In 2012, Geoscience Australia carried out marine surveys in the Vlaming Sub-basin (Perth Basin; GA0334) and Petrel Sub-basin (Bonaparte Basin; SOL5463). The purpose of these surveys was to gather pre-competitive geophysical and biophysical data on the seabed environments within targeted areas to evaluate the seal quality for CO2 storage studies in these sub-basins. Over the duration of the Vlaming Sub-basin survey, approximately 650 km2 of multibeam sonar data, 2300 line km of sub-bottom profiler (SBP) data, 6.65 km2 of sidescan sonar imagery, 4.25 km of video footage and 89 grab samples were acquired. The Petrel Sub-basin survey acquired more than 650 km2 of multibeam sonar data and 650 line km of multi-channel SBP data. A total of 114 sampling operations recovered shallow samples or video footage for sedimentological, biological and chemical analysis. These datasets have been used to investigate possible fluid migration pathways in the shallow subsurface geology. In the Petrel Sub-basin, banks, palaeo-channels, plains, ridges and pockmark fields characterise the seafloor. In the Vlaming Sub-basin, a Holocene sediment-starved system was observed with shallow valleys, shallow terraces, sediment mega-ripples and prominent ridges on the seafloor. The complexity of both these environments and the general spatial correlation between seabed features and the subsurface geology, suggest that a large number of processes interacted to produce the present geomorphology of the continental shelves. These new datasets will contribute to the regional assessment of CO2 storage prospectivity in the Vlaming and Petrel sub-basins.

This record summarises the physical environments of the seabed for the Browse Basin.

This is a compilation of Seabed and Habitat Mapping Publications 2008 - 2010: GA Record 2008_20.pdf Vlaming Sub-Basin and Mentelle Basin: Environmental Summary GA Record 2008_23.pdf A Review of Spatial Interpolation Methods for Environmental Scientists GA Record 2009_02.pdf Carnarvon Shelf Survey Post-Survey Report GA Record 2009_09.pdf Ceduna Sub-basin: Environmental Summary GA Record 2009_10.pdf Mapping and characterising soft sediment habitats, and evaluating physical variables as surrogates of biodiversity in Jervis Bay, NSW GA Record 2009_12.pdf Temporal and fine-scale variation in the biogeochemistry of Jervis Bay GA Record 2009_13.pdf Review of Ten Key Ecological Features (KEFs) in the Northwest Marine Region GA Record 2009_22.pdf Seabed Environments and Subsurface Geology of the Capel and Faust basins and Gifford Guyot,Eastern Australia GA Record 2009_26.pdf Deep Sea Lebensspuren: Biological Features on the Seafloor of the Eastern and Western Australian Margin GA Record 2009_38.pdf Frontier basins of the west Australian continental margin: post-survey report of marine reconnaissance and geological sampling survey GA2476 GA Record 2009_42.pdf A Review of Surrogates for Marine Benthic Biodiversity GA Record 2009_43.pdf Southeast Tasmania Temperate Reef Survey Post-Survey Report GA Record 2010_09.pdf Seabed Environments of the Eastern Joseph Bonaparte Gulf, Northern Australia

This dataset contains four-class hardness (i.e., hard-1, hard-soft-2, soft-3 and soft-hard-4) prediction data from seabed mapping surveys on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf of the Timor Sea. This dataset was generated based on hard90 seabed hardness classification scheme using random forest methods based on the point data of seabed hardness classification using video images and multibeam data. Refer to Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness for further information on processing techniques applied [1]. [1] Li, J., Tran, M., Siwabessy, J., 2016. Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness PLOS ONE 11(2) e0149089.

Much of the deep sea encompasses soft-sediment plains, with very few hard substrates for invertebrates to colonise. At first glance, these habitats seem barren, but they are actually teeming with life. Compared to organisms from shallow water, many animals here are quite small. In addition, most of the animals are infaunal, meaning they live within the sediment. During feeding and burrowing, these animals form a range of features called lebensspuren, defined as any type of sedimentary structure produced by a living organism. Sampling deep sea animals can be a challenge, and traditional methods of grabs and boxcores provide only a single snapshot of a small area to characterise broad regions. Underwater imagery facilitates the characterisation of biological communities over a larger area, but the quantification of biodiversity from video is often restricted to larger epifauna, thus reducing its utility to measure biodiversity in deep sea soft sediments where animals are often small or infaunal. High resolution still images provide an interesting avenue with which to quantify biological activity based on lebensspuren. In this study, we used thousands of still images taken along the edge of the Eastern and Western margins of Australia to identify and characterise deep-sea lebensppuren. The features identified were compiled into a Lebensspuren Directory (Section 7), and the data was used to correlate abiotic factors to lebensspuren and to valuate whether the quantification of lebensspuren from still photographs is an appropriate technique for broadly quantifying biological activity and diversity in the deep sea (Sections 2 - 6).

