This Record presents new U–Pb geochronological data, obtained via Sensitive High Resolution Ion Micro Probe (SHRIMP), from 43 samples of predominantly igneous rocks collected from the East Riverina region of the central Lachlan Orogen, New South Wales. The results presented herein correspond to the reporting period July 2016–June 2020. This work is part of an ongoing Geochronology Project, conducted by the Geological Survey of New South Wales (GSNSW) and Geoscience Australia (GA) under a National Collaborative Framework agreement, to better understand the geological evolution and mineral prospectivity of the central Lachlan Orogen in southern NSW (Bodorkos et al., 2013; 2015; 2016, 2018; Waltenberg et al., 2019).

Australia has been, and continues to be, a leader in isotope geochronology and geochemistry. While new isotopic data is being produced with ever increasing pace and diversity, there is also a rich legacy of existing high-quality age and isotopic data, most of which have been dispersed across a multitude of journal papers, reports and theses. Where compilations of isotopic data exist, they tend to have been undertaken at variable geographic scale, with variable purpose, format, styles, levels of detail and completeness. Consequently, it has been difficult to visualise or interrogate the collective value of age and isotopic data at continental-scale. Age and isotopic patterns at continental scale can provide intriguing insights into the temporal and chemical evolution of the continent (Fraser et al, 2020). As national custodian of geoscience data, Geoscience Australia has addressed this challenge by developing an Isotopic Atlas of Australia, which currently (as of November 2020) consists of national-scale coverages of four widely-used age and isotopic data-types: 4008 U-Pb mineral ages from magmatic, metamorphic and sedimentary rocks 2651 Sm-Nd whole-rock analyses, primarily of granites and felsic volcanics 5696 Lu-Hf (136 samples) and 553 O-isotope (24 samples) analyses of zircon 1522 Pb-Pb analyses of ores and ore-related minerals These isotopic coverages are now freely available as web-services for use and download from the GA Portal. While there is more legacy data to be added, and a never-ending stream of new data constantly emerging, the provision of these national coverages with consistent classification and attribution provides a range of benefits: vastly reduces duplication of effort in compiling bespoke datasets for specific regions or use-cases data density is sufficient to reveal meaningful temporal and spatial patterns a guide to the existence and source of data in areas of interest, and of major data gaps to be addressed in future work facilitates production of thematic maps from subsets of data. For example, a magmatic age map, or K-Ar mica cooling age map sample metadata such as lithology and stratigraphic unit is associated with each isotopic result, allowing for further filtering, subsetting and interpretation. The Isotopic Atlas of Australia will continue to develop via the addition of both new and legacy data to existing coverages, and by the addition of new data coverages from a wider range of isotopic systems and a wider range of geological sample media (e.g. soil, regolith and groundwater).

The Exploring for the Future program is an initiative by the Australian Government dedicated to boosting investment in resource exploration in Northern Australia. The Paleo- to Mesoproterozoic sedimentary and volcanic sequences of the Mount Isa–McArthur Basin region of Northern Territory and Queensland are host to a range of world class mineral deposits (Hutton et al., 2012) and include the basin-hosted base metal deposits of the North Australian Zinc Belt, the world’s richest belt of zinc deposits (Huston et al., 2006; Large et al., 2005). The region demonstrably has potential for additional world class mineral systems (Hutton et al. 2012), as well as potential to host shale gas plays (Gorton & Troup, 2018). An improved understanding of the chemistry of the host sedimentary units, including associated volcanic and intrusive rocks (potential metal source rocks) within these regions is therefore an important requisite to further understand the resource potential of the region. To assist in this we have undertaken a multi-year campaign (2016-2019) of regional geochemical sampling of geological units in the southeastern McArthur Basin, it’s continuation into the Tomkinson Province, and the Lawn Hill Platform regions of Northern Territory and northwest Queensland. Chief aims of the project were to characterise, as much as possible, the inorganic geochemistry of units of the Paleoproterozoic Tawallah, McArthur, Fickling and McNamara Groups and the Mesoproterozoic Roper and South Nicholson groups, with most emphasis on the Tawallah, McNamara and Fickling Groups. Minimal attention was paid to units of the McArthur Group which have been extensively previously sampled. The project also involved exploratory geochemical characterisation of sedimentary and igneous rocks from Paleoproterozoic and Mesoproterozoic rocks of the Tomkinson Province (Tomkinson, Namerinni and Renner groups) in Northern Territory. Minimal regional geochemical data exists for these rocks which are considered time equivalents of the Tawallah, McArthur, Nathan and Roper groups. The approach followed was based on targeting as many units as possible from drill core held within the core repository facilities of the Northern Territory and Queensland Geological surveys. Sampling strategy for individual units was based on targeting all lithological variability with particular emphasis on units not previously extensively sampled. Units were sampled at moderate to high resolution, with sampling density ranging from one sample per ~10 m intervals in organic rich intervals or lithological variable units, up to one sample per 20 to 50 m intervals in lithologically-monotonous units or in units recently sampled recently by GA or others. This data release contains the results of elemental analyses (XRF, ICP-MS), ferrous iron oxide content (FeO) and Loss-on-ignition (LOI) on 805 samples selected from 42 drill cores housed in the Geological Survey of Northern Territory’s Darwin and Alice Springs core repositories and in the Geological Survey of Queensland’s Brisbane and Mount Isa core repositories. Drillholes sampled include the Amoco holes DDH 83-1, DDH 83-2, DDH 83-3, DDH 83-4, and DDH 83-5, as well as 14MCDDH001, 14MCDDH002, 87CIIDH1, 87CIIDH2, Bradley 1, Broughton 1, DD81CY1, DD91RC18, DD91DC1, DD91HC1, DD95GC001, GCD-1, GCD-2A, GSQ Lawn Hill 3, GSQ Lawn Hill 4, GSQ Westmoreland 2, MWSD05, ND1, ND2, 12BC001, and Willieray (1DD, 3DD, 8DD), Hunter (1DD, 2DD, 3DD) and HSD001, HSD002 holes from the Tomkinson Province. The data also include a small number of non-basin samples (from drill holes AAI POTALLAH CREEK 1, ADRIA DOWNS 1, Bradley 1, GSQ Normanton 1, GSQ Rutland Plains 1, MULDDH001 and MURD013), collected at the same time, largely for isotopic studies. The resultant geochemical data was largely generated at the Inorganic Geochemistry Laboratory at Geoscience Australia (509 of the 805 analyses), with two batches (296 samples) analysed by Bureau Veritas in Perth. Eighteen samples analysed at GA were also reanalysed at Bureau Veritas for QA/QC purposes. All data was collected as part of the Exploring for the Future program. The report also includes a statistical treatment of the geochemical data looking at laboratory performance, based on certified reference material (CRMs) and sample duplicates, and interlaboratory agreement, based on samples analysed at both laboratories. Results show accuracies were within acceptable tolerances (±2 SD) for the majority of major and trace elements analysed at both laboratories. Notable exceptions included significant negative bias for Fe2O3 and positive bias for Na2O at Geoscience Australia. The results also showed that Mo (and As and Be) measurements were a consistent problem at GA, and Zn a consistent problem at BV. Precision (reproducibility) for major elements at both laboratories was very good, generally between 1 to 5%. Precisions for trace elements, varied from generally 5% or better at Geoscience Australia, and mostly between 5 and 10% for Bureau Veritas. Importantly, agreement between laboratories was good, with the majority of elements falling within ±5% agreement, and a few within 5-10% (Th, Tb, Sr, Zn, Ta, and Cr). Major exceptions to this included Na2O, K2O, Rb, Ba and Cs, as well as P2O5 and SO3, as well as those trace elements commonly present in low concentrations (e.g., Cu, As, Be, Mo, Sb, Ge, Bi). The mismatch between the alkalis is notable and of concern, with differences (based on median values) of 17% and 22% for K2O and Ba (higher at Bureau Veritas) and 32% and 300% for Ba and Na2O (higher at Geoscience Australia). The geochemical data presented here have formed the basis for ongoing studies into aspects of basin-hosted mineral systems in the McArthur–Mount Isa region, including insights into sources of metals for such deposits and delineating alteration haloes around those deposits (Champion et al., 2020a, b).

