Legacy product - no abstract available

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.

Legacy product - no abstract available

Surprisingly few natural hydrocarbon seeps have been identified in Australia's offshore basins despite studies spanning thirty years. Initial studies of natural hydrocarbon seepage around the Australian margin were generally based around the geochemical analysis of stranded bitumens, water column geochemical `sniffer' sampling, synthetic aperture radar or airborne laser fluorsensor. Later studies involved the integration of these remote sensing and geochemical techniques with mutli-channel and shallow seismic. A review of these earlier studies indicates that many seepage interpretations need to be re-evaluated and that previous data sets, when set in a global context, often represent normal background hydrocarbon levels. Relatively few sites of proven natural hydrocarbon seepage in Australia's offshore sedimentary basins can be reconciled with the dominantly passive margin setting and low recent sedimentation rates, which are not favourable for high rates of seepage, and difficulties in proving seepage on high energy, shallow carbonate shelves, where seabed features may be rapidly reworked and modern marine signatures are overprinted on authigenic seep carbonates. Active thermogenic methane seepage on the Yampi Shelf, the only proven documented occurrence in Australia, is driven by deposition of a thick Late Tertiary carbonate succession and Late Miocene tectonic reactivation. Therefore, to increase the success of detecting and correctly interpreting natural hydrocarbon seepage, data need to be analysed and integrated within the context of the local geological setting, and with an understanding of what is observed globally.

Legacy product - no abstract available

Magnetic field interpretation is not an alternative to palaeomagnetic methods of recovering remanent magnetization information, both because it deals with the resultant of induced and remanent magnetizations and because confidence in recovered magnetization directions cannot match than provided by direct palaeomagnetic measurement. Nevertheless, magnetic field interpretation is highly complementary to palaeomagnetic studies. Palaeomagnetism provides detailed information from small, localised samples whereas magnetic field interpretation provides estimates of the bulk magnetization of substantial volumes (which may be completely buried and un-sampled by boreholes). Without palaeomagnetic and rock magnetic studies much of the geological information latent in magnetic field measurements cannot be accessed, and without the coverage of magnetic field data the extents and relationships of subsurface magnetization events revealed by palaeomagnetic studies cannot be fully mapped.

Legacy product - no abstract available

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

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.

In a collaborative effort with the regional sub-commissions within IAG sub-commission 1.3 'Regional Reference Frames', the IAG Working Group (WG) on 'Regional Dense Velocity Fields' (see http://epncb.oma.be/IAG) has made a first attempt to create a dense global velocity field. GNSS-based velocity solutions for more than 6000 continuous and episodic GNSS tracking stations, were proposed to the WG in reply to the first call for participation issued in November 2008. The combination of a part of these solutions was done in a two-step approach: first at the regional level, and secondly at the global level. Comparisons between different velocity solutions show an RMS agreement between 0.3 mm/yr and 0.5 mm/yr resp. for the horizontal and vertical velocities. In some cases, significant disagreements between the velocities of some of the networks are seen, but these are primarily caused by the inconsistent handling of discontinuity epochs and solution numbers. In the future, the WG will re-visit the procedures in order to develop a combination process that is efficient, automated, transparent, and not more complex than it needs to be.