numerical modelling
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Cenozoic basins of the Lake Frome region in South Australia contain most of Australia's known resources of sandstone-hosted uranium mineralisation. In addition to the currently operating Beverley uranium mine, two other deposits have been approved for mining (Honeymoon, Four Mile East) and discoveries continue to be made in the region (e.g., Beverley North; Heathgate Resources, announcement September 2009). While the known resources are significant, the potential of the region for very large uranium deposits has not been well understood, in part because of limited knowledge of the regional and district scale geological controls on uranium mineralisation. The multidisciplinary study reported herein applies a 'mineral systems' approach to identify and map the principal geological controls on the location of known uranium mineralisation in the Lake Frome region. This new framework is aimed at providing a basis for refined exploration targeting of areas with potential for major undiscovered deposits, thus reducing investment risk for the exploration industry. There are two resources available. 1. GA Record 2009/040 PDF format 2. GA Record 2009/040 Resource Pack ZIP File (Includes GA Record 2009/040, Figure 3.3, Figure 3.4, Figure 3.5, Figure 3.6, Figure 3.7)
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Geoscience Australia has collaboratively developed a number of open source software models and tools to estimate hazard, impact and risk to communicaties for a range of natural hazard to support disaster risk reduction in Australia and the region. These models and tools include: * ANUGA * EQRM * TCRM * TsuDAT * RICS * FiDAT This presentation will discuss the drivers for developing these models and tools using open source software and the benefits to the end-users in the emergency management and planning community as well as the broader research community. Progress and plans for these models and tools will also be outlined in particular those that take advantage of the availability of high performance computing, cloud computing, webservices and global initiatives such as the Global Earthquake Model.
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Population connectivity research involves investigating the presence, strength and characteristics of spatial and temporal relationships between populations. These data can be used in many different ways: to identify source-sink relationships between populations; to detect critical pathways or keystone habitats; to find natural clusters or biogeographic regions; or to investigate the processes underlying population genetic structure, among others. This information can be of significant value for managers and decision-makers when designing reserve networks, evaluating the potential spread of invasive species. This database represents the first publicly-available collection of national/continental-scale marine connectivity data.
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Ross C Brodie James Reid Monte Carlo Inversion of SkyTEM AEM data from Lake Thetis, Western Australia A SkyTEM airborne electromagnetic dataset was inverted using a 1D reversible jump Markov chain Monte Carlo algorithm. The inversion of each dual-moment sounding generates an ensemble of 300,000 models that fit the data. The algorithm automatically varies the number of layers in the large range of models that are tested. Analysis of the statistical properties of the ensemble yields a wealth of information on the probable conductivity distribution plus the mean, mode, median and most likely summary models. Robust information on the non-uniqueness and uncertainty of the results is also afforded by the ensemble. These are conveyed on conductivity map and section products. Estimates of the probable depths to interfaces are a further outcome. These depth estimates show great potential as an aid for mapping geological surfaces. The resulting conductivity maps and sections are coherent and appear to be geologically realistic on face value. However it is demonstrated with 3D modelling that a plausible hydrogeological interpretation on the sections is likely to be an artefact of 1D inversion of a 3D geological scenario. Key words: Electromagnetic, airborne, inversion, Monte Carlo, uncertainty, 3D.
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Geoscience Australia (GA) has an active research interest in using multibeam bathymetry, backscatter data and their derivatives together with geophysical data, sediment samples, biological specimens and underwater video/still footage to create seabed habitat maps. This allows GA to provide spatial information about the physical and biological character of the seabed to support management of the marine estate. The main advantage of using multibeam systems over other techniques is that they provide spatially continuous maps that can be used to relate to physical samples and video observations. Here we present results of a study that aims to reliably and repeatedly delineate hard and soft seabed substrates using bathymetry, backscatter and their derivatives. Two independent approaches to the analysis of multibeam data are tested: (i) a two-stage classification-based clustering method, based solely on acoustic backscatter angular response curves, is used to derive a substrate type map. (ii) a prediction-based classification is produced using the Random Forest method based on bathymetry, backscatter data and their derivatives, with support from video and sediment data. Data for the analysis were collected by Geoscience Australia and the Australian Institute of Marine Science on the Van Dieman Rise in the Timor Sea using RV Solander. The mapped area is characterised by carbonate banks, ridges and terraces that form hardground with patchy sediment cover, and valleys and plains covered by muddy sediment. Results from the clustering method of hard and soft seabed types yielded classification accuracies of 78 - 87% when evaluated against seabed types as observed in underwater video. The prediction-based approach achieved a classification accuracy of 92% based on 10-fold cross-validation. These results are consistent with the current state of knowledge on geoacoustics. Patterns associated with geomorphic facies and biological categories are also observed. These results demonstrate the utility of acoustic data to broadly and objectively characterise the seabed substrate and thereby inform our understanding of the distribution of key habitat types.
