Acoustic backscatter from the seafloor is a complex function of signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, volume reverberation and other factors. Angular response is the variation in acoustic backscatter with incident angle and it is considered be an intrinsic property of the seabed. The objective of the study was to illustrate how the combination of a self-organising map (SOM) and hierarchical clustering can be used to develop an angular response facies map for Point Cloates, northwest Australia; demonstrate the cluster visualisation properties of the technique; and highlight how the technique can be used to investigate environmental variables that influence angular response.

This study used angular response curves of multibeam backscatter data to predict the distributions of seven seabed cover types in an acoustically-complex area. Several feature analysis approaches on the angular response curves were examined. A Probability Neural Network model was chosen for the predictive mapping. The prediction results have demonstrated the value of angular response curves for seabed mapping with a Kappa coefficient of 0.59. Importantly, this study demonstrated the potential of various feature analysis approaches to improve the seabed mapping. For example, the approach to derive meaningful statistical parameters from the curves achieved significant feature reduction and some performance gain (e.g., Kappa = 0.62). The first derivative analysis approach achieved the best overall statistical performance (e.g., Kappa = 0.84); while the approach to remove the global slope produced the best overall prediction map (Kappa = 0.74). We thus recommend these three feature analysis approaches, along with the original angular response curves, for future similar studies.

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

Multibeam sonars provide co-located high-resolution bathymetry and acoustic backscatter data over a swath of the seafloor. Not only does backscatter response vary with incidence angles but it also changes with different seabed habitat types as well. The resulting imagery depicts spatial changes in the morphological and physical characteristics of the seabed that many use to relate to other dataset such as biology and sediment data for seabed habitat classification purposes. As a co-custodian of national bathymetry data, Geoscience Australia holds massive volumes of multibeam data from various systems including comprehensive collection from its own SIMRAD EM3002D multibeam sonar system. Consequently, Geoscience Australia is researching the application of acoustic backscatter data for seabed habitat mapping to assist with deriving an inventory of seabed habitats for Australia's marine jurisdiction. We present a procedure and a technique developed for our SIMRAD EM3002D multibeam sonar system to derive meaningful angular backscatter response curves. The ultimate goal of this excersie is to try to make use of the angular backscatter response curve that many believe is unique and is an intrinsic property of the seafloor for seabed habitat classification purposes. Adopting the technique intially developed by the Centre for Marine Science and Technology at Curtin University of Technology, Geoscience Australia has further improved these techniques to suits its own sonar system. Issues surrounding the production of the angular backscatter response curves and their solutions will be discussed. We also present results derived from multibeam data acquired in the Joseph Bonaparte Gulf, NT and from the Carnarvorn Shelf (Point Cloates), WA from aboard AIMS Research Vessel Solander. This includes potential use of the angular backscatter response curves for seabed classification and results from a simple analysis using the Kolmogrov-Smirnov goodness of fit.

Geoscience Australia carried out a marine survey on Carnarvon shelf (WA) in 2008 (SOL4769) to map seabed bathymetry and characterise benthic environments through co-located 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.. 0308_carnarvon_shelf contains processed multibeam backscatter data of the Carnarvorn Shelf. The SIMRAD EM3002 multibeam backscatter data were processed using the CMST-GA MB Process, a multibeam processing toolbox co-developed by Geoscience Australia and Curtin University of Technology.