This dataset provides the spatially continuous data of predicted seabed gravel content (sediment fraction greater than 2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.0025 decimal degree (dd) resolution raster grids format and ascii text file. The dataset covers the north-northwest region of the Australian continental EEZ. This dataset supersedes previous predictions of seabed gravel content for the region with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant areas. This dataset is intended for use at regional scale. The dataset may not be appropriate for use at local scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and interpolations updated.

Duplicate record The dataset provides the spatially continuous data of the seabed gravel content (sediment fraction >2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.01 decimal degree resolution raster format. The dataset covers the Australian continental EEZ, including seabed surrounding Tasmania. It does not include areas surrounding Macquarie Island, and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands or Australia's marine jurisdiction off of the Territory of Heard and McDonald Islands and the Australian Antarctic Territory. This dataset supersedes previous predictions of sediment gravel content for the Australian Margin with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant regions. This dataset is intended for use at national and regional scales. The dataset may not be appropriate for use at local scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and interpolations updated.

A range of physical descriptors of the seabed can potentially be used as surrogates for defining patterns of benthic marine biodiversity, including bathymetry, geomorphology and sediment type. These variables can be mapped, described and sampled across spatial scales that are of value to the management of the marine estate by providing a template for monitoring benthic ecosystems. As part of a four-year program (2007-2010) funded by the Australian Government, Geoscience Australia led marine surveys designed to collect robust datasets for the analysis of surrogacy relationships between a suite of physical variables and benthic biota in select areas of the Australian continental shelf. This paper focuses on results of the 2008 Carnarvon shelf survey, located within a Commonwealth Marine Park and adjacent to the World Heritage-listed Ningaloo Reef (Western Australia). High resolution multibeam sonar mapping, underwater video and benthic sampling revealed a complex geomorphology of ridges, mounds and sandy bedforms. The largest ridge extends 15 km alongshore is 20 m high and interpreted as a drowned forereef. Smaller ridges are ~1 km long, oriented northeast and preserve the form of aeolian dunes. Mounds are up to 5 m high and form extensive fields surrounded by flat sandy seabed. These ridges and mounds provide hardground habitat for diverse coral and sponge communities, whereas the surrounding sandy seafloor is characterised by few sessile benthic organisms. Multivariate analysis of these relationships is used to develop predictive models of benthic habitats, demonstrating the utility of high resolution physical data for informing management of these ecosystems.

Multibeam sonar data incorporates a wide range of metrics of physical seabed properties that can be utilised to generate substrate maps for marine habitat mapping. In particular, statistical descriptors of seabed form and texture can be derived to maximise the information provided by multibeam data. This study investigates the full potential of multibeam data for mapping seabed properties for an area of geomorphically complex seabed on the continental shelf offshore from Point Cloates, Western Australia. In 2008, as part of a collaborative survey within the Commonwealth Environmental Research Facilities (CERF) Marine Biodiversity Hub, Geoscience Australia acquired high resolution multibeam data and sediment samples across a 280 km2 area of the shelf, using a Kongsberg EM 3002 (300 kHz) system. Using this data, a two stage analysis was developed to: (i) separate 'hard seabed (e.g., reefs, ridges and mounds) from 'soft' sediments, and; (ii) predict textural properties for seabed sediments, including %Gravel, %Sand, %Mud, mean grain size and sorting. For a mapping tool, we chose the Random Forest Decision Tree technique. This entailed using ten combinations of input datasets as explanatory variables, including morphometric variables derived from bathymetry, and angular response curves and related statistics derived from backscatter mosaics. The training dataset was derived by combining sediment data from grab samples with locations of hard substrate inferred from bathymetry data. The predictive mapping of 'hard' and 'soft' seabed types resulted in predictions with very strong confidence levels, especially when bathymetry information was combined with backscatter data (i.e., cross-validated Area Under Curve = 0.99). The five sediment properties were predicted with moderate to good cross-validation accuracies (Figure 1). The highest accuracies were achieved for %Mud and Sorting, (R2s equal 0.73 and 0.68, respectively).

This dataset contains species identifications of echinoderms collected during survey TAN0713 (R.V. Tangaroa, 7 Oct - 22 Nov 2007). Animals were collected from the Faust and Capel basins and Gifford Guyot with a boxcore, rock dredge, or epibenthic sled. Specimens were lodged at Museum of Victoria in June 2008. Species-level identifications were undertaken by Tim O'Hara at the Museum of Victoria and were delivered to Geoscience Australia on 1 July 2008. See GA Record 2009/22 for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications.