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The effect of water and rock loading on seismic velocities and consequently on interpreted geometries is often underestimated in offshore studies. However, direct comparative analysis of interval velocity patterns between areas of significantly different water depth and thickness of rock overburden requires various pressure related changes in velocity to be accounted for. Presentation of velocity models as a function of pressure rather than two-way time, or depth, emerges as a possible solution. Accurate velocity model is essential for meaningful time-to-depth conversion of interpreted seismic horizons. Ideally, it should be based on integration of seismic velocities from well log measurements, refraction seismic surveys and from stacking of multi-channel marine reflection data. In some cases velocities derived from stacking of high quality long streamer marine reflection seismic data correlate reasonably with well measurements and velocities derived from refraction seismic studies, and provide clues to reasonable depth conversion and lithology interpretation.

New compilations of leveled marine and onshore gravity and magnetic data are facilitating structural and geological interpretations of the northern Perth Basin, a basin complex that formed during the Paleozoic to Mesozoic within an obliquely-oriented extensional rift system on the southwestern margin of Australia. Multi-scale edge detection facilitates the mapping of structural trends within the basin and complements and adds to interpretations based on seismic reflection data. Spectrally-based depth-to-basement estimates also aid mapping of basement architecture, while 3D gravity modeling incorporating interfaces from seismic interpretation provides a means to highlight areas where constraints are lacking or interpretation needs to be revised. However, gravity modeling is limited by the lack of knowledge on Moho geometry, thereby warranting the investigation of multiple scenarios for Moho geometry.

To follow

Geoscience Australia has developed a wind hazard model for estimating the risk posed by peak wind gusts. In this study we have utilised the regional return period wind gusts as defined in the Australian/New Zealand wind loading standard (AS/NZS 1170.2, 2002) and applied the methodology detailed in the standard. In addition, our association with Dr. John Holmes (chairperson of the Australian Wind Loading committee) allowed us to make a significant attempt to remove the conservatism associated with the wind loading standard. Geoscience Australia entered into discussions with Dr. Holmes which resulted in a consultancy that reviewed the Geoscience Australia wind risk methodology and vulnerability model development (Holmes, 2004). Geoscience Australia's basic approach is detailed in the Perth Cities report (Lin et al., 2005). In the present study we build on that earlier work by examining three other city regions and contrasting the results. Each component of the methodology is described in this section with a brief overview provided below: Estimated return period regional wind speeds (for peak 3 second gusts at a height of 10 metres in open level terrain) were obtained from AS/NZS 1170.2. The local wind effects on these return period regional wind speeds were determined by assessing the local effect of terrain at the structure height of interest, the shielding effect on the structure and the topographic effect. These effects were numerically estimated using remote sensing techniques, digital elevation data and by using formulae given in AS/NZS 1170.2. Finally, the estimation of the local wind speeds that would be equalled or exceeded within a given time period (commonly called return period wind speeds or return levels) was derived by combining the local wind multipliers (terrain/height, shielding and topographic) for 8 cardinal directions with the return period regional wind speeds (from AS/NZS 1170.2) across a 25 by 25 metre grid covering each study region.

Contains digital maps of the geology of Western Australia in various formats

These datasets cover approximately 62 sq km over the Mapoon Community and are part of the 2006 North Queensland Communities LiDAR capture project. This project, undertaken by Fugro Spatial Solutions Pty Ltd on behalf of the Queensland Government captured highly accurate elevation data using LiDAR technology. Available dataset formats (in 1 kilometre tiles) are: - Classified las (LiDAR Data Exchange Format where strikes are classified as ground or non-ground) - Ground-classified LiDAR returns in XYZ format - non-ground classified LiDAR returns in XYZ format - 1 metre Digital Elevation Model (DEM) mosaic in tif format

Diagram produced for inclusion in Limits of Continental Shelf Proclamation - Option 3 (In Confidence)

In ecology, a common form of statistical analysis relates a biological variable to variables that delineate the physical environment, typically by fitting a regression model or one of its extensions. Unfortunately, the biological data and the physical data are frequently obtained from separate data sources. In such cases there is no guarantee that the biological and physical data are co-located and the regression model cannot be used. A common and pragmatic solution is to spatially predict the physical variables at the locations of the biological variables and then use the predictions as if they were observations. In this article, we show that this procedure can cause potentially misleading ferences when fitting a generalised linear model as an example. We propose a Berkson-error model which overcomes the limitations. The differences between using predicted covariates and the Berkson error model are illustrated using data from the marine environment, and a simulation study based on this data.

This abstract is to be submitted to the Australian Society of Exploration Geophysicists for consideration as a poster to be delivered at the 22nd ASEG conference and exhibition in February 2012.