2012
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In this study, AEM mapping validated by drilling has enabled the lateral extents and thickness of the Pliocene aquifers to be identified. The Pliocene in this area dominantly comprises the fluvial Calivil Formation, with the shallow marine Loxton-Parilla Sands restricted to the southernmost part of the area. Post-depositional warping, tilting and discrete offsets associated with neotoectonics are also recognised. Facies analysis indicates the Calivil was deposited in deep braided streams across a dissected sedimentary landscape. Overall, the sequence is fining-upwards, with evidence for progradation over the Loxton-Parilla. Channel fill materials comprise gravels and sands, and local fine-grained units represent abandoned channels and local floodplain sediments. Integration of textural and hydraulic testing data has revealed there are five hydraulic classes within the Calivil,. At a local scale (10s to 100s of metres), there is considerable lithological heterogeneity, however at a regional scale (kms), sands and gravels are widely distributed with particularly good aquifers developed in palaeochannels and at the confluence of palaeo-river systems. Aquifer testing has revealed Calivil to be an excellent aquifer, with high storage capacity, and locally very high transmissivities (up to 50 l/s). Integration of the AEM data with borehole geophysical data (gamma, induction and NMR) and textural and pore fluid data has enabled maps of aquifer properties including groundwater salinity, porosity, storage and hydraulic conductivity to be derived. Overall, the multi-disciplinary approach adopted has enabled rapid delineation of new groundwater resources, and facilitated assessment of the Pliocene aquifers for managed aquifer recharge.
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Crustal deformation in Fennoscandia is associated with the Glacial Isostatic Adjustment (GIA) process that is caused by ongoing stress release of the mantle after removal of the Late Pleistocene ice sheet by ~10 cal ka BP. With an earth model of defined structure and rheology and an ice-sheet model of known melting history, the GIA process can be simulated by geophysical models, and the surface deformation rates can be calculated and used to compare with GPS observations. Therefore, the crustal deformation rates observed by GPS in Fennoscandia provide constraints on the geophysical models. On the basis of two ice sheet models (ANU-ICE and ICE-5G) reconstructed independently by the Australian National University (ANU) and University of Toronto, we use the GPS derived deformation rates to invert for lithosphere thickness and mantle viscosity in Fennoscandia. The results show that only a three-layer earth model can be resolved from current GPS data, providing robust estimates of effective lithosphere thickness, upper and lower mantle viscosity. The earth models estimated from inversion of GPS data with two different ice sheet models define a narrow range of parameter space: the lithosphere thickness between 93~110 km, upper mantle viscosity between 3.4~5.0 × 1020 Pa s, and lower mantle viscosity between 7~13 × 1021 Pa s. The estimates are consistent with those inverted from relative sea-level indicators (Lambeck et al., 2010).
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The Stockpile information dataset contains information relating to the classification of stockpile contents. Classifications are: known backfill, known product, potential backfill, potential product.
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Maritime Delimitation under Article 1 of the Agreement on Maritime Delimitation between the Government of Australia and the Government of the French Republic (1982) Diagram AU/FR-02 Refer to GeoCat 65634 Treaty text and coordinates can be found at: http://www.austlii.edu.au/au/other/dfat/treaties/1983/3.html
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The 2011 digital elevation model (DEM) grid covers the whole of the Christmas Island. It was provided by AAM in 1km by 1km ESRI grid tiles which were then joined together using ESRI ArcMap. Each grid cell (1m by 1m) contains the height in metres of the ground surface derived from the 2011 LiDAR aerial survey data.
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In September and October of 2011 Geoscience Australia surveyed part of the offshore northern Perth Basin, in order to map potential sites of natural hydrocarbon seepage. The primary objectives of the survey were to map the spatial distribution of seepage sites and characterise the nature of the seepage at these sites (gas vs oil, macroseepage vs microseepage; palaeo vs modern day seepage) on the basis of: - acoustic signatures in the water column, shallow subsurface and on the seabed; - geochemical signatures in rock and sediment samples and the water column, and; - biological signatures on the seabed.
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The CHM grid covers the whole of the Cocos (Keeling) Islands. It was provided by AAM Pty Ltd in 1km tiles which were then joined together using ESRI ArcMap. Each grid cell (2m x 2m) contains the maximum vegetation height in metres derived from the 2011 LiDAR aerial survey data.
