The East Antarctic slope on the Sabrina margin has been shaped by diverse processes related to repeated glaciation. Differences in slope along this margin have driven variations in sedimentation that explain the gully morphology. Areas of lower slope angles have led to rapid sediment deposition during glacial expansion to the shelf edge, and subsequent sediment failure. Gullies in these areas are typically extremely U-shaped, initiate well below the shelf break, are relatively straight and long, and have low incision depths. Areas of higher slope angles enhance the flow of erosive turbidity currents during glaciations associated with the release of sediment-laden basal meltwaters. The meltwater flows create gullies that typically initiate at or near the shelf break, are V-shaped in profiles, have high sinuosity, deep incision depths and a relatively short down slope extent. The short down slope extent reflects a reduced sediment load associated with increased seawater entrainment as the slope becomes more concave in profile. These differences in gully morphology have important habitat implications, associated with differences in the structure and beta-diversity of the seafloor communities. This upper slope region also supports seafloor communities that are distinct from those on the adjacent shelf, highlighting the uniqueness of this environment for biodiversity. <b>Citation:</b> A.L. Post, P.E. O'Brien, S. Edwards, A.G. Carroll, K. Malakoff, L.K. Armand, Upper slope processes and seafloor ecosystems on the Sabrina continental slope, East Antarctica, <i>Marine Geology</i>, Volume 422, 2020, 106091, ISSN 0025-3227, https://doi.org/10.1016/j.margeo.2019.106091.

<div>This data package contains interpretations of airborne electromagnetic (AEM) conductivity sections in the Exploring for the Future (EFTF) program’s Eastern Resources Corridor (ERC) study area, in south eastern Australia. Conductivity sections from 3 AEM surveys were interpreted to provide a continuous interpretation across the study area – the EFTF AusAEM ERC (Ley-Cooper, 2021), the Frome Embayment TEMPEST (Costelloe et al., 2012) and the MinEx CRC Mundi (Brodie, 2021) AEM surveys. Selected lines from the Frome Embayment TEMPEST and MinEx CRC Mundi surveys were chosen for interpretation to align with the 20&nbsp;km line-spaced EFTF AusAEM ERC survey (Figure 1).</div><div>The aim of this study was to interpret the AEM conductivity sections to develop a regional understanding of the near-surface stratigraphy and structural architecture. To ensure that the interpretations took into account the local geological features, the AEM conductivity sections were integrated and interpreted with other geological and geophysical datasets, such as boreholes, potential fields, surface and basement geology maps, and seismic interpretations. This approach provides a near-surface fundamental regional geological framework to support more detailed investigations. </div><div>This study interpreted between the ground surface and 500&nbsp;m depth along almost 30,000 line kilometres of nominally 20&nbsp;km line-spaced AEM conductivity sections, across an area of approximately 550,000&nbsp;km2. These interpretations delineate the geo-electrical features that correspond to major chronostratigraphic boundaries, and capture detailed stratigraphic information associated with these boundaries. These interpretations produced approximately 170,000 depth estimate points or approximately 9,100 3D line segments, each attributed with high-quality geometric, stratigraphic, and ancillary data. The depth estimate points are formatted for compliance with Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database, the national repository for standardised depth estimate points. </div><div>Results from these interpretations provided support to stratigraphic drillhole targeting, as part of the Delamerian Margins NSW National Drilling Initiative campaign, a collaboration between GA’s EFTF program, the MinEx CRC National Drilling Initiative and the Geological Survey of New South Wales. The interpretations have applications in a wide range of disciplines, such as mineral, energy and groundwater resource exploration, environmental management, subsurface mapping, tectonic evolution studies, and cover thickness, prospectivity, and economic modelling. It is anticipated that these interpretations will benefit government, industry and academia with interest in the geology of the ERC region.</div>

This ESRI map (web) service contains geospatial seabed morphology and geomorphology information for Cairns Seamount within the Coral Sea Marine Park and are intended for use by marine park managers, regulators, the general public and other stakeholders. This web service uses the data product published in McNeil et al. (2023); eCat Record 147998.

