The Geoscience Australia Rock Properties database stores the result measurements of scalar and vector petrophysical properties of rock and regolith specimens and hydrogeological data. Oracle database and Open Geospatial Consortium (OGC) web services. Links to Samples, Field Sites, Boreholes. <b>Value:</b> Essential for relating geophysical measurements to geology and hydrogeology and thereby constraining geological, geophysical and groundwater models of the Earth <b>Scope:</b> Data are sourced from all states and territories of Australia

Geometric representations of major surface water features of Australia, such as rivers, lakes, reservoirs, dams, canals and catchments. Also includes hydrologic features such as catchment boundaries and drainage basins. <b>Value:</b> This data is not authoritative, but represent a valuable resource for visualisation, decision support and planning activities. <b>Scope:</b> This is a National dataset at resolution relevant for presentation of regional spatial data such as digital maps.

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).

This collection includes Global Navigation Satellite System (GNSS) observations from short-term occupations at multiple locations across Australia and its external territories, including the Australian Antarctic Territory. <b>Value: </b> The datasets within this collection are available to support a myriad of scientific applications, including research into the crustal deformation of the Australian continent. <b>Scope: </b> Data from selected areas of interest across Australia and its external territories, including the Australian Antarctic Territory. Over time there has been a focus on areas with increased risk of seismic activity or areas with observed natural or anthropogenic deformation. <b>Access: </b> The datasets within this collection are currently stored offline, to access please send a request to gnss@ga.gov.au

Geoscience Australia (GA) has acquired Landsat satellite image data over Australia since 1979, from instruments including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). This data represents raw telemetry which has either been received directly at Geoscience Australia's (GAs) receiving stations (Alice Springs or - formerly - Hobart), or downloaded from the United States Geological Survey Organisation. The data is maintained in raw telemetry format as a baseline to downstream processes. While this data has been used extensively for numerous land and coastal mapping studies, its utility for accurate monitoring of environmental resources has been limited by the processing methods that have been traditionally used to correct for inherent geometric and radiometric distortions in EO imagery. To improve access to Australia's archive of Landsat TM/ETM+/OLI data, several collaborative projects have been undertaken in conjunction with industry, government and academic partners. These projects have enabled implementation of a more integrated approach to image data correction that incorporates normalising models to account for atmospheric effects, BRDF (Bi-directional Reflectance Distribution Function) and topographic shading (Li et al., 2012). The approach has been applied to Landsat TM/ETM+ and OLI imagery to create the surface reflectance products. <b>Value: </b>The Landsat Raw Data Archive is processed and further calibrated to input to development of information products toward an improved understanding of the distribution and status of environmental phenomena. <b>Scope: </b>Data is provided via the US Geological Survey's (USGS) Landsat program, following downlink and recording of the data at Alice Springs Antenna (operated by Geoscience Australia) or downloaded directly from USGS EROS

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

The Topographic Position Index measures the topographic slope position of landforms by comparing the mean elevation of a specific neighbourhood area with the elevation value of a central cell. This is done for every cell or pixel in the digital elevation model (DEM) to derive the relative topographic position (e.g. upper, middle and lower landscape elements). Ruggedness informs on the roughness of the surface and is calculated as the standard deviation of elevations. Both these terrain components are used to generate a multi-scale topographic index over the Australian continent using the algorithm developed by Lindsay, J, B., Cockburn, J. M. H. and Russell, H. A. J., 2015. An integral image approach to performing multi-scale topographic position analysis, Geomorphology, 245, 51-61. Topographic position is captured across three spatial scale and display as a ternary image. The ternary image reveals a rich representation of nested landform features with broad application to geomorphological and hydrological process understanding and mapping of regolith and soils. <b>Value: </b>Broad application in understanding geomorphological and hydrological processes and in mapping regolith and soils over the Australian continent. Can be used as inputs into geospatial modelling and machine learning <b>Scope: </b>The dataset is national. The algorithm can be run on any digital elevation gridded dataset.

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.

This data collection are comprised of magnetic 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. Magnetic surveying is a geophysical method for measuring the intensity (or strength) of the Earth's magnetic field, which includes the fields associated with the Earth's core and the magnetism of rocks in the Earth's crust. Measuring the magnetism of rocks, in particular, provides a means for the direct detection of several different types of mineral deposits and for geological mapping. The magnetism of rocks depends on the volume, orientation and distribution of their constituent magnetic minerals (namely magnetite, monoclinic pyrrhotite, maghaemite and ilmenite). The instrument used in magnetic surveys is a magnetometer, which can measure the intensity of the magnetic field in nanoteslas (nT). Magnetic surveys in this collection have been acquired using aircraft or ship-mounted magnetometers and are a non-invasive method for investigating subsurface geology.

This collection includes calibrated time-series data and other products from Geoscience Australia's geomagnetic observatory network in Australia and Antarctica. Data dates back to 1924. <b>Value: </b>These data are used in mathematical models of the geomagnetic field, in resource exploration and exploitation, to monitor space weather, and for scientific research. The resulting information can be used for compass-based navigation, magnetic direction finding, and to help protect communities by mitigating the potential hazards generated by magnetic storms. <b>Scope: </b>Continuous geomagnetic time series data, indices of magnetic activity and associated metadata, Data dates back to 1924.