure 1; Table 3): K10.0_SB (late Tithonian), K20.0_SB (Valanginian), K30.0_SB (Late Hauterivian), K40.0_SB (Aptian), K50.0_SB (Late Cenomanian), K60.0_SB (Early Campanian), K65.0_SB (Maastrichtian), T10.0_SB (Base Cenozoic), T24.0_SB (Ypresian), T30.0_SB (Rupelian), T33.0_SB (Aquitanian) and water bottom based on bathymetry after Whiteway (2009), <p>2 fault population shapefiles (Figure 2): polygon envelop of shallow faults that formed during the Cenozoic collision between Australia and Asia, and horizon fault boundaries of deep regional faults that were formed through the Permian to Cretaceous.

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.

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.

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 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.