This preliminary report will provide a geochemical and ionic characterisation of groundwater, to determine baseline conditions and, if possible, to distinguish between different aquifers in the Laura basin. The groundwater quality data will be compared against the water quality guidelines for aquatic ecosystem protection, drinking water use, primary industries, use by industry, recreation and aesthetics, and cultural and spiritual values to assess the environmental values of groundwater and the treatment that may be required prior to reuse or discharge.

Regolith carbonate or secondary carbonate is a key component of the regolith, particularly in many Mediterranean, arid and semi-arid regions of Australia. National maps of regolith carbonate distribution have been compiled from regional soil, regolith and geological mapping with varying degrees of confidence and consistency. Here we apply a decision tree approach based on a piecewise linear regression model to estimate and map the near-surface regolith carbonate concentration at the continental scale. The model is based on relationships established from the 1311 field sites of the National Geochemical Survey of Australia (NGSA) and 49 national environmental covariate datasets. Regolith carbonate concentration (weight %) was averaged from the <2 mm grain size-fractions of samples taken from two depth ranges (0-10 cm and ~60-80 cm) at each NGSA site. The final model is based on the average of 20 runs generated by randomly selecting 90% training and 10% validation splits of the input data. Results present an average coefficient of determination (R2) of 0.56 on the validation dataset. The covariates used in the prediction are consistent with our understanding of the controls on the sources (inputs), preservation and distribution of regolith carbonate within the Australian landscape. The model produces a continuous, quantitative prediction of regolith carbonate abundance in surficial regolith at a resolution of 90 m with associated estimates of model uncertainty. The model-derived map is broadly consistent with our current knowledge of the distribution of carbonate-rich soil and regolith in Australia. This methodology allows the rapid generation of an internally consistent and continuous layer of geoinformation that may be applicable to other carbonate-rich landscapes globally. The methodology used in this study has the potential to be used in predicting other geochemical constituents of the regolith.

The 2011 Hillshade image (tiff) shows the ground surface detail as a single layer over the whole of Christmas Island. It can be used as an alternative to the 2011 shiny colour drape tiles, although ground detail in some areas is better shown in the tiles. It was created from the 2011 DEM using ESRI ArcMap with an azmith of 315, an altitude of 45 and a vertical exaggeration of 5x.

This use of this data should be carried out with the knowledge of the contained metadata and with reference to the associated report provided by Geoscience Australia with this data (Reforming Planning Processes Trial: Rockhampton 2050). A copy of this report is available from the the Geoscience Australia website (http://www.ga.gov.au/sales) or the Geoscience Australia sales office (sales@ga.gov.au, 1800 800 173). The wind hazard outputs are a series of rasters, one for each average recurrence interval considered, presenting peak wind hazard (peak from all directions) as measure in km/h. This file presents the future climate wind hazard. The file name indicates the hazard being presented, e.g. wspd_rp_1000_max.tif is the 1000 year Return Period (RP - equivalent to Average Reccurrence Interval (ARI)) and is the maximum wind speed from all directions. The local wind multipliers adjust the 3-second gust regional RP wind speed from 10 m above ground level to ground level with the consideration of topography and shielding effects. Eight cardinal directions are calculated for every raster cell and the maximum of these values is then derived and presented here.

The first RSTT model for Australia has been developed based on the Australian Seismological Reference Model (AuSREM) that was released in late 2012. The densely-gridded P and S wave distributions of the crust and upper mantle of AuSREM have been simplified and translated into the 7 layer crustal and upper mantle RSTT model. Travel times computed with this RSTT model are evaluated against travel times computed in full 3D through the AuSREM model to assess the impact of the approximations used by RSTT. Location estimates of 5 ground truth earthquakes (GT1, GT2 and GT5) using the global ak135 reference model, the RSTT model and the full 3D travel times are compared. It is found that the RSTT model can reproduce the 3D travel times fairly accurately within its distance of applicability, thereby improving location estimates compared to using a global travel time model like ak135. However the benefit of using RSTT for locating Australian earthquakes is far less than using full 3D travel times, mainly because most stations tend to be further away from the source than the distance of RSTT applicability.

