The Surface Hydrology Points (Regional) dataset provides a set of related features classes to be used as the basis of the production of consistent hydrological information. This dataset contains a geometric representation of major hydrographic point elements - both natural and artificial. This dataset is the best available data supplied by Jurisdictions and aggregated by Geoscience Australia it is intended for defining hydrological features.

Generalised Data Framework (GDF) The Generalised Data Framework is a High Performance Data (HPD) research project conducted within the Geoinformatics and Data Services Section of GA.

Satellite Earth observation data presents unique opportunities for society to respond to major challenges like climate change, food security and sustainable development. But significant technical challenges, including to enable different data streams to be integrated and the sheer volume of the data, are preventing that full value from being realised. The explosion in free, highresolution, global data from next-generation satellites, linked with the potential of new highperformance ICT infrastructure and architectures, positions us to meet this challenge. As the 2016 CEOS Chair, and as a sophisticated user of multiple EO satellite data streams, Australia is proposing that CEOS explore how these new technologies can ensure CEOS agency satellite data can be 'unlocked and put to work'.

Tholeiitic intrusion-hosted nickel sulphide deposits are highly sort exploration targets due to their potential size and co-products platinum-group elements and copper. The Norilsk-Talnakh (Russia), Voisey's Bay (Canada) and Jinchuan (China) deposits are world class examples. Although Australia holds the largest economic resources of nickel in the world, its nickel resources are mainly sourced from komatiitic-hosted and lateritic deposits. Known resources of tholeiitic intrusion-hosted nickel sulphides are relatively small, with Nebo-Babel and Nova-Bollinger in Western Australia the most significant examples. Given the abundance of tholeiitic igneous rocks in Australia, this important deposit type seems to be under-represented when compared to other continents with similar geology. To support the discovery of world class nickel sulphide deposits in Australia, Geoscience Australia has recently undertaken a continental-scale GIS-based prospectivity analysis for tholeiitic intrusion-hosted deposits across Australia. This analysis exploits a suite of new relevant digital datasets recently released by Geoscience Australia. For example, the analysis utilises the Australian Mafic-Ultramafic Magmatic Events GIS Dataset which places mafic and ultramafic rocks across Australia into 74 coeval magmatic events based on geochronological data. Whole rock geochemistry of mafic and ultramafic rocks has been used to differentiate between magma series and discriminate between different magmatic events and units within those events. Other new datasets include crustal domain boundaries derived from both deep crustal seismic data and neodymium depleted mantle model age data as well as a coverage of the minimum thickness of mafic rocks in the crust derived from the Australian Seismogenic Reference Earth Model. This continental-scale GIS-based nickel sulphide prospectivity analysis uses a mineral systems approach to map the four essential components of ore-forming mineral systems; (1) sources of ore constituents, (2) crustal and mantle lithospheric architecture, (3) energy sources or drivers of the ore-forming system, and (4) gradients in ore depositional physico-chemical parameters. These four components are combined into a prospectivity map using weights-of-evidence GIS-based techniques, with the most prospective areas across the continent occurring where all components are present. The mineral systems approach allows for the identification of a much larger footprint than the deposit itself, and can be applied to greenfield and/or undercover areas. The results highlight areas that contain known tholeiitic intrusion-hosted nickel sulphide deposits, such as the Musgrave and Pilbara Provinces, as well as regions that do not contain any known deposits, such as the southern margin of the Arunta Province in the Northern Territory, the Mount Isa Province in Queensland and the Paterson Province in Western Australia.

The `Inferred Isotopic Domain Boundaries of Australia data set is based on an interpretation of the recently released Neodymium depleted mantle model age map of Australia (GA Record 2013/44). The isotopic map of Australia was produced by gridding two-stage depleted mantle model ages calculated from Sm-Nd isotopic data for just over 1490 samples of felsic igneous rocks throughout Australia. The resultant isotopic map serves as a proxy for bulk crustal ages and accordingly allows the potential recognition of geological domains with differing geological histories. One of the major aims of the Neodymium depleted mantle model age map, therefore, was to use the isotopic map (and associated data) to aid in the recognition and definition of crustal blocks (geological terranes) at the continental and regional scale. Such boundaries are recognisable by regional changes in isotopic signature but are hindered by the variable and often low density of isotopic data points. Accordingly two major procedures have been adopted to locate the regional distribution of such boundaries across the geological continent. In areas of high data density (and high confidence), such as the Yilgarn Craton Western Australia, isotopic data alone was used to delineate crustal domains. In such regions it is evident that identified crustal blocks often but not universally approximate known geological terranes. In areas of moderate data density (and corresponding moderate confidence) (smoothed) boundaries of known geological provinces were used as a proxy for the isotopic boundary. For both high and moderate data densities identified crustal boundaries were extended (with corresponding less confidence) into regions of lower data density. In areas of low data density (and low confidence) boundaries were either based on other geological and/or geophysical data sets or were not attempted. The latter was particularly the case for regions covered by thick sedimentary successions. Two levels of confidence have been documented, namely the level of confidence in the location of the isotopic domain boundary, and the level of confidence that a boundary may actually exist. The `Inferred Isotopic Domain Boundaries of Australia map shows the locations of inferred boundaries of isotopic domains, which are assumed to represent the crustal blocks that comprise the Australia continent. The map therefore provides constraints on the three dimensional architecture of Australia, and allows a better understanding of how the Australian continent was constructed from the Mesoarchean through to the Phanerozoic. It is best viewed as a dynamic dataset, which will need to be refined and updated as new information, such as new isotopic data, becomes available.