This report provides a description of the activities completed during the Bynoe Harbour Marine Survey, from 3 May and 17 May 2016 on the RV Solander (Survey GA4452/SOL6432). This survey was a collaboration between Geoscience Australia (GA), the Australian Institute of Marine Science (AIMS) and Department of Land Resource Management (Northern Territory Government) and the second of four surveys in the Darwin Harbour Seabed Habitat Mapping Program. This 4 year program (2014-2018) aims to improve knowledge of the marine environments in the Darwin and Bynoe Harbour regions by collating and collecting baseline information and developing thematic habitat maps that will underpin future marine resource management decisions. The program was made possible through funds provided by the INPEX-led Ichthys LNG Project to Northern Territory Government Department of Land Resource Management, and co-investment from Geoscience Australia and Australian Institute of Marine Science. The specific objectives of the Bynoe Harbour Marine Survey GA4452/SOL6432 were to: 1. Obtain high resolution geophysical (bathymetry) data for the deeper areas of Bynoe Harbour (<5 m), including Port Patterson; and, 2. Characterise substrates (acoustic backscatter properties, sub-bottom profiles, grainsize, sediment chemistry) the deeper areas of Bynoe Harbour (<5 m), including Port Patterson. Data acquired during the survey included: 698 km2 multibeam sonar bathymetry, water column and backscatter; 102 Smith-McIntyre grabs, 104 underwater camera drops, 29 sub-bottom profile lines and 34 sound velocity profiles.

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.

Darwin Harbour is the primary sea port for northern Australia, for which accurate information on the seabed is critical and required by multiple stakeholders. These stakeholders include the offshore energy industry, the fishing industry, and government authorities responsible for managing the harbour, in particular, the Port Authority. Darwin harbour is macrotidal with large areas of shallow (<10 m) subtidal and intertidal flats, dissected by bifurcating channels with localised areas of hardground. These hardground areas provide substrate for epibenthic communities. To support the informed management of Darwin Harbour, Geoscience Australia (GA), in collaboration with the Northern Territory Department of Land Resource Management (DLRM), the Australian Institute of Marine Science (AIMS) and the Darwin Port Corporation, conducted a multibeam survey of the harbour in 2011 on board MV Matthew Flinders. This was followed in 2013 by a physical sampling (sediments and video) survey by GA in collaboration with DLRM on board MV John Hickman. This paper presents results from those surveys with a focus on techniques used to produce a spatially continuous map of the harbour floor showing the distribution of hard and soft substrate types. The Darwin Harbour surveys acquired multibeam sonar data (bathymetry and backscatter) across 180 km2 gridded to 1 m resolution, 61 seabed samples and 35 underwater video observations to map and classify the seabed into habitats. Primary geomorphic features identified in Darwin Harbour include channels, banks, ridges, plains and scarps. Within the study area, acoustically hard substrates are associated with hard ground and relatively coarse seabed sediments. The hard grounds (rock, reef and coral gardens) are found mostly on banks and often overlain by a veneer of sandy sediment. In contrast, acoustically soft substrates are associated with fine sediments (mud and fine sand) that form the plains and channels. A seascape analysis was used to classify the seabed, incorporating information from multibeam data, underwater video characterisations and seabed hardness predictions. We used the Iterative Self Organising (ISO) Unsupervised Classification technique to combine the information from five variables (bathymetry, slope, rugosity, backscatter and probability of hard seabed (p-rock)) to form a single seabed habitat classification. The 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 based on the distance ratio measure. These six classes are related to a unique combination of seabed substrate, relief, bedform, presence of a sediment veneer and presence of epibenthic biota and rock/reef (hard substrate). The results presented here demonstrate the value of acoustic data for the characterisation of the seabed substrate that provides key habitats for benthic biota. This study also highlights the utility of the p-rock variable for habitat mapping at the level of distinguishing areas of hard seabed from soft sediment areas. The resultant seabed habitat 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.

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.

The Petrel Sub-basin Marine Environmental Survey GA-0335, (SOL5463) was acquired by the RV Solander during May 2012 as part of the Commonwealth Government's National Low Emission Coal Initiative (NLECI). The survey was undertaken as a collaboration between the Australian Institute of Marine Science (AIMS) and GA. The purpose was to acquire geophysical and biophysical data on shallow (less then 100m water depth) seabed environments within two targeted areas in the Petrel Sub-basin to support investigation for CO2 storage potential in these areas.