The Davis Coastal Seabed Mapping Survey, Antarctica (GA-4301 / AAS2201 / HI468) was acquired by the Australian Antarctic Division workboat Howard Burton during February-March 2010 as a component of Australian Antarctic Science (AAS) Project 2201 - Natural Variability and Human Induced Change on Antarctic Nearshore Marine Benthic Communities. The survey was undertaken as a collaboration between Geoscience Australia, the Australian Antarctic Division and the Australian Hydrographic Service (Royal Australian Navy). The objectives were to provide multibeam bathymetry and backscatter of the coastal region of the Vestfold Hills around Davis Station, Antarctica, to aid the understanding of sea bed character, benthic habitats, provide a basis for hydrodynamic modeling of water movement around Davis, and to update and extend the navigational charts of the region.

A bathymetric survey of Darwin Harbour was undertaken during the period 24 June to 20 August 2011 by iXSurvey Australia Pty Ltd for the Department of Natural Resources, Environment, The Arts and Sport (NRETAS) in collaboration with Geoscience Australia (GA), the Darwin Port Corporation (DPC) and the Australian Institute of Marine Science (AIMS) using GA's Kongsberg EM3002D multibeam sonar system and DPC's vessel Matthew Flinders.

Geoscience Australia (GA) conducted a marine survey (GA0345/GA0346/TAN1411) of the north-eastern Browse Basin (Caswell Sub-basin) between 9 October and 9 November 2014 to acquire seabed and shallow geological information to support an assessment of the CO2 storage potential of the basin. The survey, undertaken as part of the Department of Industry and Science's National CO2 Infrastructure Plan (NCIP), aimed to identify and characterise indicators of natural hydrocarbon or fluid seepage that may indicate compromised seal integrity in the region. The survey was conducted in three legs aboard the New Zealand research vessel RV Tangaroa, and included scientists and technical staff from GA, the NZ National Institute of Water and Atmospheric Research Ltd. (NIWA) and Fugro Survey Pty Ltd. Shipboard data (survey ID GA0345) collected included multibeam sonar bathymetry and backscatter over 12 areas (A1, A2, A3, A4, A6b, A7, A8, B1, C1, C2b, F1, M1) totalling 455 km2 in water depths ranging from 90 - 430 m, and 611 km of sub-bottom profile lines. Seabed samples were collected from 48 stations and included 99 Smith-McIntyre grabs and 41 piston cores. An Autonomous Underwater Vehicle (AUV) (survey ID GA0346) collected higher-resolution multibeam sonar bathymetry and backscatter data, totalling 7.7 km2, along with 71 line km of side scan sonar, underwater camera and sub-bottom profile data. Twenty two Remotely Operated Vehicle (ROV) missions collected 31 hours of underwater video, 657 still images, eight grabs and one core. This catalogue entry refers to p-rock (probability of rock) grids produced from the angular response curves from the multibeam backscatter data. The extraction of angular response curves from the raw Simrad multibeam data was achieved using the multibeam backscatter CMST-GA MB Process v10.10.17.0 toolbox software co-developed by the Centre for Marine Science and Technology (CMST) at Curtin University of Technology and Geoscience Australia (described in Gavrilov et al., 2005a, 2005b; Parnum, 2007). A number of corrections were introduced to the data and the angular response curves were produced as the average response curve within the adopted sliding windows in which port and starboard swath were processed separately as part of the process of the removal of the backscatter angular dependence. Angular backscatter response curves were compared to the reference response of rock/hard bottom (inferred grabs and cores) using the Kolmogorov-Smirnov goodness of fit to estimate the probability (p-value) of rock (p-rock). Finally, the IDW interpolation technique was used to produce a continuous layer of the p-value of hard bottom for each study area.

