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

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.

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.

This report presents the results of seabed mapping and habitat classification surveys completed in Darwin Harbour during 2011 and 2013 as part of the Northern Territory Government's marine habitat mapping program. This research aims to provide baseline data on the existing marine habitats and characteristics of the Darwin Harbour region. It is a collaboration between Geoscience Australia (GA), the Australian Institute of Marine Science (AIMS), the Department of Land Resource Management (DLRM) and the Darwin Port Corporation. Key objectives are to: - Produce detailed maps of the bathymetry and derived parameters such as slope and rugosity, - Classify the seabed into areas of hard and soft substrate, and, - Produce seabed habitat maps (or seascapes). Data collection was completed in two stages comprising a multibeam survey, undertaken on the MV Matthew Flinders in 2011 by DLRMs predecessor, the Department of Natural Resources, Environment, the Arts and Sport (NRETAS), GA, AIMS and the Darwin Port Corporation; and, a seabed sampling survey undertaken in 2013 on the MV John Hickman, by DLRM and GA. Data acquired from the surveys included continuous high-resolution multibeam sonar bathymetry and acoustic backscatter, video and still camera observations of seabed habitats and biological communities, and physical samples of seabed sediments. Key outcomes from the surveys include: 1. Improved understanding of the seabed of Darwin Harbour. The main seabed geomorphic features identified in Darwin Harbour include banks, ridges, plains and scarps, and a deep central channel that divides into smaller and shallower channels. Acoustically hard substrates are found mostly on banks and are associated with rocky reef and sponge gardens, and are often overlain by a thin veneer of sandy sediment. In contrast, plains and channels are characterised by acoustically soft substrates and are associated with fine sediments (mud and sand). 2. Classification of physical seabed properties to produce a Seascape Map for Darwin Harbour. Six seascape classes (potential habitats) were derived using an Iterative Self Organising (ISO) unsupervised classification scheme. These six classes are related to statistically unique combinations of seabed substrate, relief, bedform and presence of sediment veneer (quite often inferred from presence of epibenthic biota). The results presented in this report demonstrate the utility of multibeam acoustic data to broadly and objectively characterise the seabed to describe the spatial distribution of key benthic habitats. This is particularly important technique in high-turbidity settings such as Darwin Harbour where the application of satellite and aerial remote sensing techniques can be limited. The results of this study will be used for the planning and analysis of data from upcoming benthic biodiversity studies as they: - Provide robust near-continuous physical variables that can be used to predictive modelling of biodiversity; - Provide high-resolution coverage of near-continuous variables that describe the key physical characteristic of the seabed of the harbour, and; - Enhance survey sample design by providing indicative locations of likely similar biology communities.

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 provides spatial information of seabed environment to support Australian marine zone management. Central to this approach is the prediction of Australia's seabed biodiversity from spatially continuous data of seabed biophysical properties. Seabed hardness is an important environmental property for predicting marine biodiversity and is often inferred from multibeam backscatter data. Although seabed hardness can be measured based on video images, they are only available at a limited number of sampled locations. In this study, we attempt to predict the spatial distribution of seabed hardness using random forest based on video classification and available marine environmental properties. We illustrate the effects of cross-validation methods including a new cross-validation function on the selection of optimal predictive models. We also test the effects of various predictor sets on the predictive accuracy. This study provides an example for predicting the spatial distribution of environmental properties using random forest in R.

This dataset contains processed and raw backscatter data in matlab format produced by the CMST-GA MB Toolbox from various swath surveys in and around Australian waters.

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.

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 were employed: (1) un-supervised classification-based, using a two stage clustering method; and (2) supervised prediction-based, using the Random Forest method. Data for the analysis were collected by Geoscience Australia and the Australian Institute of Marine Science (AIMS) over two consecutive surveys in 2009 & 2010 in eastern Joseph Bonaparte Gulf using the AIMS Research Vessel Solander. The results indicate that the approaches developed are robust and reliable because of their overall classification accuracies (78% and 87% respectively). They are consistent with the current state of knowledge on geoacoustics i.e. the most 'acoustically hard' substrates are dominated by hard-grounds and relatively coarse seabed sediments, and the most 'acoustically soft' substrates are associated with finer sediments. Patterns associated with geomorphic facies and biological categories are also observed. For instance, 'hard' classes are mostly found on banks and are always associated with mixed sponge and coral gardens whereas 'soft' classes mainly occur in valleys where fine-grained sediments are concentrated. 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 backscatter variance 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).