This dataset contains sediment and geochemistry information for the Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea collected by Geoscience Australia during September and October 2012 on RV Solander (survey GA0339/SOL5650). Further information on the survey is available in the post-survey report published as Geoscience Australia Record 2013/38: Nichol, S.L., Howard, F.J.F., Kool, J., Stowar, M., Bouchet, P., Radke, L., Siwabessy, J., Przeslawski, R., Picard, K., Alvarez de Glasby, B., Colquhoun, J., Letessier, T. & Heyward, A. 2013. Oceanic Shoals Commonwealth Marine Reserve (Timor Sea) Biodiversity Survey: GA0339/SOL5650 - Post Survey Report. Record 2013/38. Geoscience Australia: Canberra. (GEOCAT #76658).

Submarine canyons are recognised as having an influence on oceanographic processes, sediment transport, productivity and benthic biodiversity from the shelf to the slope. However, not all canyons are the same and the relative importance of an individual canyon will, in part, be determined by its form, shape and position on the continental margin. Here we present an analysis of these parameters using an updated national dataset of 713 submarine canyons for the margin of mainland Australia. Attribute data for each canyon is used to classify them into canyon types across a hierarchy of canyon physical characteristics for shelf-incised and slope-confined (blind) canyons. At each level on the hierarchy, large groupings of canyons are identified that represent common sets of characteristics. The spatial distribution of canyons on the Australian margin is not regular, with clusters located in the east, southeast, west and southwest. The northern margin has the lowest concentration of canyons. We also assess the potential productivity associated with the various canyon types using chlorophyll-a data derived from satellite (MODIS) images. Shelf-incised canyons are associated with significantly higher and more temporally variable chlorophyll-a concentrations, consistent with their function as conduits for upwelling. Australian submarine canyons are well represented in the national network of marine protected areas, with 36 percent of the mapped canyon population intersecting (whole or in part) a Commonwealth Marine Reserve. This information is relevant to setting priorities for the management of these reserves. Results from this study provide a framework for further analysis of the relative importance of canyons on the Australian margin.

Bathymetric flythrough of the Southeast Margin of Australia for a Powerpoint presentation on the Marine Geoscience capabilities of the RV Investigator. The presentation will be given at the Welcome to Port Ceremony for the ship.

A biophysical dispersal model was used to simulate hydrodynamic connectivity among canyons located within Australia's South-west marine region. The results show that exchange among canyons in this area is greatly influenced by the Leeuwin current, transporting larvae in a unidirectional manner around Cape Leeuwin, and continuing eastwards along the Great Australian Bight. Larvae within canyons tend to remain within them, however if they are transported above the canyon walls, they then have the opportunity to be transported significant distances (thousands of kilometres). Analysis of the variability in connectivity patterns reveals concentrated flow near the shelf break, with increasing levels of variability leading offshore from the canyons. While the average potential flow distance and duration between canyons were approximately 550 kilometres and 33 days respectively, the average realized flow distance and duration were approximately 30 kilometres and 6 days respectively. This study provides the first consideration of connectivity among submarine canyons and will help improve management of these features by providing a better understanding of larval movement, transboundary exchange and the potential spread of invasive species.