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. darlingrd_1m is an ArcGIS layer of the backscatter grid of the Darling Road survey area produced from the processed EM3002 and EM3002D backscatter data of the survey area using the CMST-GA MB Process

Imagine you are an incident controller viewing a computer screen which depicts the likely spread of a bushfire that's just started. The display shows houses and other structures in the fire's path, and even the demographics of the people living in the area - such as the number of people, their age spread, whether the household has independent transport, and whether English is their second language. In addition, imagine that you can quantify and display the uncertainty in both the fire weather and also the type and state of the vegetation, enabling the delivery of a range of simulations relating to the expected fire spread and impact. You will be able to addresses the 'what if' scenarios as the event unfolds and reject those scenarios that are no longer plausible. The advantages of such a simulation system in making speedy, well-informed decisions has been considered by a group of Bushfire CRC researchers who have collaborated to produce a 'proof of concept' system initially for use in addressing 3 case studies. The system has the working name FireDST (Fire Impact and Risk Evaluation Decision Support Tool). FireDST links various databases and models, including the Phoenix RapidFire fire prediction model and building vulnerability assessment model (radiant heat and ember attack), as well as infrastructure and demographic databases. The information is assembled into an integrated simulation framework through a geographical information system (GIS) interface. Pre-processed information, such as factors that determine the local and regional wind, and also the typical response of buildings to fire, are linked with the buildings through a database, along with census-derived social and economic information. This presentation provides an overview of the FireDST simulation 'proof of concept' tool and walks through a sample probabilistic simulation constructed using the tool.

Monitoring changes in the spatial distribution and health of biotic habitats requires spatially extensive surveys repeated through time. Although a number of habitat distribution mapping methods have been successful in clear, shallow-water coastal environments (e.g. aerial photography and Landsat imagery) and deeper (e.g. multibeam and sidescan sonar) marine environments, these methods fail in highly turbid and shallow environments such as many estuarine ecosystems. To map, model and predict key biotic habitats (seagrasses, green and red macroalgae, polychaete mounds [Ficopamatus enigmaticus] and mussel clumps [Mytilus edulis]) across a range of open and closed estuarine systems on the south-west coast of Western Australia, we integrated post-processed underwater video data with interpolated physical and spatial variables using Random Forest models. Predictive models and associated standard deviation maps were developed from fine-scale habitat cover data. Models performed well for spatial predictions of benthic habitats, with 79-90% of variation explained by depth, latitude, longitude and water quality parameters. The results of this study refine existing baseline maps of estuarine habitats and highlight the importance of biophysical processes driving plant and invertebrate species distribution within estuarine ecosystems. This study also shows that machine-learning techniques, now commonly used in terrestrial systems, also have important applications in coastal marine ecosystems. When applied to video data, these techniques provide a valuable approach to mapping and managing ecosystems that are too turbid for optical methods or too shallow for acoustic methods.

This dataset contains the current and predicted petroleum permits for the Australian region. The tenement information is derived from ENCOM Technologies in Melbourne and is exported from a proprietry software application called GPINFO. These tenements are updated 3 monthly. NOTE : there are no attributes for this dataset other than tenement name, if you want more information on tenements see GEOMET rec 3559 for the AGSO petroleum titles dataset. NOTE : This dataset is only generated as an Arcview shapefile, There is no corresponding Arcinfo dataset.

These colour and greyscale images are digital pictorial representations of a grid of onshore Bouguer Anomaly station values (Bouguer density of 2.67 t/m3) and offshore free air pseudo gravity station values extracted from the World Gravity Image (Sandwell and Smith, 1995). The onshore gravity observations are held in the Australian National Gravity Database (1997). These images contain wavelengths as small as 5000 m. Gravity digital data are available in point located form or as a grid for the Australian continent as a whole or for smaller areas.