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 Corporate Administrative Records Collection of Geoscience Australia (GA) is a bi fold collection; consisting of electronic/digital documents and records in physical paper format. The digital collection consists of electronic information, which may be "born digital" (created using computer technology) or converted into digital form from their original format (e.g. scans of paper documents). These records are created by all GA employees and are evidence of business conducted by GA and its predecessors. The location of these digital records is in TRIM (electronic document management system). This product treats documents and records in the same way, so that end users perform the same task on all items that are stored in the system, irrespective of whether the item is a document or is to be declared as a record. The digital records can be captured in any format; e.g. excel document, word document, pdf document, emails, etc. When a user saves a document for the first time in TRIM they are prompted for metadata, which is then used to create the record.

A short article describing the outcomes of the Tasman Frontier Petroleum Industry Workshop held at Geoscience Australia on 8 and 9 March 2012.

These products form part of the exhibition celebrating GA's involvement in the ACT and are produced as part of the ACT centenary.

No abstract available

This set is composed of a selection of geoscience booklets, paper models and an image set - Climate Change booklet - Time and Life Booklet - Volcanoes booklet - Earthquakes booklet - Australian Earthquakes image set - Plate Tectonics booklet - Plate tectonics 3D paper model set Suitable for secondary Year levels 7-12

No abstract available

The first edition ACE - Australian Continental Elements dataset is a GIS representation of the lithosphere fabrics of the Australian plate, interpreted from linear features and associated discontinuities in the gravity anomaly map of continental Australia (Bacchin et al., 2008; Nakamura et al., 2011) and the global marine gravity dataset compiled from satellite altimetry (Sandwell & Smith, 2009). It should be used in context with these input data sources, at scales no more detailed than the nominal scale of 1:5 000 000.

A key component of Geoscience Australia's marine program involves developing products that contain spatial information about the seabed for Australia's marine jurisdiction. This spatial information is derived from sparse or unevenly distributed samples collected over a number of years using many different sampling methods. Spatial interpolation methods are used for generating spatially continuous information from the point samples. These methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Machine learning methods, like random forest (RF) and support vector machine (SVM), have proven to be among the most accurate methods in disciplines such as bioinformatics and terrestrial ecology. However, they have been rarely previously applied to the spatial interpolation of environmental variables using point samples. To improve the accuracy of spatial interpolations to better represent the seabed environment for a variety of applications, including prediction of biodiversity and surrogacy research, Geoscience Australia has conducted two simulation experiments to compare the performance of 14 mathematical and statistical methods to predict seabed mud content for three regions (i.e., Southwest, North, Northeast) of Australia's marine jurisdiction Since 2008. This study confirms the effectiveness of applying machine learning methods to spatial data interpolation, especially in combination with OK or IDS, and also confirms the effectiveness of averaging the predictions of these combined methods. Moreover, an alternative source of methods for spatial interpolation of both marine and terrestrial environmental properties using point survey samples has been identified, with associated improvements in accuracy over commonly used methods.

Australia's mineral resources are an important component of its wealth, and a long term perspective of what is likely to be available for mining is a prerequisite for formulating sound policies on resources and land-access. The national resource stocks are quantified in the annual online publication: Australia's Identified Mineral Resources: http://www.australianminesatlas.gov.au/aimr/index.jsp. This provides Geoscience Australia's assessments using its national mineral resource classification system, which is based on the McKelvey resource classification system used by the United States Geological Survey (USGS). It defines known mineral resources according to two parameters: degree of geological assurance and degree of economic feasibility of exploitation. Companies listed on the Australian Securities Exchange are required to report publicly on Ore Reserves and Mineral Resources under their control, using the Joint Ore Reserves Committee Code (JORC; see http://www.jorc.org/). This system is compatible with the national system. Data reported for individual deposits by mining companies generally provide a short term commercial perspective. They are compiled in Geoscience Australia's national mineral resources database and used in the preparation of the annual national assessments of Australia's mineral resources. This involves aggregating JORC categories from company reports into larger categories in the national system.