Fisheries groups worldwide are concerned that seismic operations negatively affect catch rates within a given area, although there is a lack of field-based scientific evidence. In southeast Australia, marine seismic surveys have been blamed for mass mortalities of benthic invertebrates including the commercial scallop Pecten fumatus. Geoscience Australia conducted a 2-D seismic survey in this region in April 2015, thereby presenting an opportunity to conduct field-based experiments investigating the potential impacts on marine organisms. Moored hydrophones recorded noise before and during the seismic survey. An Autonomous Underwater Vehicle (AUV) was used to evaluate the effectiveness of seafloor images to support scallop monitoring. In addition, more traditional sampling was undertaken using a commercial scallop dredge from which a variety of biological and biochemical variables were analysed. The AUVs and dredge were deployed at three time periods (before the seismic survey, 2 months after seismic operations ceased, 10 months after seismic operations ceased), although poor-quality AUV images acquired before the survey precluded the analysis of these data. The highest sound exposure level recorded by the hydrophones was 146 dB re 1 µPa2s at 51 m water depth, at a distance of 1.4 km from the airguns. Commercial scallops were not abundant in the study area, and analysis of AUV images revealed no differences in commercial scallop types (live, clapper, dead shell, other) between experimental and control zones. Similarly, analysis of dredged scallops shows no detectable impact due to seismic activity on shell size, meat size and condition, gonad size and condition, and biochemical indices. Both AUV and dredging data showed strong spatial patterns, with significant differences between sites. Our study confirms previous work showing no evidence of immediate mortality on scallops in the field, and it expands this to include no evidence of long-term or sub-lethal effects. Negative impacts are currently confined to laboratory settings with unrealistic sound exposures. If short-term effects are investigated, we recommend a focus on the underlying mechanisms of potential impacts (i.e. physiological responses), rather than gross metrics such as mortality or size. Physiological responses to airgun sound may not be as immediately obvious as mortality or behavioural responses, but they are equally important to provide early indications of negative effects, as well as to explain the underlying mechanisms behind mortality events and reduced catch.

This study explored the full potential of high-resolution multibeam data for an automatic and accurate mapping of complex seabed under a predictive modelling framework. Despite of the extremely complex distributions of various hard substrata at the inner-shelf of the study area, we achieved a nearly perfect prediction of 'hard vs soft' classification with an AUC close to 1.0. The predictions were also satisfactory for four out of five sediment properties, with R2 values range from 0.55 to 0.73. In general, this study demonstrated that both bathymetry and backscatter information (from the multibeam data) should be fully utilised to maximise the accuracy of seabed mapping. From the modelled relationships between sediment properties and multibeam data, we found that coarser sediment generally generates stronger backscatter return and that deeper water with its low energy favours the deposition of mud content. Sorting was also found to be a better sediment composite property than mean grain size. In addition, the results proved one again the advantages of applying proper feature extraction approaches over original backscatter angular response curves.

Geoscience Australia marine reconnaissance survey TAN0713 to the Lord Howe Rise offshore eastern Australia was completed as part of the Federal Government¿s Offshore Energy Security Program between 7 October and 22 November 2007 using the New Zealand Government¿s research vessel Tangaroa. The survey was designed to sample key, deep-sea environments on the east Australian margin (a relatively poorly-studied shelf region in terms of sedimentology and benthic habitats) to better define the Capel and Faust basins, which are two major sedimentary basins beneath the Lord Howe Rise. Samples recovered on the survey contribute to a better understanding of the geology of the basins and assist with an appraisal of their petroleum potential. They also add to the inventory of baseline data on deep-sea sediments in Australia. The principal scientific objectives of the survey were to: (1) characterise the physical properties of the seabed associated with the Capel and Faust basins and Gifford Guyot; (2) investigate the geological history of the Capel and Faust basins from a geophysical and geological perspective; and (3) characterise the abiotic and biotic relationships on an offshore submerged plateau, a seamount, and locations where fluid escape features were evident. This dataset comprises total oxygen uptake and total carbon fluxes from core incubation experiments. Some relevant publications which pertain to these datasets include: 1. Heap, A.D., Hughes, M., Anderson, T., Nichol, S., Hashimoto, T., Daniell, J., Przeslawski, R., Payne, D., Radke, L., and Shipboard Party, (2009). Seabed Environments and Subsurface Geology of the Capel and Faust basins and Gifford Guyot, Eastern Australia ¿ post survey report. Geoscience Australia, Record 2009/22, 166pp. 2. Radke, L.C. Heap, A.D., Douglas, G., Nichol, S., Trafford, J., Li, J., and Przeslawski, R. 2011. A geochemical characterization of deep-sea floor sediments of the northern Lord Howe Rise. Deep Sea Research II 58: 909-921

