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

<div>The Abbot Point to Hydrographers Passage bathymetry survey was acquired for the Australian Hydrographic Office (AHO) onboard the RV Escape during the period 6 Oct 2020 – 16 Mar 2021. This was a contracted survey conducted for the Australian Hydrographic Office by iXblue Pty Ltd as part of the Hydroscheme Industry Partnership Program. The survey area encompases a section of Two-Way Route from Abbot Point through Hydrographers Passage QLD. Bathymetry data was acquired using a Kongsberg EM 2040, and processed using QPS QINSy. The dataset was then exported as a 30m resolution, 32 bit floating point GeoTIFF grid of the survey area.</div><div>This dataset is not to be used for navigational purposes.</div>

This service has been created specifically for display in the National Map and the chosen symbology may not suit other mapping applications. The Australian Topographic web map service is seamless national dataset coverage for the whole of Australia. These data are best suited to graphical applications. These data may vary greatly in quality depending on the method of capture and digitising specifications in place at the time of capture. The web map service portrays detailed graphic representation of features that appear on the Earth's surface. These features include the administration boundaries from the Geoscience Australia 250K Topographic Data, including state forest and reserves.

In the past two decades, multibeam sonar systems have become the preferred seabed mapping tool. Many users have assumed that multibeam bathymetry data is highly accurate in spatial position. In reality, both vertical and horizontal uncertainties exist in every data point. These uncertainties are often represented as one single measure of Total Propagated Uncertainty (TPU). TPU is important to understand because it affects the quality of products generated from multibeam bathymetry data. To account for the magnitude and spatial distribution of this influence, an objective uncertainty analysis is required. Randomisation is the key process in such an uncertainty analysis. This study compared two randomisation methods, restricted spatial randomness (RSR) and complete spatial randomness (CSR), in an uncertainty analysis of a slope gradient dataset derived from multibeam bathymetry data. CSR regards data error in every grid cell as independent and assumes that the data error varies within a known statistical distribution without any neighbourhood effect. RSR assumes spatial structure and thus spatial auto-correlation in the data. We present a case study from a survey of the Oceanic Shoals Commonwealth Marine Reserve in the Timor Sea, conducted in 2012 by the Marine Biodiversity Hub through the Australian Government National Environmental Research Program. The survey area is characterised by steep-sided carbonate banks and terraces with abrupt breaks in slope of limited spatial extent. As habitats, the carbonate banks and terraces are important because they provide hardground for diverse epibenthic assemblages of sponges and corals, with their steep sides marking the environmental transition to deeper water, soft sediment habitats. In this analysis, the data errors in the multibeam bathymetry data were assumed to follow a Gaussian distribution with a mean of zero and a standard deviation represented by the TPU. The CSR and RSR methods were each implemented using a Monte Carlo procedure with 500 iterations. After about 300 iterations, the Monte Carlo procedure converged for both methods. Results for the study area are compared against pre-processed slope data (Figure 1a). The averaged slope gradient from the CSR method is 4.5 degree greater than the original slope layer, whereas for the RSR method this value is 0.03 degree. Moreover, the slope layer from the CSR method resolves noticeably less detail than the original slope layer and is an over-simplification of the true bathymetry (Figure 1b). In contrast, the RSR method maintained the spatial pattern and detail observed in the original slope layer (Figure 1c). This study demonstrates that although the uncertainty in multibeam bathymetry data should not be ignored, its impact on the subsequent derivative analysis may be limited. The selection of appropriate randomisation method is important for the uncertainty analysis. When the data errors exhibit spatial structure, we recommend using the RSR method.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.

Two significant offshore data acquisition surveys along Western Australia's continental margin (AusGeo News 92) were recently completed by Geoscience Australia. They form part of the agency's ongoing collection of fundamental pre-competitive data and information to understand Australia's offshore frontier basins, and assist with planning and management of Australia's marine environments.

Much of the deep sea comprises soft-sediment habitats dominated by low abundances of small infauna, and traditional methods of biological sampling may therefore fail to sufficiently quantify biodiversity. During feeding and burrowing, many deep sea animals bioturbate the sediment, leaving signs of their activities called lebensspuren ('life traces'). In this study, we use three criteria to assess whether the quantification of lebensspuren from high resolution still images is an appropriate technique to broadly quantify biological activity in the deep sea: 1) The ability to differentiate biological assemblages between geographic regions; 2) the ability to reveal known biological patterns across environmental gradients; and 3) correlation with other methods of biological characterisation often used in the deep sea (e.g. video). Lebensspuren were quantified using a univariate measure of track richness and a multivariate measure of lebensspuren assemblages from the eastern (1712 images, 13 stations) and western (949 images, 11 stations) Australian margins. A total of 46 lebensspuren types were identified, including those matching named trace fossils. Assemblages were significantly different between the two regions, with five lebensspuren types accounting for over 95% of the differentiation (ovoid pinnate trace, crater row, spider feature, matchstick feature, mesh feature). Track richness in the combined margins dataset was correlated to depth, chlorin index (i.e. organic freshness), and possibly mud, although the strength of the relationships varied according to the dataset used. There was no relationship to total organic carbon. Lebensspuren richness from still images was significantly related to lebensspuren from video but not to occurrence of epifauna. Based on these results, the quantification of lebensspuren from still images seems an appropriate measure to broadly characterise biological activity in deep sea soft sediment ecosystems.

Please note: This product has been superseded by 50m Multibeam Dataset of Australia 2018. - This tile contains all multibeam data held by Geoscience Australia on August 2012 within the specified area. The data has been gridded to 50m resolution. Some deeper data has also been interpolated within the mapped area. The image provided can be viewed on the free software CARIS Easyview, available from the CARIS website: www.caris.com under Free Downloads.

Please note: This product has been superseded by 50m Multibeam Dataset of Australia 2018. - This tile contains all multibeam data held by Geoscience Australia on August 2012 within the specified area. The data has been gridded to 50m resolution. Some deeper data has also been interpolated within the mapped area. The image provided can be viewed on the free software CARIS Easyview, available from the CARIS website: www.caris.com under Free Downloads.

Please note: This product has been superseded by 50m Multibeam Dataset of Australia 2018. - This tile contains all multibeam data held by Geoscience Australia on August 2012 within the specified area. The data has been gridded to 50m resolution. Some deeper data has also been interpolated within the mapped area. The image provided can be viewed on the free software CARIS Easyview, available from the CARIS website: www.caris.com under Free Downloads.