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The short historical record of tropical cyclone activity in the Australian region is insufficient for estimating return period wind speeds at long return periods (greater than 100 years). Utilising the auto-correlated nature of tropical cyclone behaviour (forward speed and direction, intensity and size), Geoscience Australia has developed a statistical-parametric model of tropical cyclone behaviour to generate synthetic event sets that are statistically similar to the historical record. The track model is auto-regressive, with lag-1 auto-regression used for forward speed and bearing, and lag-2 auto-regression applied to the intensity and size characteristics. Applying a parametric wind field and a linear boundary layer model to the synthetic tropical cyclone tracks allows users to generate synthetic wind swaths, and in turn fit extreme value distributions to evaluate return period wind speeds spatially. The model has been applied to evaluate severe wind hazard across Australia and neighbouring regions. In conjunction with statistical models of synoptic (mid-latitude storms) and thunderstorm wind hazard, we have been able to generate a national assessment of severe wind hazard, which is comparable to existing wind loading design standards. Using tropical cyclone-like vortex tracks directly detected from regional climate models, it is also possible to project cyclonic wind hazard into future climate conditions, accounting for both changes in frequency and intensity of tropical cyclones.
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The folder contains the tropical cyclone wind hazard simulations undertaken by GA for the Pacific Climate Change Science Program (PCCSP). Subfolders contain the input data and results for historical and projected wind hazard for the West Pacific obtained using GA's Tropical Cyclone Risk Model (TCRM). The historical hazard was derived from the global best track archive (IBTrACS) and two sets of hazard projections were performed. The first set was based on statistical-dynamical downscaled tracks from WindRiskTech for four GCMs (CNRM, ECHAM5, MIROC, MRI) and for two time windows of 20C and 2080-2099 under an A1B SRES emission scenario. The second set was based on tracks from dynamical-downscaling (CCAM) of five GCMs (CSIRO MK3.5, ECHAM5, GFDL CM2.0, GFDL CM2.1, HADCM3) for 1981-2000 and 2080-2099 under an A2 SRES emissions scenario. A doc folder also contains a report and draft paper to provide additional information.
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Spatial interpolation methods for generating spatially continuous data from point samples of environmental variables are essential for environmental management and conservation. They may fall into three groups: non-geostatistical methods (e.g., inverse distance weighting), geostatistical methods (e.g., ordinary kriging) and combined/hybrid methods (e.g. regression kriging); and their performance is often data-specific (Li and Heap, 2008). Because of the robustness of machine learning methods, like random forest and support vector machine, in data mining fields, we introduced them into spatial statistics by applying them to the spatial predictions of seabed mud content in combination with existing spatial interpolation methods (Li et al., 2011). This development can be viewed as an extension of the combined methods from statistical methods to machine learning field. These applications have significantly improved the prediction accuracy and opened an alternative source of methods for spatial interpolation. Given that they have only been applied to one variable, several questions remain, namely: are they dataset- specific? How reliable are their predictions for different datasets and variables? Could other machine learning methods (such as boosted regression trees) improve the spatial interpolations? To address these questions, we experimentally compared the predictions of several methods for sand content on the southwest Australian marine margin. We tested a variety of existing spatial interpolation methods, machine learning methods and their combinations. In this study, we discuss the experimental results and the value of this advancement in spatial interpolation, visually examine the spatial predictions, and compare the results with the findings in the previous publications. The outcomes of this study can be applied to the spatial prediction of marine and terrestrial environmental variables.
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The Tropical Cyclone Risk Model (TCRM) is a stochastic modelling system intended for the evaluation of hazard and risk associated with tropical cyclones, specifically focused on wind hazard. It allows users to simulate a large (order thousands of years) catalogue of tropical cyclone events that are statistically similar to the historical tropical cyclone record (or other input tropical cyclone records). TCRM has been used to evaluate wind hazard at local and regional scales to inform risk assessments and multi-hazard mapping exercises. By using data extracted from global climate models, TCRM can also be used to evaluate future changes in TC hazard and risk. Users can also simulate single TC events to evaluate impacts in near-real time to inform emergency management and response activities. The TCRM code is written in Python, and can be executed on a range of computing architectures - massively parallel systems (e.g. NCI National Facility) to desktop computers - and operating systems (currently Windows and *NIX systems). By carefully designing and developing the software, we have accommodated a wide audience of potential users.
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Modelling the effects on the built environment of natural hazards such as riverine flooding, storm surges and tsunami is critical for understanding their economic and social impact on urban communities. Geoscience Australia and the Australian National University are developing a hydrodynamic inundation modelling tool called ANUGA to help simulate the impact of these hazards.