Identification of major hydrocarbon provinces from existing world assessments for hydrocarbon potential can be used to identify those sedimentary basins at a global level that will be highly prospective for CO2 storage. Most sedimentary basins which are minor petroleum provinces and many non-petroliferous sedimentary basins will also be prospective for CO2 storage. Accurate storage potential estimates will require that each basin be assessed individually, but many of the prospective basins may have ranges from high to low prospectivity. The degree to which geological storage of CO2 will be implemented in the future will depend on the geographical and technical relationships between emission sites and storage locations, and the economic drivers that affect the implementation for each source to sink match. CO2 storage potential is a naturally occurring resource, and like any other natural resource there will be a need to provide regional access to the better sites if the full potential of the technology is to be realized. Whilst some regions of the world have a paucity of opportunities in their immediate geographic confines, others are well endowed. Some areas whilst having good storage potential in their local region may be challenged by the enormous volume of CO2 emissions that are locally generated. Hubs which centralize the collection and transport of CO2 in a region could encourage the building of longer and larger pipelines to larger and technically more viable storage sites and so reduce costs due to economies of scale.

Apatite fission track and vitrinite reflectance data suggest considerable mid to Late Cretaceous deposition in the Murray Basin area, and substantial erosion of this sediment prior to sedimentation in the Murray Basin commencing in the Late Palaeocene/Early Eocene.

Legacy product - no abstract available

30 Sept - 3 Oct., 1997, Adelaide, South Australia.

10-12 July, 2002.

The conjugate margins of Wilkes Land, Antarctica, and the Great Australian Bight (GAB) are amongst the least understood continental margins. Break up along the GAB-Wilkes Land part of the Australian-Antarctic margin commenced at approximately 83 Ma. Using recent stratigraphic interpretations developed for the GAB, we have established a sequence stratigraphy for the Wilkes Land margin that will, for the first time, allow for a unified study of the conjugate margins. By reconstructing the two margins to their positions prior to break up we were able to identify comparable packages on the Wilkes Land margin to those recognised on the GAB margin. Excluding the glacial sediments on the Antarctic margin, the sedimentary sequence along the Wilkes Land margin is very thin compared to the GAB margin, which has substantially more syn- and post-rift sediments. Despite the differences in thickness, the syn-rift sedimentary package on the Wilkes Land margin exhibits a similar style of extensional faulting and seismic character to its GAB margin counterpart. In comparison, post-rift sequences on the Wilkes Land margin are markedly different in geometry and seismic character from those found on the GAB margin. Isopach mapping shows substantial differences in the thickness of the post-breakup sediments, suggesting different sediment sources for the two margins. The Late Cretaceous Hammerhead Supersequence provides much of the post-rift thickness for the GAB margin as a result of large sediment influx into the basin. This supersequence is characterised by a thick progradational succession and was deposited in fluvio-deltaic and marine environments. The equivalent succession on the Wilkes Land margin has a different seismic character. It is thinner and aggradational, suggesting a distal marine environment of deposition.

Significant volumes of Big Lake Suite granodiorite intrude basement in the Cooper Basin region of central Australia. Thick sedimentary sequences in the Cooper and overlying Eromanga Basins provide a thermal blanketing effect resulting in elevated temperatures at depth. 3D geological maps over the region have been produced from geologically constrained 3D inversions of gravity data. These density models delineate regions of low density within the basement that are interpreted to be granitic bodies. A region was extracted from the 3D geological map and used as a test-bed for modelling the temperature, heat flow and geothermal gradients. Temperatures were generated on a discretised version of the model within GeoModeller and were solved by explicit finite difference approximation using a Gauss-Seidel iterative scheme. The thermal properties that matched existing bottom hole temperatures and heat flows measurements were applied to the larger 3D map region. An enhancement of the GeoModeller software is to allow the input thermal properties to be specified as distribution functions. Multiple thermal simulations are carried out from the supplied distributions. Statistical methods are used to yield the probability estimates of the temperature and heat flow, reducing the risk of exploring for heat.

Abstract: In most cases a single pixel in a satellite image contains information from more than one type of land cover substance. One challenge is to decompose a pixel with mixed spectral readings into a set of endmembers, and estimate the corresponding abundance fractions. The linear spectral unmixing model assumes that spectral reading of a single pixel is a linear combination of spectral readings from a set of endmembers. Most linear spectral unmixing algorithms rely on spectral signatures from endmembers in pre-defined libraries obtained from previous on-ground studies. Therefore, the applications of these algorithms are restricted to images whose extent and acquisition time coincide with those of the endmember library. We propose a linear spectral unmixing algorithm which is able to identify a set of endmembers from the actual image of the studied area. Existing spectral libraries are used as training sets to infer a model which determines the class labels of the derived image based endmembers. The advantage of such an approach is that it is capable of performing consistent spectral unmixing in areas with no established endmember libraries. Testing has been conducted on a Landsat7 ETM+ image subset of the Gwydir region acquired on Jun 22, 2008. Three types of land cover classes: bare soil, green vegetation and non-photosynthetic are specified for this test. A set consisting of 150 endmember samples and a number of ground abundance observations were obtained from a corresponding field trip. The study successfully identified an endmember set from the image for the specified land cover classes. For most test points, the spectral unmixing and estimation of the corresponding abundance are consistent with the ground validation data. From the 20th International Congress on Modelling and Simulation (MODSIM2013)

Geoscience Australia (GA) has recently completed two regional-scale Airborne Electromagnetic (AEM) surveys: one in the Paterson Region, WA; and the other in the Pine Creek region, NT. These surveys provide AEM data at line spacings of 200 m to 6 km covering an area greater than 110 000 km2. The surveys were designed to promote more detailed investigations by the mineral exploration industry. An inherent risk in using AEM surveys is that the depth of penetration of the primary electromagnetic field is highly variable. Although forward modelling is undertaken before the AEM campaign, the depth to which we can reliably invert the AEM signal to generate conductivity models is not known until after the survey is flown. In order to estimate the penetration depth of the AEM surveys, we calculate the depth of investigation (DOI) based on the GA layered-earth inversion algorithm, which is influenced by both conductivity measurements and reference model assumptions. We define the DOI as the maximum depth at which the inversion is influenced more by the conductivity data than the reference model. We present the DOI as a 2D grid across both the Paterson and Pine Creek AEM surveys. Labelled the 'AEM go-map', the DOI grid helps to promote AEM exploration by decreasing risk when industry undertakes follow-up surveys within these regions.

A new approach for the 1D inversion of AEM data has been developed. We use a reversible jump Markov Chain Monte Carlo method to perform Bayesian inference. The Earth is partitioned by a variable number of non-overlapping cells defined by a 1D Voronoi tessellation. A cell is equivalent to a layer in conventional AEM inversion and has a corresponding conductivity value. The number and the position of the cells defining the geometry of the structure with depth, as well as their conductivities, are unknowns in the inversion. The inversion is carried out with a fully non-linear parameter search method based on a transdimensional Markov chain. Many conductivity models, with variable numbers of layers, are generated via the Markov chain and information is extracted from the ensemble as a whole. The variability of the individual models in the ensemble represents the posterior distribution. Spatially averaging results is a form of 'data-driven' smoothing, without the need to impose a specific number of layers, an explicit smoothing function, or choose regularization parameters. The ensemble can also be examined to ascertain the most probable depths of the layer interfaces in the vertical structure. The method is demonstrated with synthetic time-domain AEM data. The results show that an attractive feature of this method over conventional approaches is that rigorous information about the non-uniqueness and uncertainty of the solution is obtained. We also conclude that the method will also have utility for AEM system selection and investigation of calibration problems.