Managed aquifer recharge (MAR) enhances recharge to aquifers. As part of the Exploring for the Future Southern Stuart Corridor project, remotely sensed data were used to map regolith materials and landforms, and to identify areas that represent potential MAR target areas for future investigation. Nine areas were identified, predominantly associated with alluvial landforms in low-gradient landscape settings. The surface materials are typically sandy, or sandy and silty, with the prospective areas overlying newly identified groundwater resources associated with Paleozoic sedimentary rocks of the Wiso and Georgina basins. The workflow used here can be rapidly rolled out across broader areas, and can be supplemented by higher-resolution, longer time-series remote-sensing data, coupled with data analytics, modelling and expert knowledge. Such an approach will help to identify areas of the arid interior that may be suitable for MAR schemes that could supplement water for remote communities, and agricultural and other natural resource developments. <b>Citation:</b> Smith, M.L., Hostetler, S. and Northey, J., 2020. Managed aquifer recharge prospectivity mapping in the Northern Territory arid zone using remotely sensed data. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

This report presents key results from the Upper Burdekin Groundwater Project conducted as part of Exploring for the Future (EFTF)—an eight year Australian Government funded geoscience data and information acquisition program. The first four years of the Program (2016–20) aimed to better understand the potential mineral, energy and groundwater resources in northern Australia. The Upper Burdekin Groundwater Project focused on the McBride Basalt Province (MBP) and Nulla Basalt Province (NBP) in the Upper Burdekin region of North Queensland. It was undertaken as a collaborative study between Geoscience Australia and the Queensland Government. This document reports the key findings of the project, as a synthesis of the hydrogeological investigation project and includes maps and figures to display the results.

This OGC Web Map Service (WMS) contains geospatial seabed morphology and geomorphology information for Flinders Reefs within the Coral Sea Marine Park and are intended for use by marine park managers, regulators, the general public and other stakeholders. This web service uses the data product published in McNeil et al. (2023); eCat Record 147998.

<div>This data product contains geospatial seabed morphology and geomorphology information for Flinders Reefs and Cairns Seamount (Coral Sea Marine Park). These maps are intended for use by marine park managers, regulators, the general public and other stakeholders. A nationally consistent two-part (two-step) seabed geomorphology classification system was used to map and classify the distribution of key seabed features. </div><div><br></div><div>In step 1, semi-automated GIS mapping tools (GA-SaMMT; Huang et al., 2022; eCat Record 146832) were applied to a bathymetry digital elevation model (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and to quantitatively characterise their geometries. Their geometric attributes were then used to classify each shape into discrete Morphology Feature types (Part 1: Dove et al., 2020; eCat Record 144305). In step 2, the seabed geomorphology was interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Part 2: Nanson et al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, seabed sediment samples and sub-bottom profiles supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations.</div><div><br></div><div>The Flinders Reefs seabed morphology and geomorphology maps were derived from an 8 m horizontal resolution bathymetry DEM compiled from multibeam surveys (FK200429/GA4861: Beaman et al., 2020; FK200802/GA0365: Brooke et al, 2020), Laser Airborne Depth Sounder (LADS), Light Detection and Ranging (LiDAR) and bathymetry supplied by the Australian Hydrographic Office.</div><div><br></div><div>A subset of the FK200802/GA0365 multibeam survey was gridded at 1 m horizontal resolution to derive the key morphology and geomorphology features at the top of Cairns Seamount (-35 to -66 m; within the upper mesophotic zone).</div><div><br></div><div>The data product and application schema are fully described in the accompanying Data Product Specification. </div><div><br></div><div><em>Beaman, R., Duncan, P., Smith, D., Rais, K., Siwabessy, P.J.W., Spinoccia, M. 2020. Visioning the Coral Sea Marine Park bathymetry survey (FK200429/GA4861). Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/140048>https://dx.doi.org/10.26186/140048</a>; GA eCat record 140048</em></div><div><br></div><div><em>Brooke, B., Nichol, S., Beaman, R. 2020. Seamounts, Canyons and Reefs of the Coral Sea bathymetry survey (FK200802/GA0365). Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/144385>https://dx.doi.org/10.26186/144385</a>; GA eCat record 144385</em></div><div><br></div><div><em>Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. <a href=https://doi.org/10.5281/zenodo.4075248>https://doi.org/10.5281/zenodo.4075248</a>; GA eCat Record 144305 </em></div><div><br></div><div><em>Huang, Z., Nanson, R. and Nichol, S. (2022). Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/146832>https://dx.doi.org/10.26186/146832</a>; GA eCat Record 146832</em></div><div><br></div><div><em>Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo. <a href=https://doi.org/10.5281/zenodo.7804019>https://doi.org/10.5281/zenodo.7804019</a>; GA eCat Record 147818 </em></div>