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Hydrogeology of East Timor
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The Japanese Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) data has 14 bands spanning: the visible and near-infrared (VNIR, 15 m pixel resolution); shortwave-infrared (SWIR, 30 m pixel resolution); and thermal infrared (TIR, 90 m pixel resolution). This wavelength range allows measurement of diagnostic spectral features for mineral groups significant for the characterisation of primary geology, metamorphic, metasomatic alteration and weathering effects. Each band in the 1-12?m region has been positioned over a diagnostic mineral group spectral feature, for example Al-clays, iron oxides, carbonates and silica (Cudahy et al., 2012). These features have been used to process ASTER scenes covering the whole of Australia into a suite of geoscience products. Continental-Scale Mineralogical Patterns The AlOH Group Composition map for South Australia (Figure 1) is a good example of how these maps can be used to characterise the surface on a large scale. In this case there is a very distinctive change from Al-smectite soils (green and red colours) in the East to the kaolinite-rich soils (blue) in the West. Most of the soils consist of Quaternary Dunes and therefore, the change in soil type probably represents a change in source material over time. Prospect-Scale Uranium Mineral System The Arkaroola region, located in the Northern Flinders Ranges, South Australia (Figure 2) provides an excellent opportunity to explore the ASTER geoscience products because the Cenozoic plains to the East and North of the escarpment contain the majority of Australia's known resources of sandstone-hosted uranium mineralisation (Skirrow, 2009) but the extent of the mineral system is unknown. The mineral system consists of the uranium-rich Proterozoic basement of the Finders Ranges as the source, oxidised uranium-bearing meteoric water travelling by gravitational energy as the pathway and reducing environments as the mechanism for uranium precipitation (Skirrow, 2009). Whilst most of the mineral system operates below the surface, there are some components that can be identified within the ASTER products, which provide information on the surface of the Earth and may indicate the presence of an underlying system. The Ferric Oxide Composition Map shows goethite-rich sediment (in blue) in contrast to hematite-rich sediment (reds to yellows) and is particularly useful in mapping transported material including paleochannels and paleovalleys (Cudahy, 2012). This product was studied in combination with a regional scale Airborne Electromagnetic (AEM) survey that was undertaken in the region by Geoscience Australia and the Geological Survey of South Australia as part of the Australian Government's Onshore Energy Security Program (OESP) in 2010. A number of palaeovalleys were identified using the data collected and one of these areas is highlighted by the black box in Figure 3. This area contains paleogene sands of medium conductivity (yellow) meeting the Pooraka Formation of low conductivity (blue). In this case the paleogene sands are the remanet valleys. Figure 3 also shows that the Ferric Oxide Composition product can distinguish these paleovalleys, as they are more hematite rich (light green) than the surrounding Pooraka Formation (blue). Therefore, this product can be used to help locate valleys in other locations.
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Australia boasts arguably the richest Late Neogene to Quaternary faulting record anywhere in the world's stable continental region (SCR) crust. Variation in Quaternary fault scarp length, vertical displacement, relation to other faults and topography justifies the division of the continent according to fault character. Six onshore 'neotectonic domains' are recognised, while an additional offshore domain is proposed by analogy with the eastern United States. Each domain relates to a distinct underlying crustal type and architecture, which can be broadly considered to represent cratonic, non-cratonic and extended crustal environments. In general, greater topographic expression associated with faults occurring in extended crust relative to non-extended crust suggests a higher rate of seismic activity in the former setting, consistent with observations worldwide. Using the same reasoning, non-cratonic crust might be expected to have a higher rate of seismic activity than cratonic crust. This distinction, together with the variance in fault character between domains, should be recognised in attempts to identify analogous systems elsewhere in the world. A common characteristic of large (paleo)earthquake occurrence in Australia appears to be temporal clustering. Periods of earthquake activity comprising a finite number of large events are separated by much longer periods of seismic quiescence. In several instances there is evidence for deformation at scales of several hundred kilometres switching on and off over the last several million years. What is not clear from the limited paleoseismological data available is whether successive active periods are comparable in terms of slip, number of events, magnitude of events, etc. Irrespective, this apparent bimodal recurrence behaviour poses problems for probabilistic seismic hazard assessment (PSHA) in that it implies that large earthquake recurrence for long return periods is not random (i.e. Poissonian). The points critical to understanding the hazard posed by SCR faults, and modelling this hazard probabilistically, become: 1) is the fault in question in the midst of an active period, or in a quiescent period; 2) how many large events might constitute an active period, and how many ruptures has the fault generated so far in its current active period (should it be in one); and 3) what is the mean recurrence interval in an active period, and what is the variability around this mean? Keywords: intra-plate, neotectonics, paleoseismology, temporal clustering