Geoscience Australia defines a sample as a feature observed, measured or collected in the field. A specimen is a physical individual sample collected during the field work. This data set represents a subset of all Sampling data held by Geoscience Australia that have been collected as part of drilling activities (ie relate to Australian Boreholes). The data will be utilised by other data domains by providing Sampling context to various Observation & Measurement data.

<div>To set out how Geoscience Australia is meeting its vision for the Exploring for the Future program, we have summarised the ways our scientific activities, outputs and intended outcomes and impacts are linked, using the Impact Pathway diagram. This updated brochure includes program impact infographics.</div>

<div>The Bushfire Boundaries - Historical Dataset (version 2) represents the aggregation of jurisdictional supplied burnt areas polygons that date from the early 1900's through to 2023 (excluding the Northern Territory). The burnt areas represent curated jurisdictional owned polygons of both bushfires and prescribed (planned) burns.</div><div><br></div><div>This dataset was produced under Work Stream 1C - Activity 3 of the National Bushfire Intelligence Capability; a collaborative partnership between the Australian Climate Services, CSIRO (NBIC), Geoscience Australia (GA), and the Emergency Management Spatial Information Network (EMSINA). Under agreement this Project (Activity 3) will release a nationally consistent, harmonised and standardised historical bushfire boundary dataset derived from the authoritative state and territory agencies in both 2023 (this dataset) and again in November 2024. </div><div><br></div><div>The information released within this dataset is reflective of the data supplied by participating authoritative agencies. It may, or may not, represent all fire history within that jurisdiction.</div><div><br></div>

<div>Disruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some CRM, the primary drivers of these supply chain disruptions are likely to be issues related to environmental, social, and governance (ESG) rather than geological scarcity. Herein we combine public geospatial data as mappable proxies for key ESG indicators (e.g., conservation, biodiversity, freshwater, energy, waste, land use, human development, health and safety, and governance) and a global dataset of news events to train and validate three models for predicting “conflict” events (e.g., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources conflicts, but we show that machine learning results are biased by proxies for population density and likely underestimate the potential for conflict in remote areas. Knowledge-driven methods are the least impacted by population bias and are used to calculate an ESG rating that is then applied to a global dataset of lithium occurrences as a case study. We demonstrate that giant lithium brine deposits (i.e., >10&nbsp;Mt Li2O) are restricted to regions with higher spatially situated risks relative to a subset of smaller pegmatite-hosted deposits that yield higher ESG ratings (i.e., lower risk). Our results reveal trade-offs between the sources of lithium, resource size, and spatially situated risks. We suggest that this type of geospatial ESG rating is broadly applicable to other CRM and that mapping spatially situated risks prior to mineral exploration has the potential to improve ESG outcomes and government policies that strengthen supply chains. <b>Citation:</b> Haynes M, Chudasama B, Goodenough K, Eerola T, Golev A, Zhang SE, Park J and Lèbre E (2024) Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium. <i>Earth Sci. Syst. Soc. </i>4:10109. doi: 10.3389/esss.2024.10109

An integrated analysis of geoscience information and benthos data has been used to identify benthic biotopes (seafloor habitats and associated communities) in the nearshore marine environment of the Vestfold Hills, East Antarctica. High-resolution bathymetry and backscatter data were collected over 42km2 to depths of 215 m using a multibeam sonar system. Epibenthic community data and in situ observations of seafloor morphology, substrate composition and bedforms were obtained from towed underwater video. Analysis of the datasets was used to identify statistically distinct benthic assemblages and describe the physical habitat characteristics related to each assemblage, with seven discrete biotopes identified. The biotopes include a range of habitat types including shallow coastal embayments and rocky outcrops which are dominated by dense macroalgae communities, and deep muddy basins which are dominated by mixed invertebrate communities. Transition zones comprising steep slopes provide habitat for sessile invertebrate communities. Areas of flat sandy plains are relatively barren. The relationship between benthic community composition and environmental parameters is complex with many variables (e.g. depth, substrate type, longitude, latitude and slope) contributing to differences in community composition. Depth and substrate type were identified as the main drivers of benthic community composition, however, depth is likely a proxy for other unmeasured depth-dependent parameters such as light availability, frequency of disturbance by ice, currents and/or food availability. Sea ice cover is also an important driver and the benthic community in areas of extended sea ice cover is comprised of sessile invertebrates and devoid of macroalgae. This is the first study that has used an integrated sampling approach based on multibeam sonar and towed underwater video to investigate benthic assemblages across a range of habitats in a nearshore marine environment in East Antarctica. This study demonstrates the efficacy of using multibeam sonar and towed video systems to survey large areas of the seafloor and to collect non-destructive high-resolution data in the sensitive Antarctic marine environment. The multibeam data provide a physical framework for understanding benthic habitats and the distribution of benthic communities. This research provides a baseline for assessing natural variability and human induced change on nearshore marine benthic communities (Australian Antarctic Science Project AAS-2201), contributes to Geoscience Australia's Marine Environmental Baseline Program, and supports Australian Government objectives to manage and protect the Antarctic marine environment.

