<p>The South West McArthur, Barkly Gravity Survey P201901 is a gravity survey jointly funded under Geoscience Australia’s (GA) Exploring for the Future program and the Northern Territory Geological Survey's (NTGS) Resourcing the Territory 2018-2022 Initiative. Atlas Geophysics was commissioned by GA to conduct the survey, supporting both GA's and NTGS's programs. The survey supports GA's Exploring for the Future program, and NTGS's unlocking the resource potential of the Barkly Tablelands. <p>The survey infills existing 4km gravity coverage to 2km coverage. This is the second part of a larger gravity survey, the first being the East Tennant Gravity Survey P201901, NT, 2019 (eCat number 132968). Together the two surveys can be called the Tennant Creek Mount Isa (TISA) Gravity Surveys, P201901. <p>The data package consist of 3,303 gravity stations as a point located dataset and grids of the newly acquired gravity data

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 OGC compliant service provides access to magnetotelluric data and associated products, which have been produced by Geoscience Australia’s Magnetotelluric Program. This program includes regional magnetotelluric projects and the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP), a collaborative project between Geoscience Australia, the State and Northern Territory geological surveys, universities, and other research organisations. The data provided in this service comprise resistivity model depth sections and the locations of sites used in these studies.

High-grade gold (Au), copper (Cu) and bismuth (Bi) ores in the Tennant Creek goldfield have been mined from hydrothermal magnetite and/or hematite-rich ironstone bodies. Less well known is a style of Au-Cu-Bi mineralisation hosted by quartz vein systems within shear zones outside ironstones. Sensitive High Resolution Ion Micro Probe (SHRIMP) U-Pb-Th analyses of hydrothermal monazite [(LREE)PO4] associated with this mineralisation style at the Orlando East Au-Cu-Bi deposit and Navigator 6 Au prospect yield ages of 1659 ± 13 Ma and 1659 ± 15 Ma, respectively. These ages are nearly 200 million years younger than the age established from ironstone-hosted ores in the district. This new result widens the exploration ‘search space’ for gold into rock formations previously regarded as too young to host this style of mineralisation. <b>Citation:</b> Skirrow, R.G., Cross, A.J., Magee, C.W., Lecomte, A., and Mercadier, J., 2020. Identification of a new ca. 1660 Ma Au-Cu-Bi metallogenic event at Tennant Creek. 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 report presents the results of an elemental and carbon and oxygen isotope chemostratigraphy study on three historic wells; Kidson-1, Willara-1 and Samphire Marsh-1, from the southern Canning Basin, Western Australia. The objective of this study was to correlate the Early to Middle Ordovician sections of the three wells to each other and to wells with existing elemental and carbonate carbon isotope chemostratigraphy data from the Broome Platform, Kidson and Willara sub-basins, and the recently drilled and fully cored stratigraphic Waukarlycarly 1 well from the Waukarlycarly Embayment.

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.

The WOfS summary statistic represents, for each pixel, the percentage of time that water is detected at the surface relative to the total number of clear observations. Due to the 25-m by 25-m pixel size of Landsat data, only features greater than 25m by 25m are detected and only features covering multiple pixels are consistently detected. The WOfS summary statistic was produced over the McBride and Nulla Basalt provinces for the entire period of available data (1987 to 2018). Pixels were polygonised and classified in order to visually enhance key data in the imagery. Areas depicted in the dataset have been exaggerated to enable visibility.

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

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.

This Surat Basin dataset contains descriptive attribute information for the areas bounded by the relevant spatial groundwater feature in the associated Hydrogeology Index map. Descriptive topics are grouped into the following themes: Location and administration; Demographics; Physical geography; Surface water; Geology; Hydrogeology; Groundwater; Groundwater management and use; Environment; Land use and industry types; and Scientific stimulus. The Surat Basin is a sedimentary basin with approximately 2500 m of clastic fluvial, estuarine, coastal plain, and shallow marine sedimentary rocks, including sandstone, siltstone, mudstone, and coal. Deposition occurred over six cycles from the Early Jurassic to the Cretaceous, influenced by eustatic sea-level changes. Each cycle lasted 10 to 20 million years, ending around the mid-Cretaceous. Bounded by the Auburn Arch to the northeast and the New England Orogen to the southeast, it connects to the Clarence-Moreton Basin through the Kumbarilla Ridge. The Central Fold Belt forms its southern edge, while Cenozoic uplift caused erosion in the north. The basin's architecture is influenced by pre-existing faults and folds in the underlying Bowen Basin and the nature of the basement rocks from underlying orogenic complexes. Notable features include the north-trending Mimosa Syncline and Boomi Trough, overlying the deeper Taroom Trough of the Bowen Basin and extending southwards. The Surat Basin overlies older Permian to Triassic sedimentary basins like the Bowen and Gunnedah Basins, unconformably resting on various older basement rock terranes, such as the Lachlan Orogen, New England Orogen, and Thomson Orogen. Several Palaeozoic basement highs mark its boundaries, including the Eulo-Nebine Ridge in the west and the Kumbarilla Ridge in the east. Paleogene to Neogene sediments, like those from the Glendower Formation, cover parts of the Surat Basin. Remnant pediments and Cenozoic palaeovalleys incised into the basin have added complexity to its geological history and may influence aquifer connections. Overall, the Surat Basin's geological history is characterized by millions of years of sedimentation, tectonic activity, and erosion, contributing to its geological diversity and economic significance as a source of natural resources, including coal and natural gas.