Geological Survey of South Australia SAREIC Technical Day conference 2015

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy, can be inferred based on underwater video footage at limited locations. It can also be predicted to two classes. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e. hard90 and hard70) for seabed video footage by. We developed optimal predictive models to predict the spatial distribution of seabed hardness using random forest (RF) based on point data of hardness classes and spatially continuous multibeam backscatter data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), the combined, Boruta, and RRF were tested. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were also examined. Finally, the most accurate models were used to predict the spatial distribution of the hardness classes and the predictions were visually examined and compared with the predictions based on two-class hardness classification. This study confirms that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness can be predicted into a spatially continuous layer with a high degree of accuracy; 3) the typical approach used to pre-select predictors by excluding highly correlated predictors needs to be re-examined when using machine learning methods, at least, for RF, in the environmental sciences; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving the predictive models; 5) FS is essential for identifying an optimal RF predictive model and the RF methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data, can be applied to `small p and large n problems in environmental sciences, and is recommended for future studies. In addition, automated computational programs for AVI need be developed to improve its computational efficiency and caution should be taken when applying filter FS method in selecting predictive models in future studies.

Conservation planning requires spatial information on biodiversity within a region of interest and its patterns of association with physical environmental features. Such information, however, is often unavailable, and spatial planning is reliant upon proxies based on assumed relationships between species and environmental features have been used as the basis of spatial planning. Here we evaluate the effectiveness of a set of key ecological features (KEF) used in the design of Australia's network of Commonwealth Marine Reserves for representing key marine macrobenthos in a large and biodiverse but data-sparse region in the Oceanic Shoals Commonwealth Marine Reserve (CMR), Timor Sea. Predictive spatial models of the distributions of four key habitat-forming macrobenthic taxa including hard corals, soft corals, gorgonians and sponges, were built using 10 geophysical variables and Boosted Regression Trees. We identified the extent to which KEFs captured the distributions of each taxon, and whether models derived from the western region of the CMR could predict well the distribution of the same taxon in the eastern CMR. All four taxa showed similar habitat preferences, occurring on the tops of raised geomorphic features with hard substrata and while absent from deeper habitats with soft substrata. However, high variability in the biodiversity observed among similar features indicated that factors other than geomorphology alone influence spatial patterns in the distribution of macrobenthos in the region. Overall, models derived from the western region performed reasonably well predicting distribution patterns in eastern region. Transferability of models among sites increased with greater model precision accuracy (higher deviance explained), and all models predicted taxon absence better than presence.

Offshore seismic surveys have long been considered to be disruptive to fisheries, and recent claims have been made that marine seismic operations cause scallop declines in southeastern Australia. Despite the importance of this issue to both fisheries and petroleum industries, few studies target commercially important species in realistic exposure scenarios. One of the main challenges in underwater sound impact studies is the meaningful translation of laboratory results to the field, largely due to variations due to underwater sound properties and experimental set-ups. In the current study, we use in situ experiments to investigate whether field populations of the commercial (Pectens fumatus ) and doughboy scallops (Mimachlamys asperrima) are negatively affected by a seismic survey undertaken in the Gippsland Basin in April 2015 (30 80 m depth). Both theoretical and field-based noise propagation models were developed to quantify noise exposure of the animals at the seabed and to establish whether sound monitoring markedly improves model outputs. Images of the seafloor at were acquired using an Automated Underwater Vehicle (AUV) to evaluate the novel use of AUVs in scallop stock assessment. Samples were collected before and after the seismic survey using a commercial scallop dredge, and a range of metrics are currently being quantified. In this presentation, we describe preliminary results and critically review our current understanding of low-frequency sound impact on marine molluscs.

A video created for the Australia Minerals booth at the China Mining 2015conference. The video has key information translated into Mandarin.