Subset of Rockchem whole-rock database release 3. Contains 1009 whole-rock analyses of rocks from the Arunta Block.

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.

Please contact education@ga.gov.au for information regarding the availability of this product.

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.

This includes collection of core from sonic drilling and soil and water samples from boreholes and surface water. The Core is stored in plastic in core trays (4 x 1m). The water samples are disposed of once analysed.

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

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.

No abstract available

Spatial interpolation methods for generating spatially continuous data from point locations of environmental variables are essential for ecosystem management and biodiversity conservation. They can be classified into three groups (Li and Heap 2008): 1) non-geostatistical methods (e.g., inverse distance weighting), 2) geostatistical methods (e.g., ordinary kriging: OK) and 3) combined methods (e.g. regression kriging). Machine learning methods, like random forest (RF) and support vector machine (SVM), have shown their robustness in data mining fields. However, they have not been applied to the spatial prediction of environmental variables (Li and Heap 2008). Given that none of the existing spatial interpolation methods is superior to the others, several questions remain, namely: 1) could machine learning methods be applied to the spatial prediction of environmental variables; 2) how reliable are their predictions; 3) could the combination of these methods with the existing interpolation methods improve the predictions; and 4) what contributes to their accuracy? To address these questions, we conducted a simulation experiment to compare the predictions of several methods for mud content on the southwest Australian marine margin. In this study, we discuss results derived from this experiment, visually examine the spatial predictions, and compare the results with the findings in previous publications. The outcomes of this study have both practical and theoretical importance and can be applied to the spatial prediction of a range of environmental variables for informed decision making in environmental management. This study reveals a new direction in and provides alternative methods for spatial interpolation in environmental sciences.