Multibeam sonar swath-mapping has revealed small submarine volcanic cones on the northeastern Lord Howe Rise (LHR), a submerged ribbon continent. Two such cones, aligned NNW and 120 km apart, were dredged at 23-24Degrees S. Water depth is about 1150 m nearby: the southern cone rises to 750 m and the northern to 900 m. Volcanic rocks dredged from the cones are predominantly highly altered hyaloclastites with minor basalt. The clasts are mostly intensely altered vesicular brownish glass with lesser basalt, in zeolitic, clayey, micritic or ferruginous cement. Lavas and hyaloclastites contain altered phenocrysts of olivine and plagioclase, and fresh clinopyroxene. The latter have compositions between acmite and Ti-augite, and match well clinopyroxene phenocrysts in undersaturated intraplate basanitic mafic lavas. Interbedded micrites in the volcaniclastics represent calcareous ooze that was deposited with (or later than) the volcanic pile. Foraminifera indicate that the oldest micrite is late Early Miocene (~16 Ma), and that the original ooze was deposited in cool water. Late Miocene to Pliocene micrites, presumed to be later infillings, all contain warm water forms. This evidence strongly suggests that both cones formed in pelagic depths in the Early Miocene. Ferromanganese crusts from the two cones are up to 7 cm thick and similar physically, but different chemically. The average growth rate is 3 mm/m.y.. Copper, nickel and cobalt content are relatively high in the north, but copper does not exceed 0.08 wt %, nickel 0.65% and cobalt 0.25%. The Mn:Fe ratio is high in the south (average 13.7) suggesting strong hydrothermal influence. Such small volcanic cones related to intraplate hotspot-type magmatism may occur in extensive fields like those off southern Tasmania. On Lord Howe Rise, the known small volcanic cones coincide with broad gravity highs in areas of shallow continental basement. The highs probably represent Neogene plume-related magmatism. The thick continental crust may dissipate and spread the magma widely, whereas plumes may penetrate thin oceanic crust more readily and build larger edifices. The correspondence of the ages derived from micropalaeontology and from extrapolating from nearby dated hotspot traces support such a genesis. Accordingly, gravity highs in the right setting may help predict fields of small volcanic seamounts.

This includes collection of core from sonic drilling and soil and water samples from boreholes and surface water. The Core is stored in plastic in core trays (4 x 1m). The water samples are disposed of once analysed.

GEOMA T is a geological-oceanographic computer modelling project which aims to enhance our understanding of the processes controlling sediment mobilisation on the Australian continental shelf. This report describes tidal and surface ocean swell-wave models and their application to studies of shelf sediment mobilisation. The work has been carried out over the past 2 years by a team of collaborators from AGSO, the University of Tasmania, the Australian Bureau of Meteorology and Kort & Matrikelstyrelsen, Geodetic Division in Denmark. Our models predict that swell wave energy is sufficient to mobilise fine sand (0.1 mm diameter), on at least one occasion during the year March 1997 to February 1998, over 63.5% of the Australian continental shelf. The largest and most powerful waves were able to mobilise fine sand up to a water depth of 148 m in the Great Australian Bight. Tidal currents are capable of mobilising fine sand at least once per semi-lunar cycle (ie. -2 weeks) over about 56.4% of the shelf. Overlaying the wave and tide threshold exceedence maps demonstrates that there are areas on the shelf where one process dominates, some areas where tides and waves are of relatively equal importance and still other areas where neither process is significant. We defined 6 shelf regions of relative wave and tidal energy: zero (no-mobility); waves-only, wave-dominated, mixed, tide-dominated and tides-only. The relative distribution of these regions varies with grain size. Inclusion of estimated mean grain size is being undertaken at the present time and this will enhance the usefulness of the regionalisations. GEOMA T provides a predictive, process-based understanding of the shelf sedimentary system. It helps to explain the distribution patterns of surficial sediments and will probably be useful for mapping biological habitats and communities, although further work is needed to better define these relationships. GEOMA T provides a useful tool that will assist with marine environmental management in general, and with the National Ocean's Office regional marine planning process in particular. It has demonstrated applications to marine engineering projects where shelf sediment mobilisation is of concern and to regional studies of pollution dispersal and accumulation.

