This dataset contains all multibeam bathymetry data held by Geoscience Australia (GA) dating back to survey obtained since 1993. <b>Value: </b>Bathymetry data is used for a wide range of marine applications including: navigation, environmental assessment, jurisdictional boundaries, resource exploration. <b>Scope: </b>Data holdings lying within the offshore area of Australia, including international waters. <b> To access the AusSeaBed Marine Data Portal </b> use the following link: <a href="https://portal.ga.gov.au/persona/marine#/">https://portal.ga.gov.au/persona/marine#/</a>

This collection supports the compilation of national mineral resource and production statistics, and mineral prospectivity analysis. The collection includes the location of Australian mineral occurrences and mineral deposit descriptions, with geological, resource and production data. This information is stored in two Geoscience Australia databases, the Mineral Deposits & Occurrences Database (OZMIN) and the Mineral Occurrence Locations (MINLOC) database. The collection also includes a number of supporting Geographic Information System (GIS) datasets (e.g., mineral prospectivity datasets, ports, power stations); maps and reports. <b>Value:</b> Data related to the known location and production of mineral resources supports decisions related to resource and economic development. <b>Scope: </b>The collection covers the Australian continent and is updated annually. It now contains data on over one thousand major and historically significant mineral deposits for 60 mineral commodities (including coal).

The national Tropical Cyclone Hazard Assessment (TCHA) defines the severe wind hazard posed to Australia based on the frequency and intensity of tropical cyclones making landfall around the Australian coastline. Contact us at hazards@ga.gov.au if you need further information. URL: https://www.ga.gov.au/about/projects/safety/tcha <b>Value: </b>The TCHA provides vital information to emergency managers, town planners and infrastructure owners to plan and reduce the threat of tropical cyclone hazard on the Australian coast, and for the insurance industry to understand the tropical cyclone risk as an input to pricing insurance premiums. The TCHA is a key data source to calculate local cyclone impact models for the development of evidence-based disaster management plans, evacuation plans or inform infrastructure planning or mitigation strategies. High risk areas can be identified and prioritised for further analysis, or to extract scenarios to explore risk mitigation and community safety at a local and regional level. The TCHA includes a catalogue of synthetic tropical cyclone events (including tracks and wind fields), hazard profiles for selected locations across Australia, and maps of annual recurrence interval (ARI) wind speeds due to tropical cyclones. Geoscience Australia provides essential evidence based information to government and emergency managers around Australia to improve our communities' ability to prepare for, mitigate against and respond to natural disasters. <b>Scope: </b>Continental scale.

The Australian National Exposure Information System (NEXIS) collates the best publicly-available information, statistics, spatial and survey data into comprehensive and nationally-consistent exposure information datasets. Where data is limited, models are used to apply statistics based on similar areas. Exposure Information products are created at the national, state or local level to understand the elements at risk during an event or as a key input for analysis in risk assessments. <b>Value: </b>NEXIS products are not intended for operational purposes at the building or individual feature level. Its strength is to provide consistent aggregated exposure information for individual event footprints or at standard community, local, state and national geographies such as the Australian Bureau of Statistics (ABS) Statistical Areas (SA) or Local Government Areas (LGA). <b>Scope: </b>National detailed exposure information of the number of people, dwellings, other buildings and structures, businesses, agricultural and environmental assets. Further information can be found at the following URL: https://www.ga.gov.au/scientific-topics/community-safety/risk-and-impact/nexis

This collection includes Global Navigation Satellite System (GNSS) observations from long-term continuous or semi continuous reference stations at multiple locations across Australia and its external territories, including the Australian Antarctic Territory. <b>Value:</b> The datasets within this collection are provided on an openly accessible basis to support a myriad of scientific and societal positioning applications in Australia. These include the development and maintenance of the Australian Geospatial Reference System (AGRS); the densification of the International Terrestrial Reference Frame (ITRF); crustal deformation studies; atmospheric studies; and the delivery of precise positioning services to Australian businesses. <b>Scope: </b> Data from reference stations across Australia and its external territories, including the Australian Antarctica Territory. <b>Access: </b> To access the datasets and query station information visit the <a href="https://gnss.ga.gov.au./">Global Navigation Satellite System Data Centre</a>

