Governments at the Commonwealth, State, Territory and Local level are committed to minimising the impact of natural disasters through a variety of Disaster Risk Reduction (DRR) programs. Risk analysis is one of the processes undertaken to inform DRR decision making and policy development. It involves estimating the extent and severity of one or more natural hazards, understanding the location and characteristics of the 'elements at risk' from those hazards (also known as exposure) and modelling the vulnerability and response of those elements exposed to the subject hazards. Understanding the vulnerability of buildings is crucial in risk analysis activities, as damage to buildings can have significant direct and indirect impacts on individuals, communities, economies and the functioning of society at large. The development of quality spatially-enabled information is a key activity in the risk analysis process. After demonstrating a proof of concept in 2005-2006, Geoscience Australia has led the development of exposure information for Australia via the National Exposure Information System (NEXIS). Within NEXIS, currently available spatial and non-spatial data from various sources is routinely combined, reorganised for consistency, managed and supplied to stakeholders. The products derived from NEXIS enable risk analysis specialists and policy makers to access recent exposure information they require to analyse and assess the risk posed by the hazards in Australia. At the core of NEXIS is information about buildings. There are many challenges to developing and providing reliable information about buildings across the country. Through an offer of assistance from the ACT Government, Geoscience Australia has developed an innovative and rapid method to analyse and interpret cadastral data to estimate an important exposure attribute. This presentation describes the development of the method, the resulting benefits for exposure information in the ACT and outlines how cadastral data can improve DRR outcomes across Australia.

Optical, Geospatial, Radar, and Elevation Supplies and Services Panel (OGRE) 2011/12 Annual Report

The Australian Geoscience Data Cube has won the 2016 Content Platform of the Year category at the Geospatial World Leadership Awards. The awards recognise significant contributions made by champions of change within the global geospatial industry and were presented during the 2017 Geospatial World Forum held in Hyderabad, India. The Data Cube was developed by Geoscience Australia in partnership with the CSIRO and the National Computational Infrastructure at the Australian National University, and is a world-leading data analysis system for satellite and other Earth observation data. Visit www.datacube.org.au to find out more including the technical specifications, and learn how you can develop your own Data Cube and become part of the collective.

Australia wide mineralogical maps have recently been generated and released by CSIRO and Geoscience Australia using the 14 band satellite-borne ASTER imaging sensors. Seventeen map products related to surface composition have been produced for the geoscience community. Band parameters were developed based on spectral absorption features representing either abundance of mineral groups, specific minerals and their chemistry, vegetation cover or regolith related characteristics. A detailed study was undertaken, investigating the geoscience exploration capabilities of these newly released map products, individually, and integrated with airborne geophysics and digital elevation models over the Mt Fitton test site in northern Flinders Ranges, South Australia. This site includes the Mt Fitton talc deposits, gold prospects, and areas of hydrothermal activity and metasomatism.

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments, and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets, and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyze and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration, and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc related Cu-Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. <b>Citation:</b> E. C. Grunsky, P. de Caritat; State-of-the-art analysis of geochemical data for mineral exploration. <i>Geochemistry: Exploration, Environment, Analysis</i> 2019; 20 (2): 217–232. doi: https://doi.org/10.1144/geochem2019-031 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

As the Central Bureau for the Asia Pacific Reference Frame (APREF), Geoscience Australia were keen to transition to the most up-to-date realisation of a trusted global reference frame from the IGS, being IGS20. However, following adoption of ITRF2020/IGS20 there were apparent site-specific, centimetre-level coordinate inconsistencies between ITRF2020/IGS20 and ITRF2014/IGb14, concerningly presenting as an inconsistent height offset across the APREF network. The Asia-Pacific Reference Frame (APREF) is a network consisting of more than 1000 stations across the Asia-Pacific region, including ~700 Australian stations as well as global IGS core stations. For our routine analysis of the network, we process GPS-only double-difference observations in network mode and align them to the global reference frame of choice, using the Bernese software. We process daily solutions, and then stack them to generate weekly solutions. We then take these coordinates and apply the Australian Plate Motion Model to acquire GDA2020 coordinates, which is a plate-fixed national datum used widely across Australia, including to calculate the legally traceable coordinates we provide to station owners and operators. Taking the latest available products (satellite clock and orbit files, and antenna models) from the IGS, APREF (weekly) solutions are aligned to the IGS20 reference system (where IGS20 is the IGS realisation of ITRF2020), however, we found station-specific offsets of our APREF solutions between the ITRF2014/IGb14 and ITRF2020/IG2S0 due to updates in the ground antenna calibration values from igs14.atx to igs20.atx that reached up to 3 cm. Some of the antenna calibration values were updated post-release of ITRF2020 (updates between igsR3.atx and igs20.atx), which results in further inconsistencies at the coordinate level between the APREF solutions and ITRF2020 solutions. This presentation will discuss the challenges faced when implementing a new global frame of reference at the regional level and the impact on downstream users. We commend the IGS for their efforts in providing high quality, openly available products and services and would like to prompt conversation about the consideration of user requirements for the development of downstream products (such as regional reference frames). Abstract to be submitted to/presented at the American Geophysical Union (AGU) Fall Meeting 2023 (AGU23) - https://www.agu.org/fall-meeting

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments, and regolith in general, are usually carried out by governments and mineral exploration companies at continental (0.5 – 50 million km2), regional (500 – 500,000 km2) and local (0.5 – 500 km2) scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. A geochemical database, with 50 or more elements determined to sufficiently low detection limits, represents a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data comprised of many thousands of samples requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. When assembling large datasets from various sources, care must be taken to understand the nature of the sample media, the methods of sample collection and preparation, the laboratory digestion procedures and the analytical instrumentation methods. Problems that are typically associated with the interpretation of multi-element geochemical data include closure, missing values, censoring, merging, levelling different datasets, and adequate spatial sample design. Of particular significance is the effect of stoichiometry within the logratio framework that has been developed to deal with compositional data. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyze and interpret the information inherent to geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis after compositionally appropriate data imputation and transformation. Classification and predictive procedures, at the continental, regional and camp scales, can be used to confirm lithological variability, hydrothermal alteration, and mineralization. Studies of multi-element geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc related Cu-Au mineralisation from a regional survey in the southern Thomson Orogen of northern New South Wales and southern Queensland to the continental scale, reveals a number of potential regions with similar or even higher mineral potential throughout Australia. Abstract presented at Exploration ’17 Sixth Decennial International Conference on Mineral Exploration (https://www.mining.com/web/exploration-17-sixth-decennial-internatioal-conference-mineral-exploration/

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