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.

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.

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

This report provides a description of the activities completed during the Outer Darwin Harbour Mapping Survey, from 28 May and 23 June 2015 on the RV Solander (Survey GA0351/SOL6187). 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 first 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 Outer Darwin Harbour Marine Survey GA0351/SOL6187 were to: 1. Obtain high resolution geophysical (bathymetry) data for outer Darwin Harbour, including Shoal Bay; 2. Characterise substrates (acoustic backscatter properties, grainsize, sediment chemistry) for outer Darwin Harbour, including Shoal Bay; and 3. Collect tidal data for the survey area. Data acquired during the survey included: 720 km2 multibeam sonar bathymetry and acoustic backscatter; 96 sampling stations collecting seabed sediments, underwater photography and video imagery and oceanographic information including tidal data and 54 sound velocity profiles.

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.

This dataset contains seascape classification layer derived from bathymetry and backscatter, and their derivative from seabed mapping surveys in Darwin Harbour. The survey 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 survey obtained detailed bathymetric map of Darwin Harbour. Refer to the GA record ' Mapping and Classification of Darwin Harbour Seabed' for further information on processing techniques applied (GeoCat: 79212; GA Record: 2015/xx)

The Oceanic Shoals Commonwealth Marine Reserve (CMR) (>71,000 km2) is located in the Timor Sea and is part of the National Representative System of Marine Protected Areas of Australia. The Reserve incorporates extensive areas of carbonate banks and terraces that are recognised in the North and North West Marine Region Plans as Key Ecological Features (KEFs). Although poorly studied, these banks and terraces have been identified as potential biodiversity hotspots for the Australian tropical north. As part of the National Environment Research Program Marine Biodiversity Hub, Geoscience Australia in collaboration with the Australian Institute of Marine Science undertook a marine biodiversity survey in 2012 to improve the knowledge of this area and better understand the importance of these KEFs. Amongst the many activities undertaken, continuous high-resolution multibeam mapping, video and still camera observations, and physical seabed sampling of four areas covering 510 km2 within the western side of the CMR was completed. Multibeam imagery reveals a high geomorphic diversity in the Oceanic Shoals CMR, with numerous banks and terraces, elevated 30 to 65 m above the generally flat seabed (~105 m water depth), that provide hard substrate for benthic communities. The surrounding plains are characterised by fields of depressions up to 1 m deep (pockmarks) formed in soft silty sediments that are generally barren of any epibenthos (Fig .1). A distinctive feature of many pockmarks is a linear scour mark that extends several tens of metres (up to 150 m) from pockmark depressions. Previous numerical and flume tank simulations have shown that scouring of pockmarks occurs in the direction of the dominant near-seabed flow. These geomorphic features may therefore serve as a proxy for local-scale bottom currents, which may in turn inform on sediment processes operating in these areas and contribute to the understanding of the distribution of biodiversity. This study focused on characterising these seabed scoured depressions and investigating their potential as an environmental proxy for habitat studies. We used ArcGIS spatial analyst tools to quantify the features and explored their potential relationships with other variables (multibeam backscatter, regional modelled bottom stress, biological abundance and presence/absence) to provide insight into their development, and contribute to a better understanding of the environment surrounding carbonate banks. Preliminary results show a relationship between pockmark types, (i.e. with or without scour mark) and backscatter strength. This relationship suggests some additional shallow sub-surface control, mainly related to the presence of buried carbonate banks. In addition, the results suggest that tidal flows are redirected by the banks, leading to locally varied flow directions and 'shadowing' in the lee of the larger banks. This in turn is likely to have an influence on the observed density and abundance of benthic assemblages.

Geoscience Australia undertook a marine survey of the Vlaming Sub-basin in March and April 2012 to provide seabed and shallow geological information to support an assessment of the CO2 storage potential of this sedimentary basin. 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 Vlaming Sub-basin is located offshore from Perth, Western Australia, and was previously identified by the Carbon Storage Taskforce (2009) as potentially highly suitable for CO2 storage. The principal aim of the Vlaming Sub-basin marine survey (GA survey number GA334) 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 Vlaming Sub-basin that may extend up to the seabed. The survey also mapped seabed habitats and biota in the areas of interest to provide information on communities and biophysical features that may be associated with seepage. This research addresses key questions on the potential for containment of CO2 in the Early Cretaceous Gage Sandstone (the basin's proposed CO2 storage unit) and the regional integrity of the South Perth Shale (the seal unit that overlies the Gage Sandstone). This dataset comprises high resolution backscatter grids.

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.

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