The shallow-water (<160m) marine environment around the Australian research station, Casey station (east Antarctica) is a high use area, frequently visited by both large resupply vessels and smaller workboats conducting scientific research in the area, yet high resolution modern bathymetric data in the area, as well as much of the east Antarctic continental margin, is limited. The Casey area hosts significant levels of biodiversity, but this knowledge is geographically restricted in scope (i.e. shallow depths, close to shore). This biodiversity faces pressures from human activities and effects of climate change, yet extensive knowledge gaps remain, limiting efforts to conserve and manage it effectively. Improved bathymetric surveying in this region will begin to fill these knowledge gaps by conducting representative sampling of both the physical environment and biological communities and reduce the risk to maritime operations in the region. During the period December 2014 to February 2015, a collaborative multibeam survey (Australian Antarctic Division, Royal Australian Navy and Geoscience Australia) was conducted in the shallow-water near-shore regions adjacent to Casey station and covered an area of ca. 28 km2. The survey employed Geoscience Australia's KONGSBERG EM3002 dual head sonar system mounted on an Australian Antarctic Division supplied science workboat, the RV Howard Burton. In total, the surveyed region covered ca. 34 km2, to a maximum depth of ca. 170m. The data was processed in CARIS v8 and a seafloor surface has been gridded at a resolution of 1m. Preliminary field-based interpretation of the submarine geomorphology reveal several dominant geomorphological features which can be simplified into 4 domains as follows: (1) NW and WSW trending fault and channel systems, (2) glacio-fluvial seafloor features (possible terminal moraines) within channel features, (3) bedrock basement highs and (4) `deep isolated basins.

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

In the past two decades, multibeam sonar systems have become the preferred seabed mapping tool. Many users have assumed that multibeam bathymetry data is highly accurate in spatial position. In reality, both vertical and horizontal uncertainties exist in every data point. These uncertainties are often represented as one single measure of Total Propagated Uncertainty (TPU). TPU is important to understand because it affects the quality of products generated from multibeam bathymetry data. To account for the magnitude and spatial distribution of this influence, an objective uncertainty analysis is required. Randomisation is the key process in such an uncertainty analysis. This study compared two randomisation methods, restricted spatial randomness (RSR) and complete spatial randomness (CSR), in an uncertainty analysis of a slope gradient dataset derived from multibeam bathymetry data. CSR regards data error in every grid cell as independent and assumes that the data error varies within a known statistical distribution without any neighbourhood effect. RSR assumes spatial structure and thus spatial auto-correlation in the data. We present a case study from a survey of the Oceanic Shoals Commonwealth Marine Reserve in the Timor Sea, conducted in 2012 by the Marine Biodiversity Hub through the Australian Government National Environmental Research Program. The survey area is characterised by steep-sided carbonate banks and terraces with abrupt breaks in slope of limited spatial extent. As habitats, the carbonate banks and terraces are important because they provide hardground for diverse epibenthic assemblages of sponges and corals, with their steep sides marking the environmental transition to deeper water, soft sediment habitats. In this analysis, the data errors in the multibeam bathymetry data were assumed to follow a Gaussian distribution with a mean of zero and a standard deviation represented by the TPU. The CSR and RSR methods were each implemented using a Monte Carlo procedure with 500 iterations. After about 300 iterations, the Monte Carlo procedure converged for both methods. Results for the study area are compared against pre-processed slope data (Figure 1a). The averaged slope gradient from the CSR method is 4.5 degree greater than the original slope layer, whereas for the RSR method this value is 0.03 degree. Moreover, the slope layer from the CSR method resolves noticeably less detail than the original slope layer and is an over-simplification of the true bathymetry (Figure 1b). In contrast, the RSR method maintained the spatial pattern and detail observed in the original slope layer (Figure 1c). This study demonstrates that although the uncertainty in multibeam bathymetry data should not be ignored, its impact on the subsequent derivative analysis may be limited. The selection of appropriate randomisation method is important for the uncertainty analysis. When the data errors exhibit spatial structure, we recommend using the RSR method.