Australia's near-pristine estuaries are some of our most valuable natural assets, with many natural and cultural heritage values. They are important as undisturbed habitat for native plants and animals, for biodiversity conservation, as Indigenous lands and for tourism. They also support near-shore fisheries. In addition, by studying near-pristine estuaries, scientists can learn more about the way humans have changed natural systems. This information then feeds into natural resource management because it constitutes benchmark or baseline information against which similar information from more modified estuaries can be compared.

An important aim of the comparative geomorphology of estuaries project was to increase understanding of the environmental characteristics of near-pristine estuaries and provide a reference dataset for quantifying changes in habitat patterns in modified systems. It was anticipated that this aim would be fulfilled by identifying key geomorphic characteristics of the near-pristine systems that may be used to benchmark the current condition of, and quantify change within, 'modified' waterways. Here we provide examples of some very promising results obtained from our preliminary analyses of the geomorphic habitat area information contained within the GIS maps available on OzEstuaries.

This is a joint product developed by GA and Skyring Environment Entetrprises. It is an animated CDROM developed specifically in Authoware software for state of the art visual presentation.

A growing need to manage marine biodiversity sustainably at local, regional and global scales cannot be met by applying the limited existing biological data. Abiotic surrogates of biodiversity are thus increasingly valuable in filling the gaps in our knowledge of biodiversity patterns, especially identification of hotspots, habitats needed by endangered or commercially valuable species and systems or processes important to the sustained provision of ecosystem services. This review examines the use of abiotic variables as surrogates for patterns in benthic assemblages with particular regard to how variables are tied to processes affecting biodiversity and how easily those variables can be measured at scales relevant to resource management decisions.

Geoscience Australia and the National Oceans Office carried out a joint project to produce a consistent, high-quality 9 arc second (0.0025° or ~250m at the equator) bathymetric data grid of those parts of the Australian water column jurisdiction lying between 92º E and 172º E and 8 º S and 60º S. As well as the waters adjacent the continent of Australia and Tasmania, the area selected also covers the area of water column jurisdiction surrounding Macquarie Island, and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands. The area selected does not include Australia's marine jurisdiction off the Territory of Heard and McDonald Islands and the Australian Antarctic Territory.

Map showing all of Australia's Maritime Jurisdiction north of approx 25°S . This includes areas around Cocos (Keeling) Islands and areas west of Christmas Island as well as those contiguous to the continent in the north. Included as one of the now 28 constituent maps of the "Australia's Maritime Jurisdiction Map Series" (GeoCat 71789). Depicting Australia's extended continental shelf approved by the Commission on the Limits of the Continental Shelf in April 2008, treaties and various maritime zones. Background bathymetry image is derived from a combination of the 2009 9 arc second bathymetry and topographic grid by Geoscience Australia and a grid by W.H.F. Smith and D.T. Sandwell, 1997. Background land imagery derived from Blue Marble, NASA's Earth Observatory. 3277mm x 1050mm (for 42" plotter) sized .pdf downloadable from the web.

In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of seabed mud content across the Australian Exclusive Economic Zone (AEEZ), which indicated that machine learning methods are generally among the most accurate methods. In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK) and inverse distance squared (IDS). We aim to identify the most accurate methods for spatial interpolation of seabed mud content in three regions (i.e., N, NE and SW) in AEEZ using samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods and their combinations with OK or IDS) is compared using a simulation experiment. The prediction accuracy changes with the methods, inclusion and exclusion of slope, search window size, model averaging and the study region. The combination of RF and OK (RFOK) and the combination of RF and IDS (RFIDS) are, on average, more accurate than the other methods based on the prediction accuracy and visual examination of prediction maps in all three regions when slope is included and when their searching widow size is 12 and 7, respectively. Averaging the predictions of these two most accurate methods could be an alternative for spatial interpolation. The methods identified in this study reduce the prediction error by up to 19% and their predictions depict the transitional zones between geomorphic features in comparison with the control. This study confirmed the effectiveness of combining machine learning methods with OK or IDS and produced an alternative source of methods for spatial interpolation. Procedures employed in this study for selecting the most accurate prediction methods provide guidance for future studies.