Disaster management is most effective when it is based on evidence. Evidence-based disaster management means that decision makers are better informed, and the decision making process delivers more rational, credible and objective disaster management outcomes. To achieve this, fundamental data needs to be translated into information and knowledge, before it can be put to use by the decision makers as policy, planning and implementation. Disaster can come in all forms: rapid and destructive like earthquakes and tsunamis, or gradual and destructive like drought and climate change. Tactical and strategic responses need to be based on the appropriate information to minimise impacts on the community and promote subsequent recovery. This implies a comprehensive supply of information, in order to establish the direct and indirect losses, and to establish short and long term social and economic resilience. The development of the National Exposure Information System (NEXIS) is a significant national project being undertaken by Geoscience Australia (GA). NEXIS collects, collates, manages and provides the information required to assess multi-hazard impacts. Exposure information may be defined as a suite of information relevant to all those involved in a natural disaster, including the victims, the emergency services, and the policy and planning instrumentalities.

In April 2015 Geoscience Australia (GA) acquired 908 km (full-fold) Gippsland Southern Margin Infill 2D Seismic data using Gardline's M/V Duke. The survey was designed to better resolve the Foster Fault System and provide better integration between the GDPI10 2D seismic survey and the numerous existing surveys in the central deep. The data was processed using pre-stack depth migration with deghosting.

short discussion on why and how to define lithostratigraphic units, and where to find information on describing sequence stratigraphic and regolith units.

The Australian Government policy is to ensure that uranium mining, milling and rehabilitation is based on world best practice standards. A best practice guide for in situ recovery (ISR) uranium mining has been developed to communicate the Australian Government's expectations with a view to achieving greater certainty that ISR mining projects meet Australian Government policy and consistency in the assessment of ISR mine proposals within multiple government regulatory processes. The guide focuses on the main perceived risks; impacts on groundwaters, disposal of mining residues, and radiation protection. World best practice does not amount to a universal template for ISR mining because the characteristics of individual ore bodies determine the best practice

We measured the light absorption properties of two naturally occurring Australian hydrocarbon oils, a Gippsland light crude oil and a North West Shelf light condensate. Using these results in conjunction with estimated sensor environmental noise thresholds, the theoretical minimum limit of detectability of each oil type (as a function of oil thickness) was calculated for both the hyperspectral HYMAP and multispectral Quickbird sensors. The Gippsland crude oil is discernable at layer thickness of 20 micro metres or more in the Quickbird green channel. The HYMAP sensor was found to be theoretically capable of detecting a layer of Gippsland crude oil with a thickness of 10 micro metres in approximately six sensor channels. By contrast, the North West Shelf light condensate was not able to be detected by either sensor for any thickness up to 200 icro metres. Optical remote sensing is therefore not applicable for detecting diagnostic absorption features associated with this light condensate oil type, which is considered representative for the prospective Australian Northwest Shelf area. We conclude that oil type is critical to the applicability of optical remote sensing for natural oil slick detection and identification. We recommend that a sensor- and oil-specific sensitivity study should be conducted prior to applying optical remote sensors for oil exploration. The oil optical properties were obtained using two different laboratory methods, a reflectance-based approach and transmittance-based approach. The reflectance-based approach was relatively complex to implement, but was chosen in order to replicate as closely as possible real world remote sensing measurement conditions of an oil film on water. The transmittance-based approach, based upon standard laboratory spectrophotometric measurements was found to generate results in good agreement with the reflectance-based approach. Therefore, for future oil- and sensor-specific sensitivity studies, we recommend the relatively accessible transmittance-based approach, which is detailed in this paper.

In a collaborative effort with the regional sub-commissions within IAG sub-commission 1.3 'Regional Reference Frames', the IAG Working Group (WG) on 'Regional Dense Velocity Fields' (see http://epncb.oma.be/IAG) has made a first attempt to create a dense global velocity field. GNSS-based velocity solutions for more than 6000 continuous and episodic GNSS tracking stations, were proposed to the WG in reply to the first call for participation issued in November 2008. The combination of a part of these solutions was done in a two-step approach: first at the regional level, and secondly at the global level. Comparisons between different velocity solutions show an RMS agreement between 0.3 mm/yr and 0.5 mm/yr resp. for the horizontal and vertical velocities. In some cases, significant disagreements between the velocities of some of the networks are seen, but these are primarily caused by the inconsistent handling of discontinuity epochs and solution numbers. In the future, the WG will re-visit the procedures in order to develop a combination process that is efficient, automated, transparent, and not more complex than it needs to be.

Overview of the Deep Crustal Seismic surveys conducted by Geoscience Australia and funded through the Onshore Energy Security Program since it's commencement in 2006 to September 2009.

This paper presents a model to assess bushfire hazard in south-eastern Australia. The model utilises climate model simulations instead of observational data. Bushfire hazard is assessed by calculating return periods of the McArthur Forest Fires Danger Index (FFDI). The return periods of the FFDI are calculated by fitting an extreme value distribution to the tail of the FFDI data. The results have been compared against a spatial distribution of bushfire hazard obtained by interpolation of FFDI calculated at a number of recording stations in Australia. The results show that climate simulations produce a similar pattern of bushfire hazard than the interpolated observations but the simulated values tend to be up to 60% lower than the observations. This study shows that the major source of error in the simulations is the values of wind speed. Observational wind speed is recorded at a point-based station whilst climate simulated wind speed is averaged over a grid cell. On the other hand FFDI calculation is very sensitive to wind speed and hence to improve the calculation of FFDI using climate simulations it is necessary to correct the bias observed in the simulations. A statistically-based procedure to correct the simulation bias has been developed in this project. Bias-corrected calculation of FFDI shows that the major bushfire hazard in south-eastern Australia is in the western parts of SA and NSW; and in south-western Tasmania.

The sensitivity of the Jaiswal and Wald (Earthq. Spectra, 2010) empirical earthquake fatality model is evaluated relative to the model space for a suite of macroseismic intensity prediction methods. The relative difference between intensity prediction methods is shown through the use of self-organizing maps to visualize high-dimensional ground shaking data in a two-dimensional space. Among all the macroseismic intensity prediction methods evaluated, there is significant variability in the resulting loss estimates for an earthquake of given source parameters with losses being most sensitive to those intensity models that predict high near-source ground shaking. Because the empirical fatality models evaluated herein are based on a consistent suite of ground-motion model inputs, application of the fatality models with other intensity prediction methods may result in undesirable outcomes. Consequently, it is recommend that empirical loss models be calibrated directly with hazard inputs used in the proposed loss assessment methodology.

Can you help the Geoscience Australia Library? We are seeking the field notebooks of any geologists who worked for the Bureau of Mineral Resources (BMR) in Antarctica, especially those from the 1950s-80s, to include in our digitisation project. Tucked away in archive boxes in the basement compactus of the Geoscience Australia Library in Canberra, lie over 3500 geol ogical field notebooks. These notebooks contain the observations of BMR geologists from the 1940s onwards as they worked their way across Australia, parts of Papua New Guinea and Pakistan, and the Australian Antarctic Territory. Around 100 Antarctic field notebooks are the focus of a pilot digitisation project to improve access to the rich data they contain and ensure they are preserved for future generations to use.