A regional hydrocarbon prospectivity study was undertaken in the onshore Canning Basin in Western Australia as part of the Exploring for the Future (EFTF) program, an Australian Government initiative dedicated to driving investment in resource exploration. As part of this program, significant work has been carried out to deliver new pre-competitive data including new seismic acquisition, drilling of a stratigraphic well, and the geochemical analysis of geological samples recovered from exploration wells. A regional, 872 km long 2D seismic line (18GA-KB1) acquired in 2018 by Geoscience Australia (GA) and the Geological Survey of Western Australia (GSWA), images the Kidson Sub-basin of the Canning Basin. In order to provide a test of geological interpretations made from the Kidson seismic survey, a deep stratigraphic well, Barnicarndy 1, was drilled in 2019 in a partnership between Geoscience Australia (GA) and the Geological Survey of Western Australia (GSWA) in the Barnicarndy Graben, 67 km west of Telfer, in the southwest Canning Basin. Drilling recovered about 2100 m of continuous core from 580 mRT to the driller’s total depth (TD) of 2680.53 mRT. An extensive analytical program was carried out to characterise the lithology, age and depositional environment of these sediments. This data release presents organic geochemical analyses undertaken on rock extracts obtained from cores selected from the Barnicarndy 1 well. The molecular and stable isotope data carbon and hydrogen will be used to understand the type of organic matter being preserved, the depositional facies and thermal maturity of the Lower Ordovician sedimentary rocks penetrated in this well. This information provides complementary information to other datasets including organic petrological and palynological studies.

Geoscience Australia, CSIRO, and the Australian Space Agency collaboratively developed a 2-page A4 flyer to promote education and careers in space to students and teachers. The flyer showcases Australia's unique capability in the space sector, far beyond astronomers and astronauts. It also lists QR codes of several Australian educational resources on a diversity of space topics for preschoolers through to university students. It is designed to be shared virtually or in person with stakeholders interested in promoting space science literacy and careers.

Australian Resource and Energy Infrastructure map is a national view of Australia's mineral resources and energy infrastructure, Base scale of 1:5,000,000.

This collection of documents detail various field techniques and processes that GA conduct. They are in conjunction with a series of Field Activity Technique Engagement Animations. The target audience are the communities that are impacted by our data acquisition activities. Field techniques in this collection include; • AEM fixed wing • AEM Helicopter • Borehole Geophysics • Goundwater sampling • Magnetotelluric (MT) surveys • Passive seismic surveys • Rapid Deployment Kits (RDKs) • Reflection seismic surveys • Surface Magnetic Resonance (SMR) surveys • Stratigraphic drilling

This animation shows how Airborne Electromagnetic Surveys Work. It is part of a series of Field Activity Technique Engagement Animations. The target audience are the communities that are impacted by our data acquisition activities. There is no sound or voice over. The 2D animations include a simplified view of what AEM equipment looks like, what the equipment measures and how the survey works.

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

Weathering intensity or the degree of weathering is an important characteristic of the earth’s surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith.