short discussion on why and how to define lithostratigraphic units, and where to find information on describing sequence stratigraphic and regolith units.

We measured the light absorption properties of two naturally occurring Australian hydrocarbon oils, a Gippsland light crude oil and a North West Shelf light condensate. Using these results in conjunction with estimated sensor environmental noise thresholds, the theoretical minimum limit of detectability of each oil type (as a function of oil thickness) was calculated for both the hyperspectral HYMAP and multispectral Quickbird sensors. The Gippsland crude oil is discernable at layer thickness of 20 micro metres or more in the Quickbird green channel. The HYMAP sensor was found to be theoretically capable of detecting a layer of Gippsland crude oil with a thickness of 10 micro metres in approximately six sensor channels. By contrast, the North West Shelf light condensate was not able to be detected by either sensor for any thickness up to 200 icro metres. Optical remote sensing is therefore not applicable for detecting diagnostic absorption features associated with this light condensate oil type, which is considered representative for the prospective Australian Northwest Shelf area. We conclude that oil type is critical to the applicability of optical remote sensing for natural oil slick detection and identification. We recommend that a sensor- and oil-specific sensitivity study should be conducted prior to applying optical remote sensors for oil exploration. The oil optical properties were obtained using two different laboratory methods, a reflectance-based approach and transmittance-based approach. The reflectance-based approach was relatively complex to implement, but was chosen in order to replicate as closely as possible real world remote sensing measurement conditions of an oil film on water. The transmittance-based approach, based upon standard laboratory spectrophotometric measurements was found to generate results in good agreement with the reflectance-based approach. Therefore, for future oil- and sensor-specific sensitivity studies, we recommend the relatively accessible transmittance-based approach, which is detailed in this paper.

Disaster management is most effective when it is based on evidence. Evidence-based disaster management means that decision makers are better informed, and the decision making process delivers more rational, credible and objective disaster management outcomes. To achieve this, fundamental data needs to be translated into information and knowledge, before it can be put to use by the decision makers as policy, planning and implementation. Disaster can come in all forms: rapid and destructive like earthquakes and tsunamis, or gradual and destructive like drought and climate change. Tactical and strategic responses need to be based on the appropriate information to minimise impacts on the community and promote subsequent recovery. This implies a comprehensive supply of information, in order to establish the direct and indirect losses, and to establish short and long term social and economic resilience. The development of the National Exposure Information System (NEXIS) is a significant national project being undertaken by Geoscience Australia (GA). NEXIS collects, collates, manages and provides the information required to assess multi-hazard impacts. Exposure information may be defined as a suite of information relevant to all those involved in a natural disaster, including the victims, the emergency services, and the policy and planning instrumentalities.

In a collaborative effort with the regional sub-commissions within IAG sub-commission 1.3 'Regional Reference Frames', the IAG Working Group (WG) on 'Regional Dense Velocity Fields' (see http://epncb.oma.be/IAG) has made a first attempt to create a dense global velocity field. GNSS-based velocity solutions for more than 6000 continuous and episodic GNSS tracking stations, were proposed to the WG in reply to the first call for participation issued in November 2008. The combination of a part of these solutions was done in a two-step approach: first at the regional level, and secondly at the global level. Comparisons between different velocity solutions show an RMS agreement between 0.3 mm/yr and 0.5 mm/yr resp. for the horizontal and vertical velocities. In some cases, significant disagreements between the velocities of some of the networks are seen, but these are primarily caused by the inconsistent handling of discontinuity epochs and solution numbers. In the future, the WG will re-visit the procedures in order to develop a combination process that is efficient, automated, transparent, and not more complex than it needs to be.

This study tested the performance of 16 species models in predicting the distribution of sponges on the Australian continental shelf using a common set of environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distributions of sponge as a species group can be successfully predicted. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data - the new data were able to improve modelling performance for all the models both in terms of statistics (~10%) and in the predicted spatial distributions. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom water temperature and the resource variables that describe bottom water nutrient status were found to be useful surrogates for sponge distribution at the broad regional scale. This study demonstrates that predictive modelling techniques can enhance our understanding of processes that influence spatial patterns of benthic marine biodiversity. Ecological Informatics

Geoscience Australia conducted a marine mapping survey between October 2008 and January 2009 to document the seabed environments and sub-surface geology of the Zeewyck, Houtman and Exmouth sub-basins and the deep-water Wallaby (Cuvier) Plateau, in Western Australia. The seabed mapping survey was the second and largest mapping survey of the Commonwealth Government's Offshore Energy Security Program. The survey documented seabed environments using multibeam sonar and sub-bottom profiler data, and characterised benthic habitats and biota from towed video footage and seabed samples. Preliminary analysis indicates that the seabed of the three sub-basins comprises carbonate mud that supports relatively sparse infaunal assemblages, while the numerous submarine canyons that incise the basins are characterised by steep rock walls that support sparse assemblages of suspension feeding organisms, such as sponges and gorgonians. Three volcanic (basaltic) peaks on the upper slopes of the sub-basins (rising 200 m above the seabed) were also mapped and surveyed, with relic coral communities recorded within their sediments. Data collected from the survey are being analysed in conjunction with existing environmental data to describe the key seabed habitats and biota for the offshore basins through a series of environmental summaries that will be made available to support future acreage release in the sub-basins. This research was undertaken concurrent to a regional 2D seismic survey to provide a broader understanding of the region. The environmental summaries of these and other Australian Frontier regions will be available to support future acreage release as part of the Offshore Energy Security Program.

Mafic and ultramafic rocks hosted by metamorphosed deep marine sediments in the Glenelg River Complex of SE Australia comprise variably tectonised fragments of a late Neoproterozoic-earliest Cambrian hyper-extended continental margin that was dismembered and thrust westward over the adjacent continental margin during the Cambro-Ordovician Delamerian-Ross Orogeny. Ultramafic rocks include serpentinised harzburgite of inferred subcontinental lithospheric origin that had already been exhumed at the seafloor before sedimentation commenced whereas mafic rocks exhibit mainly E- and N-MORB basaltic compositions consistent with emplacement into a deep marine environment floored by little if any continental crust. Contrary to previous suggestions, these rocks and their metasedimentary host rocks are not a more distal correlative of the Cambrian Kanmantoo Group. The latter is host to basaltic rocks with higher degrees of crustal contamination and a detrital zircon population with a prominent peak at 500-600 Ma. Except for quartz greywacke in the uppermost part of the sequence, the Glenelg River Complex is devoid of detrital zircon, pointing to deep marine sedimentation far removed from any continental margin. Deep seismic reflection data support the idea that the Glenelg River Complex is underlain by a substrate of mafic and ultramafic rocks and preclude earlier interpretations based on aeromagnetic data that the continental margin hosts a thick pile of seaward-dipping basaltic flows analogous to those developed along volcanic margins in the North Atlantic.

Reliable marine benthic habitat maps at regional and national scales are needed to enable the move towards the sustainable management of marine environmental resources. The most effective means of developing broad-scale benthic habitat maps is to use commonly available marine physical data due to the paucity of adequate biological data and the prohibitive cost of directly sampling benthic biota over large areas. A new robust method of mapping marine benthic habitats at this scale was developed based on a stratified approach to habitat classification. This approach explicitly uses knowledge of marine benthic ecology to determine an appropriate number of stratification levels, to choose the most suitable environmental variables for each level, and to select ecologically significant boundary conditions (i.e. threshold values) for each variable. Three stratification levels, with nine environmental variables, were created using a spatial segmentation approach. Each level represents major environmental processes and characteristics of the Australian marine benthic environment. The finest scale of benthic habitat is represented by seafloor physical properties of topography, sediment grain size and seabed shear stress. Water-column nutrient parameters and bottom water temperature depicted the intermediate scale, while the broadest scale was defined by seabed insolation parameters derived from depth data. The classifications of the three stratified levels were implemented using an object-based fuzzy classification technique that recognises that habitats are largely homogenous spatial regions, and transitions between habitats are often gradual. Classification reliability was indicated in confidence maps. Physical habitat diversity was evaluated for the final benthic habitat map that combines the three classifications. The final benthic habitat map identifies the structurally complex continental shelf break as an area of relatively high habitat diversity. Continental Shelf Research

Uluru (Ayers Rock) and Kata Tjuta (Mount Olga) are two of Australia's best-known landmarks, and thousands of people visit them each year. Geoscience Australia is preparing a new edition of 'Uluru & Kata Tjuta: a geological history' (Sweet et al in prep), which will include a new solid-geology map and cross-sections based on outcrop information, the results of drilling of more than 200 water bores in the 1970s by the Northern Territory Government, and interpretation of aeromagnetic data collected in 1988 by the Northern Territory Geological Survey.

This paper presents a model to assess bushfire hazard in south-eastern Australia. The model utilises climate model simulations instead of observational data. Bushfire hazard is assessed by calculating return periods of the McArthur Forest Fires Danger Index (FFDI). The return periods of the FFDI are calculated by fitting an extreme value distribution to the tail of the FFDI data. The results have been compared against a spatial distribution of bushfire hazard obtained by interpolation of FFDI calculated at a number of recording stations in Australia. The results show that climate simulations produce a similar pattern of bushfire hazard than the interpolated observations but the simulated values tend to be up to 60% lower than the observations. This study shows that the major source of error in the simulations is the values of wind speed. Observational wind speed is recorded at a point-based station whilst climate simulated wind speed is averaged over a grid cell. On the other hand FFDI calculation is very sensitive to wind speed and hence to improve the calculation of FFDI using climate simulations it is necessary to correct the bias observed in the simulations. A statistically-based procedure to correct the simulation bias has been developed in this project. Bias-corrected calculation of FFDI shows that the major bushfire hazard in south-eastern Australia is in the western parts of SA and NSW; and in south-western Tasmania.