Geoscience Australia carried out a marine survey on Carnarvon shelf (WA) in 2008 (SOL4769) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments 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 Australian Institute of Marine Science (AIMS) Research Vessel Solander. Bathymetric mapping, sampling and video transects were completed in three survey areas that extended seaward from Ningaloo Reef to the shelf edge, including: Mandu Creek (80 sq km); Point Cloates (281 sq km), and; Gnaraloo (321 sq km). Additional bathymetric mapping (but no sampling or video) was completed between Mandu creek and Point Cloates, covering 277 sq km and north of Mandu Creek, covering 79 sq km. Two oceanographic moorings were deployed in the Point Cloates survey area. The survey also mapped and sampled an area to the northeast of the Muiron Islands covering 52 sq km. cloates_3m is an ArcINFO grid of Point Cloates of Carnarvon Shelf survey area produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

This metadata relates to the ANUGA hydrodynamic modelling results for Busselton, south-west Western Australia. The results consist of inundation extent and peak momentum gridded spatial data for each of the ten modelling scenarios. The scenarios are based on Tropical Cyclone (TC) Alby that impacted Western Australia in 1978 and the combination of TC Alby with a track and time shift, sea-level rise and riverine flood scenarios. The inundation extent defines grid cells that were identified as wet within each of the modelling scenarios. The momentum results define the maximum momentum value recorded for each inundated grid cell within each modelling scenario. Refer to the professional opinion (Coastal inundation modelling for Busselton, Western Australia, under current and future climate) for details of the project.

This map has been produced for a court case for the Fair Work Ombudsman. The points on the map were sourced from documents supplied by the Fair Work Ombudsman. Boundaries sourced from AMB v2.0 Refer to Advice Register 679. Location M:\advice\fwo

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). This file identifes the storm tide inundation extent for a specific Average Recurrence Interval (ARI) event. Naming convention: SLR = Sea Level Rise s1a4 = s1 = Stage 1(extra-tropical storm tide), s2 = Stage 2 (tropical cyclone storm tide) (relating to Haigh et al. 2012 storm tide study), a4 = area 4 and a5 = area 5 2p93 = Inundation height, in this case 2.93 m Dice = this data was processed with the ESRI Dice tool.

The hydrocarbon generative potential and the thermal maturity of source rocks in the offshore northern Perth Basin was reassessed based on existing and new geochemical data to get a better understanding of the basin's prospectivity. The study establishes for the first time that the main source of onshore accumulations, the Late Permian-Early Triassic Hovea Member, is well developed offshore and contains organic-rich sediments of oil-prone character. This finding shatters the long-held view that the Hovea Member was either absent or of poor quality offshore and provides a new perspective on the basin's prospectivity. The source potential of the Hovea Member varies spatially with best source rocks observed in the Beagle Ridge and Central Abrolhos Sub-basin. The Late Permian Irwin River Sequence and several Jurassic Sequences are also identified as prime potential source rocks offshore, mostly for their gas-generative potential. Oil-generative potential was identified in the Middle to Late Jurassic Yarragadee Sequence and possibly in the Middle Jurassic Cadda Sequence.

A defining characteristic of the seabed is the proportion that is hard, or immobile. For marine ecosystems, hard seabed provides the solid substrate needed to support sessile benthic communities, often forming 'hotspots' of biodiversity such as coral and sponge gardens. For the offshore resource and energy industry, knowledge of the distribution of hard versus soft seabed is important for planning infrastructure (pipelines, wells) and to managing risk posed by geo-hazards such as migrating sand waves or mass movements on steep banks. Maps that delineate areas of hard and soft seabed are therefore a key product to the informed management and use of Australia's vast marine estate. As part of the Australian Government's Offshore Energy Security Program (2007-2011) and continuing under the National CO2 Infrastructure Plan (2011-2015), Geoscience Australia has been developing integrated seabed mapping methods to better map and predict seabed hardness using acoustic data (multibeam sonar), integrated with information from biological and physical samples. The first method used was a two-stage, classification-based clustering method. This method uses acoustic backscatter angular response curves to derive a substrate type map. The angular response curve is the backscatter value as a function of the incidence angle, where this angle lies between the incident acoustic signal from the normal. The second method was a prediction-based classification, using a machine learning method called random forest. This method was based on bathymetry, backscatter data and their derivatives, as well as underwater video and sediment data. The techniques developed by Geoscience Australia offer a fast and inexpensive assessment of the seabed that can be used where intensive seabed sampling is not feasible. Moreover, these techniques can be applied to areas where only multibeam acoustic data are available. Importantly, the identification of seabed substrate types in spatially continuous maps provides valuable baseline information for effective marine conservation management and infrastructure development.

Previous approaches to estimating the subsurface temperatures for the whole of the Australian continent have relied on the interpolation of direct temperature observations from mineral and petroleum boreholes, well completion reports and/ or measured and synthetic heat flow determinations. However, data points are scarce and unevenly distributed across the continent, which leads to the interpolation of observed temperatures across vast distances. The limited depth range of data points also means that shallow observations are often extrapolated to a depth of interest, using limited assumptions about geology at depth. Furthermore, the information available from some well completion reports can often lead to questions of data quality, particularly from older reports. A robust method for estimating temperatures in areas without direct observations is important as understanding the conditions required to generate anomalous temperatures will help to reduce geothermal exploration risk. It may also lead to identifying areas of potential that are currently unidentified due to the lack of availability of temperature data, or erroneous observations. A new approach for estimating continental-scale subsurface temperatures is currently being developed that relies on combining available proxy data using high-performance computing and large continental-scale datasets. The new modelling approach brings together the current national-scale knowledge of the datasets including the Moho, potential field geophysics, sedimentary basins, thermal conductivity, crustal heat production, and granite volumes and heat production. Bringing together such a range of datasets provides a geoscientific basis by which to estimate temperature in regions where direct observations are not available. Furthermore, the performance of computing facilities, such as the National Computational Infrastructure, is enabling insights into the nature of Australia's geothermal resources which had not been previously available. This includes developing an understanding of the errors involved in such a study through the quantification of uncertainties. Currently the new approach is being run at a pilot scale, however, initial results are encouraging. The pilot study has been able to reproduce the temperature trends observed in areas that have been heavily constrained by bore-hole observations. Furthermore, a number of areas have now been identified, due to the difference in their estimated temperature from previous methods, which warrant further study.

Vegetation index time series generated from coarse resolution sensors such as MODIS, MERIS and AVHRR have long been used to characterize land cover, land cover change and vegetation dynamics. However such techniques have not typically been applied to moderate resolution data i.e. Landsat data due to challenges associated with automated atmospheric correction, view angle normalization, BRDF correction and automated cloud/cloud shadow/pixel saturation. However operational approaches that address these challenges [1][2] and [3] have recently been implemented on national archives of Landsat imagery. These two developments make it possible to generate a vegetation index time series at Landsat scales. Furthermore, the conversion of digital numbers to surface reflectance makes it possible to use surface reflectance measurements made by Landsat-5 TM and Landsat-7 ETM+ [4] to create a time series that consists of measurements made by both sensors. The capacity to generate 25 metre resolution vegetation index time series represents an important development in our capacity to monitor food production systems and native vegetation communities from both a productivity and vegetation condition perspective. The capacity to create time series from multiple sensors is a critical step in enabling systematic analysis of deep archives of satellite imagery so that we can compare the changes taking place now with those that have occurred in the past.

Understanding how land cover responds to natural and anthropogenic drivers is critical as increasing population, climate fluctuations and competing land uses place increased pressure on both natural and food/fibre production systems. In 2011 Geoscience Australia released Version 1 of the Dynamic Land Cover Dataset (DLCDv1)[1] which consisted of a ISO 19144-2 land cover classification based on 8 years of 250 metre Moderate Resolution Image Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series [1]. Whereas Version 1 was a synoptic overview of land cover, Version 2 of the DLCD provides a series of land cover maps updated on an annual basis to enable resource managers, decision makers and biophysical modelers to track the change in land cover on a systematic basis.

Consolidated seabed boundaries under Article 1 of the 1971 Agreement, Articles 1 & 2 of the 1972 Agreement & Article 1 of the 1997 Treaty between the Governments of the Commonwealth of Australia and the Republic of Indonesia Diagram AU/INDON-06 Refer previous GeoCat 65368 Treaty text and coordinates can be found at: http://www.austlii.edu.au/au/other/dfat/treaties/1973/31.html Note: Points A1 & A2 form part of the Indonesian-Papua New Guinea Seabed Boundary http://www.austlii.edu.au/au/other/dfat/treaties/1973/32.html and http://http://www.austlii.edu.au/au/other/dfat/treaties/notinforce/1997/4.html Note: This is a signed text but has not yet entered into force