As part of the controlled release experiments at the Ginninderra test site, geophysical surveys have been acquired using electromagnetic techniques at a range of frequencies. The primary objective was to assess whether these could provide insight into the soil structure at the site, give guidance as to where to monitor for leakage, and provide additional information that may explain the observed sub-surface and surface CO2 migration behavior. A secondary objective was to assess whether CO2 leaks could be located based on secondary impacts such as drying of the soil profile. Ground penetrating radar surveys were taken during the second release experiment (October - December 2012). Different frequency shielded antennas were trialled in order to optimize the signal. Two surveys were conducted: one baseline survey prior to CO2 release and another during the release experiment. The GPR results show a reduction in range and clear reflections to the west indicating that clay was present. To the east we see clearer reflections from sand layers and the water table. These observations corresponded with larger scale sub-surface soil features determined from EM31 and EM38 electromagnetic surveys. Application of these geophysical surveys for CO2 leak detection and monitoring design are discussed. Paper for CO2CRC Research Symposium 2013

As a participating organisation in the Global Mapping Project, and following discussions held at the 22nd meeting of the International Steering Committee for Global Mapping (ISCGM), the Secretariat of the ISCGM has requested the assistance of Geoscience Australia in the validation of intermediate products of global land cover, the Global Land Cover by National Mapping Organisation (GLCNMO) version 3. The request sent to Geoscience Australia involves the use of existing maps and other materials, based on expertise and knowledge to report the validation of the GLCNMO version 3 datasets.

<p>This resource contains multibeam bathymetry data for Bynoe Harbour collected by Geoscience Australia (GA), the Australian Institute of Marine Science (AIMS) and the Northern Territory Government (Department of Environment and Natural Resources) during the period between 3 and 27 May 2016 on the RV Solander (survey SOL6432/GA04452). This project was made possible through offset funds provided by INPEX-led Ichthys LNG Project to Northern Territory Government Department of Environment and Natural Resources, and co-investment from Geoscience Australia and Australian Institute of Marine Science. The intent of this four year (2014-2018) program is to improve knowledge of the marine environments in the Darwin and Bynoe Harbour regions by collating and collecting baseline data that enable the creation of thematic habitat maps that underpin marine resource management decisions. <p>The specific objectives of the survey were to: <p>1. Obtain high resolution geophysical (bathymetry) data for Bynoe Harbour; <p>2. Characterise substrates (acoustic backscatter properties, grainsize, sediment chemistry) for Bynoe Harbour; and <p>3. Collect tidal data for the survey area. Data acquired during the survey included: multibeam sonar bathymetry and acoustic backscatter; physical samples of seabed sediments, underwater photography and video of grab sample locations and oceanographic information including tidal data and sound velocity profiles. <p>This dataset comprises multibeam bathymetry data. A detailed account of the survey is provided in: Siwabessy, P.J.W., Smit, N., Atkinson, I., Dando, N., Harries, S., Howard, F.J.F., Li, J., Nicholas W.A., Picard, K., Radke, L.C., Tran, M., Williams, D. and Whiteway, T. 2016. Bynoe Harbour Marine Survey 2016: GA4452/SOL6432 – Post-survey report. Record 2017/04. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/Record.2017.004.

This resource contains bathymetry and backscatter data for the Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea collected by Geoscience Australia during September and October 2012 on RV Solander (survey GA0339/SOL5650). The survey used a Kongsberg EM3002 300 kHz multibeam sonar system mounted in single head configuration to map four areas, covering a combined area of 507 square kilometres. Data are gridded to 2 m spatial resolution. The Oceanic Shoals Commonwealth Marine Reserve survey was undertaken as an activity within the Australian Government's National Environmental Research Program Marine Biodiversity Hub and was the key component of Research Theme 4 - Regional Biodiversity Discovery to Support Marine Bioregional Plans. Hub partners involved in the survey included the Australian Institute of Marine Science, Geoscience Australia, the University of Western Australia, Museum Victoria and the Museum and Art Gallery of the Northern Territory. Data acquired during the survey included: multibeam sonar bathymetry and acoustic backscatter; sub-bottom acoustic profiles; physical samples of seabed sediments, infauna and epibenthic biota; towed underwater video and still camera observations of seabed habitats; baited video observations of demersal and pelagic fish, and; oceanographic measurements of the water column from CTD (conductivity, temperature, depth) casts and from deployment of sea surface drifters. Further information on the survey is available in the post-survey report published as Geoscience Australia Record 2013/38 (Nichol et al. 2013).

Geoscience Australia undertook a marine survey of the Vlaming Sub-basin in March and April 2012 to provide seabed and shallow geological information to support an assessment of the CO2 storage potential of this sedimentary basin. The survey was undertaken under the Australian Government's National CO2 Infrastructure Plan (NCIP) to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. The Vlaming Sub-basin is located offshore from Perth, Western Australia, and was previously identified by the Carbon Storage Taskforce (2009) as potentially highly suitable for CO2 storage. The principal aim of the Vlaming Sub-basin marine survey (GA survey number GA334) was to look for evidence of any past or current gas or fluid seepage at the seabed, and to determine whether these features are related to structures (e.g. faults) in the Vlaming Sub-basin that may extend up to the seabed. The survey also mapped seabed habitats and biota in the areas of interest to provide information on communities and biophysical features that may be associated with seepage. This research addresses key questions on the potential for containment of CO2 in the Early Cretaceous Gage Sandstone (the basin's proposed CO2 storage unit) and the regional integrity of the South Perth Shale (the seal unit that overlies the Gage Sandstone). This dataset comprises high resolution backscatter grids.