The effective management of Darwin Harbour in Northern Australia is dependent upon accurate spatial information of seabed habitats that is required by multiple stakeholders. To develop this information, a combination of spatially continuous multibeam data, and targeted video and sediment data were used to classify the seabed and generate habitat maps. These data were acquired during collaborative surveys between Geoscience Australia, the Northern Territory Department of Land Resource Management (DLRM), the Australian Institute of Marine Science and the Darwin Port Corporation. A seascape analysis was used to classify the seabed, incorporating information from multibeam data and underwater video characterisations. We used the Iterative Self Organising Unsupervised Classification technique to combine the information from five variables to form a single classification showing potentially different seabed habitats. The 'probability of hard seabed' (p-rock) variable was derived by comparing the angular backscatter response of known areas of hard seabed to all other angular backscatter responses. We found that six habitat classes were statistically optimal and related to a unique combination of seabed substrate, relief, bedform, presence of a sediment veneer and presence of epibenthic biota and rock/reef. This presentation focuses on methods used to produce a continuous map of the harbour showing the distribution of multiple habitat types. We demonstrate the value of acoustic data for the characterisation of the seabed substrate. The resultant maps are being used by the Northern Territory DLRM to inform ongoing management of Darwin Harbour, with additional mapping planned for offshore areas and adjacent harbours in the region.

Acoustic remote sensing is the only effective technique to investigate deep sea bottom. Modern high-frequency multibeam echosounders transmit and receive backscatter signals from hundreds of narrow-angle beams which enlighten small footprints on the seabed. They can produce bathymetry and backscatter data with a spatial resolution around 2% of water depth, which enables us to map the seabed with great detail and accuracy. After calibration, the backscatter intensity is largely controlled by three seabed physical properties: the acoustic impedance contrast (often called hardness), apparent interface roughness (relative to acoustic frequency) and volume inhomogeneity [3, 4, 7]. These seabed physical properties are directly related to sediment grain size characteristics at the sedimentary areas. Studies showed that backscatter intensity had a moderate and positive correlation with sediment mean grain size [1, 3, 6]. Also, backscatter intensity was found to be positively correlated with coarse fractions and inversely correlated with finer fractions [2, 5, 6]. Other sediment grain size properties, especially sorting may also play important roles in the backscatter-sediment relationship [3, 5, 6]. The backscatter-sediment relationship, however, is complex in nature. Research is needed to better understand how acoustic sound interacts with sediment. This study aims to explore this relationship using a set of high quality sediment and multibeam backscatter data, and a robust spatial modelling technique. The co-located sediment and multibeam data were collected from four different areas of Australian margin which represent different sedimentary environments. Five hundred sixty-four sediment grab samples were taken from these survey areas. They were analysed in laboratory using the same procedure to generate grain size properties of %gravel, %sand, %mud, mean grain size, sorting, skewness and kurtosis. The multibeam data were collected using Kongsberg's 300 kHz EM3002 system. The raw multibeam backscatter was processed using the CMST-GA MB Process v8.11.02.1 software developed by Geoscience Australia and the Centre for Marine Science and Technology at Curtin University of Technology. As a result, the backscatter mosaics from incidence angles of 1o to 60o, at an interval of 1o, were generated. The backscatter intensity values from these 60 incidence angles were extracted for all of the sediment samples. The machine learning model Random Forest Decision Tree (RFDT) was used to investigate the backscatter-sediment relationship. The seven sediment grain size properties were the explanatory variables. The response variable was the backscatter intensity from each incidence angle. The model performance was evaluated using 10-fold cross-validation. For incidence angles between 1o and 42o, the RFDT models achieved fairly good performance, with a percentage of variance explained around 70% (Figure 1). The model performance gradually decreased for the outer beam range (incidence angle > 42o). Mud content was consistently identified as the most important explanatory variable to the backscatter strength. The second most important explanatory was usually sediment mean grain size. The RFDT models were also able to generate predicted response curves to quantitatively investigate the relationships between the important explanatory variables and individual response variables. The predicted relationship between %mud and the acoustic backscatter intensity is shown in Figure 2. This indicates a negative but non-linear relationship, with the increase of mud content in the sediment, the backscatter intensity decreases. This finding is consistent with that of previous studies [2, 5, 6]. Fine sediment with high mud content not only is soft (e.g., low impedence contrast) but also has high acoustic penetration (e.g., high attenudation in sediment), which naturally incurs low backcatter return

Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam swath mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. The multibeam acoustic data at a ~1000 km2 area of the Carnarvon Shelf, Western Australia was used in a predictive modeling approach to map eight seabed sediment parameters. The modeling results indicates overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness, and Mean Grain Size. The study demonstrated that predictive modelling using the combination of machine learning models has several advantages over the interpolation of Cokriging. Combing multiple machine learning models can not only improve the prediction performance but also provides the ability to generate useful prediction uncertainty maps. Another important finding is that choosing an appropriate set of explanatory variables, through a manual feature selection process, is a critical step for optimizing model performance. In addition, machine learning models are able to identify important explanatory variables, which is useful in explaining underlying environmental process and checking prediction against existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of co-variance of physical and biological data for this area. International Journal of Geographical Information Science

Geoscience Australia carried out marine surveys in southeast Tasmania in 2008 and 2009 (GA0315) to map seabed bathymetry and characterise benthic environments through observation of habitats using underwater towed video. Data was acquired using the Tasmania Aquaculture and Fisheries Institute (TAFI) Research Vessel Challenger. Bathymetric mapping was undertaken in seven survey areas, including: Freycinet Pensinula (83 sq km, east coast and shelf); Tasman Peninsula (117 sq km, east coast and shelf); Port Arthur and adjacent open coast (17 sq km); The Friars (41 sq km, south of Bruny Island); lower Huon River estuary (39 sq km); D Entrecastreaux Channel (7 sq km, at Tinderbox north of Bruny Island), and; Maria Island (3 sq km, western side). Video characterisations of the seabed concentrated on areas of bedrock reef and adjacent seabed in all mapped areas, except for D Entrecastreaux Channel and Maria Island. The "challenger" folder contains raw multibeam backscatter data from two surveys archived seperately in 0306_tasman1 and 0315_se_tasmania. The raw multibeam backscatter data were collected along survey lines using GAs Kongsberg SIMRAD EM3002 in single head configuration from aboard MV Challenger.

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 wave generated 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. The "0308_carnarvon_shelf" folder contains raw multibeam backscatter data of the Carnarvorn Shelf. The raw multibeam backscatter data were collected along survey lines using GAs Kongsberg SIMRAD EM3002 in single head configuration from aboard RV Solander.

This dataset contains multibeam sonar angular backscatter response curve data of area A1 from seabed mapping surveys on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf of the Timor Sea. The survey was conducted under a Memorandum of Understanding between Geoscience Australia (GA) and the Australian Institute of Marine Science (AIMS) in two consecutive years 2009 (GA survey number GA-0322 and AIMS survey number SOL4934) and 2010 (GA survey number GA-0325 and AIMS survey number SOL5117). The surveys obtained detailed geological (sedimentological, geochemical, geophysical) and biological data (macro-benthic and infaunal diversity, community structure) for the banks, channels and plains to investigate relationships between the physical environment and associated biota for biodiversity prediction. The surveys also provide Arafura-Timor Sea, and wider northern Australian marine region context for the benthic biodiversity of the Van Diemen Rise. Four study areas were investigated across the outer to inner shelf. Refer to the GA record 'Methodologies for seabed substrate characterisation using multibeam bathymetry, backscatter, and video data: A case study for the Eastern Joseph Bonaparte Gulf, Northern Australia' for further information on processing techniques applied (GeoCat: 74092; GA Record: 2013/11).

This dataset contains hardness classification data from seabed mapping surveys on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf of the Timor Sea. The survey was conducted under a Memorandum of Understanding between Geoscience Australia (GA) and the Australian Institute of Marine Science (AIMS) in two consecutive years 2009 (GA survey number GA-0322 and AIMS survey number SOL4934) and 2010 (GA survey number GA-0325 and AIMS survey number SOL5117). The surveys obtained detailed geological (sedimentological, geochemical, geophysical) and biological data (macro-benthic and infaunal diversity, community structure) for the banks, channels and plains to investigate relationships between the physical environment and associated biota for biodiversity prediction. The surveys also provide Arafura-Timor Sea, and wider northern Australian marine region context for the benthic biodiversity of the Van Diemen Rise. Four study areas were investigated across the outer to inner shelf. Refer to the GA record 'Methodologies for seabed substrate characterisation using multibeam bathymetry, backscatter, and video data: A case study for the Eastern Joseph Bonaparte Gulf, Northern Australia' for further information on processing techniques applied (GeoCat: 74092; GA Record: 2013/11).