The Exploring for the Future program Showcase 2022 was held online on 8-10 August 2022. Day 3 (10th August) included talks on two themes moderated by David Robinson. Minerals, energy and groundwater systems theme: - Upper Darling Floodplain - Dr Sarah Buckerfield - Geoscience insights from Energy Resources - Lidena Carr - Mineral systems insights: New concepts from old data - Dr David Huston Resource potential theme: - Mineral Potential: Narrowing the exploration search space - Dr Arianne Ford - CO2-Enhanced oil recovery: Application to residual oil zones - Dr Aleks Kalinowski - Hydrogen and green steel - Dr Andrew Feitz You can access the recording of the talks from YouTube here: Day 3 part 1 https://youtu.be/cdzn3JNReOs Day 3 part 2 https://youtu.be/DjghAig51Ao

Remotely sensed datasets provide fundamental information for understanding the chemical, physical and temporal dynamics of the atmosphere, lithosphere, biosphere and hydrosphere. Satellite remote sensing has been used extensively in mapping the nature and characteristics of the terrestrial land surface, including vegetation, rock, soil and landforms, across global to local-district scales. With the exception of hyper-arid regions, mapping rock and soil from space has been problematic because of vegetation that either masks the underlying substrate or confuses the spectral signatures of geological materials (i.e. diagnostic mineral spectral features), making them difficult to resolve. As part of the Exploring for the Future program, a new barest earth Landsat mosaic of the Australian continent using time-series analysis significantly reduces the influence of vegetation and enhances mapping of soil and exposed rock from space. Here, we provide a brief background on geological remote sensing and describe a suite of enhanced images using the barest earth Landsat mosaic for mapping surface mineralogy and geochemistry. These geological enhanced images provide improved inputs for predictive modelling of soil and rock properties over the Australian continent. In one case study, use of these products instead of existing Landsat TM band data to model chromium and sodium distribution using a random forest machine learning algorithm improved model performance by 28–46%. <b>Citation:</b> Wilford, J. and Roberts, D., 2020. Enhanced barest earth Landsat imagery for soil and lithological modelling. 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 dataset includes point estimates of groundwater recharge in mm/year. Recharge rates have been estimated at monitoring bore locations in the basaltic aquifers of the Nulla and McBride basalt provinces. Recharge estimates have been calculated using the “chloride mass balance” method. The chloride mass balance process assumes that the chloride ion is a conservative tracer in precipitation, evapotranspiration, recharge and runoff; and that all the chloride is from rainfall, instead of for example halite saturation or dissolution processes. So the volumetric water balance and the flux of chloride balance must both be true. Assuming that runoff and evapotranspiration are negligible (so approximated by zero), the equation is simplified: Water balance P=ET+R+Q Water balance multiplied by chloride concentrations (chloridefluxbalance) P∙Cl_ppt=ET∙Cl_ET+R∙Cl_gw+Q∙Cl_riv | ΔCl_reac≈0 Assumptions to simplify equation P∙Cl_ppt=R∙Cl_gw | Q≈0 & ET≈0 Rearranging for recharge rate (unknown) R=P∙(Cl_ppt)/(Cl_gw ) | Q≈0 & ET≈0 Where P = precipitation rate; ET = evapotranspiration rate; R = recharge rate; Q = runoff to streams; Clppt = concentration of Cl in precipitation; ClET = concentration of chloride in evapotranspiration; Clgw = concentration of Cl in groundwater; Clriv = concentration of chloride in river runoff; ΔClreac = change in chloride concentrations from reactions.

The Exploring for the Future program Showcase 2022 was held on 8-10 August 2022. Day 2 (9th August) included talks on two themes moderated by Marina Costelloe. Data and toolbox theme: - Data acquisition progress - Dr Laura Gow - Quantitative tool development: HiQGA.jl and HiPerSeis - Dr Anandaroop Ray - Data delivery advances: Underpinned by careful data curation - Mark Webster Geology theme: - Mapping Australia's geology: From the surface down to great depths - Dr Marie-Aude Bonnardot - Towards a national understanding of Groundwater - Dr Hashim Carey - Uncovering buried frontiers: Tennant Creek to Mount Isa - Anthony Schofield and Dr Chris Carson - Lithospheric characterisation: Mapping the depths of the Australian tectonic plate - Dr Marcus Haynes You can access the recording of the talks from YouTube here: Showcase Day 2 – Part 1 https://youtu.be/US6C-xzMsnI Showcase Day 2 – Part 2 https://youtu.be/ILRLXbQNnic

The Paleozoic alkaline and related igneous rocks of Australia web map service depicts the spatial representation of the alkaline and related rocks of Paleozoic age.

Improvements in discovery and management of minerals, energy and groundwater resources are spurred along by advancements in surface and subsurface imaging of the Earth. Over the last half decade Australia has led the world in the collection of regionally extensive airborne electromagnetic (AEM) data coverage, which provides new constraints on subsurface conductivity structure. Inferring geology and hydrology from conductivity is non-trivial as the conductivity response of earth materials is non-unique, but careful calibration and interpretation does provide significant insights into the subsurface. To date utility of this new data is limited by its spatial extent. The AusAEM survey provides conductivity constraints every 12.5 m along flight lines with no constraints across vast areas between flight lines spaced 20 km apart. Here we provide a means to infer the conductivity between flight lines as an interim measure before infill surveys can be undertaken. We use a gradient boosted tree machine learning algorithm to discover relationships between AEM conductivity models across northern Australia and other national data coverages for three depth ranges: 0–0.5 m, 9–11 m and 22–27 m. The predictive power of our models decreases with depth but they are nevertheless consistent with our knowledge of geological, landscape evolution and climatic processes and an improvement on standard interpolation methods such as kriging. Our models provide a novel complementary methodology to gridding/interpolating from AEM conductivity alone for use by the mining, energy and natural resource management sectors. <b>Citation: </b>Wilford J., Ley-Cooper Y., Basak S., & Czarnota K., 2022. High resolution conductivity mapping using regional AEM survey and machine learning. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146380.

As global metal demands are increasing whilst new discoveries are declining, the magnetotelluric (MT) technique has shown promise as an effective technique to aid mineral systems mapping. Several case studies have shown a spatial correlation between mineral deposits and conductors, with some showing that resistivity models derived from MT are capable of mapping mineral systems from the lithosphere to deposit scale. However, until now, the statistical significance of such correlations has not been demonstrated and therefore hindered robust utilization of MT data in mineral potential assessments. Here we quantitatively analyze resistivity models from Australia, the United States of America (USA), South America and China and demonstrate that there is a statistically-significant correlation between upper mantle conductors and porphyry copper deposits, and between mid-crustal conductors and orogenic gold deposits. Volcanic hosted massive sulfide deposits show significant correlation with upper mantle conductors in Australia. Differences in the correlation pattern between these deposit types likely relate to differences in the chemistry, redox state and location of source mineralizing fluids and magmas, and indicate signatures of mineral system processes can be preserved in the crust and mantle lithosphere for hundreds of millions of years. Appeared in Scientific Reports volume 12, Article number: 8190 (2022), 17 May 2022

The Paleozoic alkaline and related igneous rocks of Australia web map service depicts the spatial representation of the alkaline and related rocks of Paleozoic age.

This package contains presentations given during NT Resources week, at the Uncovering East Tennant workshop held in Darwin on September 3, 2019, and Mining the Territory, September 5, 2019. The presentation given by Andrew Heap at the Mining the Territory forum is a high level overview of the data collection and activities of GA and it's collaborative partners across Northern Australia in conjunction with the Exploring for the Future (EFTF) program. The workshop, held in collaboration with the Northern Territory Geological Survey, outlined new mineral exploration opportunities in the East Tennant area, which lies beneath the Barkly Tableland and extends approximately 250 km east of Tennant Creek. The East Tennant area has been the focus of geochemical, geological and geophysical data acquisition as part of Geoscience Australia's Exploring for the Future program. This free event showcased new science insights for the East Tennant area and how this under-explored region has opportunities for greenfield mineral discoveries.

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.