The national standard lexicon of geologic units, including: age, lithology, geologic relationships for all Australian geological units, and a record of their use in literature. Links to Geological Provinces and Geological Maps. The collection is maintained by Geoscience Australia on behalf of the Australian Stratigraphy Commission, a standing committee of the Geological Society of Australia. <b>Value: </b>The lexicon standardises terminology for geologic units, thereby enabling integration of different geologic studies and datasets. <b>Scope: </b>Covers all Australian Territories, including Australia's Antarctic Territories. The database contains over 17,500 current stratigraphic names and over 36,000 variations, most of which are superseded, obsolete, or misspelt versions of the current names. The publicly accessible portion of this collection is made available through the Australian Stratigraphic Units Database (ASUD), the national authority on stratigraphic names in Australia and can be accessed here: <a href="https://pid.geoscience.gov.au/dataset/ga/21884">https://pid.geoscience.gov.au/dataset/ga/21884</a>

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.

Comprises a national satellite imagery mosaic and derived information products produced by a collaboration of CSIRO, Geoscience Australia (GA) and State and Territory Surveys, and several additional national and international collaborators. Mineral products were derived using a validated mosaic of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. <b>Value: </b>The data are used to understand distributions of and changes in surface materials and assessment of environmental, agricultural and resource potential. <b>Scope: </b>This dataset covers the continent with the intent to provide the best quality mosaic from 10+ year archive of scenes across Australia (i.e., lowest cloud/vegetation cover, high sun angle etc)

This data collection is comprised of radiometric (gamma-ray spectrometric) surveys acquired across Australia by Commonwealth, State and Northern Territory governments and the private sector with project management and quality control undertaken by Geoscience Australia. The radiometric method measures naturally occurring radioactivity arising from gamma-rays. In particular, the method is able to identify the presence of the radioactive isotopes potassium (K), uranium (U) and thorium (Th). The measured radioactivity is then converted into concentrations of the radioelements K, U and Th in the ground. Radiometric surveys have a limited ability to see into the subsurface with the measured radioactivity originating from top few centimetres of the ground. These surveys are primarily used as a geological mapping tool as changes in rock and soil type are often accompanied by changes in the concentrations of the radioactive isotopes of K, U and Th. The method is also capable of directly detecting mineral deposits. For example, K alteration can be detected using the radiometric method and is often associated with hydrothermal ore deposits. Similarly, the method is also used for U and Th exploration, heat flow studies, and environmental mapping purposes such as characterising surface drainage features. The instrument used in radiometric surveys is a gamma-ray spectrometer. This instrument measures the number of radioactive emissions (measured in counts per second) and their energies (measured in electron volts (eV)). Radiometric data are simultaneously acquired with magnetic data during airborne surveys and are a non-invasive method for investigating near-surface geology and regolith.

Analysis Ready Data (ARD) takes medium resolution satellite imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive ARD package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. ARD consists of sub products, including : 1) NBAR Surface Reflectance which produces standardised optical surface reflectance data using robust physical models which correct for variations and inconsistencies in image radiance values. Corrections are performed using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). 2) NBART Surface Reflectance which performs the same function as NBAR Surface Reflectance, but also applies terrain illumination correction. 3) OA Observation Attributes product which provides accurate and reliable contextual information about the data. This 'data provenance' provides a chain of information which allows the data to be replicated or utilised by derivative applications. It takes a number of different forms, including satellite, solar and surface geometry and classification attribution labels. ARD enables generation of Derivative Data and information products that represent biophysical parameters, either summarised as statistics, or as observations, which underpin an understanding of environmental dynamics. The development of derivative products to monitor land, inland waterways and coastal features, such as: - urban growth - coastal habitats - mining activities - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping) - water extent Derivative products include: - Water Observations from Space (WOfS) - National Intertidal Digital Elevation Model (NIDEM) - Fractional Cover (FC) - Geomedian ARD and Derivative products are reproduced through a period collection upgrade process for each sensor platform. This process applied improvements to the algorithms and techniques and benefits from improvements applied to the baseline data that feeds into the ARD production processes. <b>Value: </b>These data are used to understand distributions of and changes in surface character, environmental systems, land use. <b>Scope: </b>Australian mainland and some part of adjacent nations. Access data via the DEA web page - <a href="https://www.dea.ga.gov.au/products/baseline-data">https://www.dea.ga.gov.au/products/baseline-data</a>