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  • We present a potential field litho-inversion scheme to assist with 3D geological mapping. In contrast to schemes where physical properties are the primary variables, the litho-inversion format removes the need for a post-inversion interpretation phase to distil information into the form of a geological map. Constraints are described in terms of real-world physical property characteristics and specific geological criteria rather than generic and abstract mathematical objectives. The character of the geological map is defined by the lithological categories, topological structure and the degree of boundary complexity of the starting litho-model, supplied in the form of a voxel mesh. These characteristics are preserved in all subsequent models. The lithology of specified voxels can be held fixed at the values in the start model. Each lithological unit is treated as a random mixture of one or more components, each with its own set of physical property characteristics. Global physical property parameters are supplied for each component, together with optional spatial and inter-property covariance characteristics. Faults can be simulated using a collection of voxel faces that act as discontinuities during spatial analysis. The shape and position of lithological boundaries can be controlled by comparing the boundary with a reference surface. A Bayesian approach to inversion is used. Each new model in an MCMC chain is produced by modifying the parameters for a single voxel. Different geometrical arrangements are generated by re-assigning the lithological category for voxels adjacent to lithological boundaries. Different physical property simulations are produced by re-sampling the properties from the supplied distributions. To be accepted and saved for later analysis, each candidate must pass a series of tests, one for each constraint plus one for each of the input potential field datasets. This inversion formulation allows separate inversion of either gravity or magnetic data or simultaneous inversion of both datasets.

  • Map(s) of Co (cobalt) concentration (Total content, Aqua Regia soluble content, and/or Mobile Metal Ion soluble content) in Top Outlet Sediment (TOS) and/or Bottom Outlet Sediment (BOS) samples, dry-sieved to <2 mm and/or <75 um grain size fractions. Source: The Geochemical Atlas of Australia (Caritat and Cooper, 2011)

  • Igneous rocks have long been recognised as an important source of metals in uranium mineral systems. Although magmas may form mineral deposits in their own right, they may also contribute directly to basin-related mineral systems as a source of metals and/or ligands. Thus, mapping of the distribution of uranium in igneous rocks has the potential to highlight potentially prospective regions for uranium mineralisation at a macro-scale. Map 3 in the series of three maps of the uranium content of Australian igneous rocks shows the interpreted solid geology distribution of igneous rocks. Since no nationally seamless solid geology map yet exists, datasets have been compiled from a variety of State and Territory open file sources. Polygons are coloured by their average uranium content. The average uranium content of each polygon was calculated by plotting the igneous polygons together with geochemical sample points (distribution shown in Map 1 of the series) using ArcGIS software. Each polygon was then attributed with the average uranium value (in ppm) of all intersecting geochemical sample points. This approach allows igneous uranium content to be assessed on the pluton- to province-scale, depending on polygon resolution. Furthermore, the use of solid geology datasets allows for the uranium content of igneous rocks in the subsurface to be assessed, opening up broader areas for new potential uranium mineral systems. Together with the two other maps in the series, this map demonstrates the close spatial relationship between uranium-rich igneous rocks and areas of known uranium mineralisation. In addition, new regions previously unknown for uranium mineralisation can be identified.

  • Map(s) of Pr (praseodymium) concentration (Total content, Aqua Regia soluble content, and/or Mobile Metal Ion soluble content) in Top Outlet Sediment (TOS) and/or Bottom Outlet Sediment (BOS) samples, dry-sieved to <2 mm and/or <75 um grain size fractions. Source: The Geochemical Atlas of Australia (Caritat and Cooper, 2011)

  • Marketing information flyer outlining National Geographic Information Group's (NGIG) function and capability with Geoscience Australia. Examples of capability include details on the National Elevation Data Framework (NEDF) and MapConnect - maps online. Map Connect has been decommisioned and replaced by Interactive Maps.

  • Although the positional accuracy of spatial data has long been of fundamental importance in GIS, it is still largely unknown for linear features. As early as 1987 the US National Center for Geographic Information and Analysis identified accuracy as one of the key elements of successful GIS implementation. Yet two decades later, while there is a large body of geodetic literature addressing the positional accuracy of point features, there is little research addressing the positional accuracy of linear features, and still no accepted accuracy model for linear features. This research aims to address some of these shortcomings by exploring the effect on linear feature positional accuracy of feature type, complexity, segment length, vertex proximity and 'scale'. A geographically sensible error model for linear features using point matching from a test line to a reference line of higher accuracy is developed and a case study undertaken using well-regarded and commonly used Australian topographic datasets. Half a million points are matched between test and reference lines for a range of topographic feature types at a spectrum of 'scales' and summary statistics are generated that shed light on the relationships between positional accuracy and 'scale', feature type, complexity, segment length, and vertex proximity. It is found that (a) metadata for the tested datasets significantly underestimates the positional accuracy of the data; (b) positional accuracy varies with 'scale' but not, as might be expected, in a linear fashion; (c) positional accuracy varies with feature type, but not as the rules of generalisation suggest; (d) complex features lose accuracy faster than less complex features as 'scale' is reduced; (e) the more complex a real-world feature, the worse its positional accuracy when mapped; and (f) accuracy mid-segment is poorer than accuracy end-segment.

  • Proterozoic Uranium Mineralising events on Australian Proterozoic Georgions base, 1:5 000 000 October 2007 Version (PDF and JPG)

  • Report on the activities of the administrative and technical sections in the Katherine-Darwin area, to October, 1954. A brief account is given of geological and geophysical operations. The results of prospecting and development work are summarised.

  • Map(s) of Ba (barium) concentration (Total content, Aqua Regia soluble content, and/or Mobile Metal Ion soluble content) in Top Outlet Sediment (TOS) and/or Bottom Outlet Sediment (BOS) samples, dry-sieved to <2 mm and/or <75 um grain size fractions. Source: The Geochemical Atlas of Australia (Caritat and Cooper, 2011)

  • Map(s) of Gd (gadolinium) concentration (Total content, Aqua Regia soluble content, and/or Mobile Metal Ion soluble content) in Top Outlet Sediment (TOS) and/or Bottom Outlet Sediment (BOS) samples, dry-sieved to <2 mm and/or <75 um grain size fractions. Source: The Geochemical Atlas of Australia (Caritat and Cooper, 2011)