The service contains the Australian Coastal Geomorphology Smartline, used to support a national coastal risk assessment. The 'Smartline' is a representation of the geomorphic features located within 500m of the shoreline, denoted by the high water mark. The service includes geomorphology themes and stability classes.

This data product contains seabed morphology and geomorphology information for a subset area of Zeehan Marine Park. A nationally consistent seabed geomorphology classification scheme was used to map and classify the distribution of key seabed features. The Zeehan Marine Park seabed morphology and geomorphology maps were derived from a 2 m horizontal resolution bathymetry DEM compiled from a multibeam survey undertaken for Parks Australia by the University of Tasmania. Semi-automated GIS mapping tools (GA-SaMMT); (Huang et. al., 2022; eCat Record 146832) were applied to a bathymetry digital elevation model (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and to quantitatively characterise polygon geometries. Geometric attributes were then used to classify each shape into discrete Morphology Feature types (Dove et. al., 2020; eCat Record 144305). Seabed geomorphology features were interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Nanson et. al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, and survey reports supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations. The data product and classification schema are fully described in the accompanying Data Product Specification. Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. https://doi.org/10.5281/zenodo.4075248; Huang, Z., Nanson, R., Nichol, S. 2022. Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146832 Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo. https://doi.org/10.5281/zenodo.7804019

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>Most geological mapping either over-estimates the amount of bedrock exposed at the surface or can miss local bedrock exposures in geological units describing cover materials (i.e. alluvium and colluvium). A machine learning, Convolutional Neural Network (CNN) has been applied to detect outcrops (exposed bedrock at the earth’s surface) and areas of very shallow cover over bedrock (i.e. sub-crop) at one meter resolution. We used a multi-feature training dataset consisting of sites associated with urban areas, roads, outcrops, waterbodies, soil (includes bare soil and soil covered by green and dry vegetation), trees, and shadows. Even though we were only interested in mapping outcrop, a multi-criteria label set significantly improved overall accuracy of the model. The explanatory variables or covariates included high-resolution satellite imagery, Sentinel-2 imagery, and terrain derivatives. The modelling approach was tested over an area in central West NSW, Australia. Labels were split into 80% for training and 20% for out-of-sample validation. Spatial K-groups were used in the training set to minimize auto-spatial correlation between neighbouring points and reduce the potential for overfitting. Two CNN model architectures were evaluated: Simple-Net and UNet. The Simple-Net structure consists of 2D Convolution layer and flatten layer, whereas the UNet architecture includes a mixture of 2D convolution layer, max pooling, up sampling and flatten layer. These models were tested with and without the use of high-resolution imagery. The UNet model incorporating high resolution imagery gave the best results (accuracy of 0.841 and an F1 score of 0.814), compared with Simple-Net (accuracy of 0.823 and an F1 score of 0.786). However, the Simple-Net’s model without the incorporation of high-resolution imagery was a slight improvement over the UNet architecture and due to the lack of national coverage for high-resolution imagery, the Simple-Net model offers better scalability. The detection of outcrop/sub-crop has broad application in improving the spatial explicitness of existing geological maps, improving sample detection and interpretation of litho-stratigraphy and geochemistry. High spatial resolution of outcrop/sub-crop also has implications in the agricultural and civil engineering sectors, ecology and in understanding surface and near-surface hydrological systems. After this proof-of-concept phase we plan to up-scale the approach nationally using a more representative labels and national covariates.</div><div><br></div><div><strong>Citation: </strong>Du, Z., Wilford, J. &amp; Roberts, D., 2024. Find a rock or a rock nearby using Convolution Neural Networks. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/149631</div>