The use of multibeam bathymetry, backscatter data and their derivatives together with geophysical data, sediment samples, biological collections and underwater video/still footage to generate seabed habitat maps is an active research interest of Geoscience Australia. The obvious advantage over other techniques is that the multibeam system offers the creation of spatially continuous maps. This report presents the results of an investigation of the potential use of multibeam data (bathymetry, backscatter and their derivatives) to classify/predict the seabed substrate. Principally, the aim was to reliably and repeatedly distinguish hard from soft terrain in Van Diemen Rise of eastern Joseph Bonaparte Gulf using two independent approaches: a classification-based approach and a prediction-based approach.

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.

A seabed mapping survey over a series of carbonate banks, intervening channels and surrounding sediment plains on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf was completed under a Memorandum of Understanding between Geoscience Australia and the Australian Institute of Marine Sciences. The survey obtained detailed geological (sedimentological, geochemical, geophysical) and biological data (macro-benthic and infaunal diversity, community structure) for the banks, channels and plains to establish the late-Quaternary evolution of the region and investigate relationships between the physical environment and associated biota for biodiversity prediction. The survey also permits the biodiversity of benthos of the Van Diemen Rise to be put into a biogeographic context of the Arafura-Timor Sea and wider northern Australian marine region. Four study areas were investigated across the outer to inner shelf. Multibeam sonar data provide 100 per cent coverage of the seabed for each study area and are supplemented with geological and biological samples collected from 63 stations. In a novel approach, geochemical data collected at the stations provide an assessment of sediment and water quality for surrogacy research. Oceanographic data collected at four stations on the Van Diemen Rise will provide an understanding of the wave, tide and ocean currents as well as insights into sediment transport. A total of 1,154 square kilometres of multibeam sonar data and 340 line-km of shallow (<100 mbsf) sub-bottom profiles were collected.

Geoscience Australia (GA) has an active research interest in using multibeam bathymetry, backscatter data and their derivatives together with geophysical data, sediment samples, biological specimens and underwater video/still footage to create seabed habitat maps. This allows GA to provide spatial information about the physical and biological character of the seabed to support management of the marine estate. The main advantage of using multibeam systems over other techniques is that they provide spatially continuous maps that can be used to relate to physical samples and video observations. Here we present results of a study that aims to reliably and repeatedly delineate hard and soft seabed substrates using bathymetry, backscatter and their derivatives. Two independent approaches to the analysis of multibeam data are tested: (i) a two-stage classification-based clustering method, based solely on acoustic backscatter angular response curves, is used to derive a substrate type map. (ii) a prediction-based classification is produced using the Random Forest method based on bathymetry, backscatter data and their derivatives, with support from video and sediment data. Data for the analysis were collected by Geoscience Australia and the Australian Institute of Marine Science on the Van Dieman Rise in the Timor Sea using RV Solander. The mapped area is characterised by carbonate banks, ridges and terraces that form hardground with patchy sediment cover, and valleys and plains covered by muddy sediment. Results from the clustering method of hard and soft seabed types yielded classification accuracies of 78 - 87% when evaluated against seabed types as observed in underwater video. The prediction-based approach achieved a classification accuracy of 92% based on 10-fold cross-validation. These results are consistent with the current state of knowledge on geoacoustics. Patterns associated with geomorphic facies and biological categories are also observed. These results demonstrate the utility of acoustic data to broadly and objectively characterise the seabed substrate and thereby inform our understanding of the distribution of key habitat types.

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.

This resource contains backscatter data for the the Leveque Shelf, a sub-basin of the Browse Basin, in May 2013 on RV Solander (survey GA0340/SOL5754). The survey used a Kongsberg EM3002 300 kHz multibeam sonar system mounted in single head configuration to map six areas, covering a combined area of 1070 square kilometres. Data are gridded to 2 m spatial resolution. This survey provides seabed and shallow geological information to support an assessment of the CO2 storage potential of the Browse sedimentary basin. The basin, located on the Northwest Shelf, Western Australia, was previously identified by the Carbon Storage Taskforce (2009) as potentially suitable for CO2 storage. The survey was undertaken under the Australian Government's National CO2 Infrastructure Plan (NCIP) to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. The principal aim of the Leveque Shelf marine survey was to look for evidence of any past or current gas or fluid seepage at the seabed, and to determine whether these features are related to structures (e.g. faults) in the Leveque Shelf area that may extend to the seabed. The survey also mapped seabed habitats and biota to provide information on communities and biophysical features that may be associated with seepage. This research, combined with deeper geological studies undertaken concurrently, addresses key questions on the potential for containment of CO2 in the basin's proposed CO2 storage unit, i.e. the basal sedimentary section (Late Jurassic and Early Cretaceous), and the regional integrity of the Jamieson Formation (the seal unit overlying the main reservoir)

On behalf of Australia, and in support of the Malaysian accident investigation, the Australian Transport Safety Bureau (ATSB) led search operations for missing Malaysian Airlines flight MH370 in the Southern Indian Ocean. Geoscience Australia provided advice, expertise and support to the ATSB to facilitate marine surveys, which were undertaken to provide a detailed map of the sea floor topography and to aid navigation during the underwater search. This dataset comprises Side Scan Sonar (SSS), Synthetic Aperture Sonar (SAS) and multibeam sonar backscatter data at 5 m resolution. Data was collected during Phase 2 marine surveys conducted by the Governments of Australia, Malaysia and the People’s Republic of China between September 2014 to January 2017. The data was acquired by Echo Surveyor 7 (Kongsberg AUV Hugin 1000), Edgetech 2400 Deep Tow and SLH PS-60 Synthetic Aperture Sonar Deep Tow deployed from the following vessels: Fugro Supporter, Fugro Equator, Fugro Discovery, Havila Harmony, Dong Hai Jiu 101 and Go Phoenix. All material and data from this access point is subject to copyright. Please note the creative commons copyright notice and relating to the re-use of this material. Geoscience Australia's preference is that you attribute the datasets (and any material sourced from it) using the following wording: Source: Governments of Australia, Malaysia and the People's Republic of China, 2018. MH370 Phase 2 data. For additional assistance, please contact marine@ga.gov.au. We honour the memory of those who have lost their lives and acknowledge the enormous loss felt by their loved ones.

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.

The Australian Maritime Jurisdiction of approximately 7,000,000 km2 has, at most, 25% of its seabed surveyed at high resolution. Since September 2001, under Commonwealth Policy on Spatial Data Access and Pricing, Intergovernmental Committee on Spatial Data Access and Pricing, the co-custodian of the bathymetry data collected within the Australian Marine Jurisdiction has been assigned to Geoscience Australia (GA). GA thus hosts various formats of raw as well as processed bathymetry datasets from multiple sensors, including multibeam sonar systems. The quality between datasets varies, depending on the objectives of the survey. As of January 2013, the multibeam sonar bathymetric coverage held by GA was acquired by 48 vessels, 26 different multibeam sonar systems in 9 different frequencies between 12 and 455 kHz. Consequently, GA has to deal with a variety of survey standards, making the post-processing and merging not efficient. The objective of this document is thus to provide standards and guidance to GA personnel and contractors who conduct multibeam data acquisition and processing during marine surveys to maximise consistency and efficiency. This document provides the most critical steps to multibeam acquisition and a mandatory checklist and deliverables. Specific details and tips for processing using Caris HIPS & SIPS software and Kongsberg EM series data are also provided in the appendix.

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.