Stations on the Australian continent receive a rich mixture of ambient seismic noise from the surrounding oceans and the numerous small earthquakes in the earthquake belts to the north in Indonesia, and east in Tonga-Kermadec, as well as more distant source zones. The noise field at a seismic station contains information about the structure in the vicinity of the site, and this can be exploited by applying an autocorrelation procedure to the continuous records. By creating stacked autocorrelograms of the ground motion at a single station, information on crust properties can be extracted in the form of a signal that includes the crustal reflection response convolved with the autocorrelation of the combined effect of source excitation and the instrument response. After applying suitable high pass filtering the reflection component can be extracted to reveal the most prominent reflectors in the lower crust, which often correspond to the reflection at the Moho. Because the reflection signal is stacked from arrivals from a wide range of slownesses, the reflection response is somewhat diffuse, but still sufficient to provide useful constraints on the local crust beneath a seismic station. Continuous vertical component records from 223 stations (permanent and temporary) across the continent have been processed using autocorrelograms of running windows 6 hours long with subsequent stacking. A distinctive pulse with a time offset between 8 and 30 s from zero is found in the autocorrelation results, with frequency content between 1.5 and 4 Hz suggesting P-wave multiples trapped in the crust. Synthetic modelling, with control of multiple phases, shows that a local Ppmp phase can be recovered with the autocorrelation approach. This approach can be used for crustal property extraction using just vertical component records, and effective results can be obtained with temporary deployments of just a few months.

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 eparate sources of data. 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 predict the physical variables at the locations of the biological variables and then to use the predictions as if they were observations.We show that this procedure can cause potentially misleading inferences and we use generalized linear models 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 by using data from the marine environment, and a simulation study based on these data.

Mineral deposits are a product of the coincidence of favourable geological conditions within a given spatial and temporal setting. Collectively, these key geological elements may be considered as aspects of a mineral system. Mineral system-based investigations of the potential for a range of uranium systems have recently been undertaken in northern Queensland, east-central South Australia and the southern Northern Territory. Building on the methodology employed in northern Queensland, the mineral system assessment in South Australia and the Northern Territory consists of four key system components: (1) sources of metals and fluids, (2) drivers of fluid flow, (3) fluid pathways and architecture, and (4) depositional sites and mechanisms. Favourable geological criteria are developed from these four components, which are in turn translated into mappable geological proxies. Thus, the mineral systems framework drives the collection and synthesis of geoscientific data. This approach minimises the influence of localised geological controls, which may only be significant at the mine scale, and allows the system to be mapped on a broad scale, maximising the 'footprint' of mineralisation. Locations of known mineralisation are not considered in the assessment but are used to verify results. By employing a systems-based approach, the potential of relatively unexplored areas may be assessed objectively, transparently and systematically. Significantly, the approach used here is able to predict the potential for unrecognised mineralisation beneath cover. The assessments undertaken for uranium potential successfully reproduce the location of known uranium deposits and, importantly, delineate several areas where uranium mineralisation is currently unknown.

Abstract # : 1479734 Paper # : GP43B-1142 Session : GP43B Potential-field and EM methods for geologic problems of the mid and upper crust Developments for 3D gravity and magnetic modeling in spherical coordinates Richard Lane - Geoscience Australia - rjllane@gmail.com Qing Liang - China University of Geosciences (Wuhan) - qingliang.cug@gmail.com Chao Chen - China University of Geosciences (Wuhan) - chenchao@cug.edu.cn Yaoguo Li - Colorado School of Mines - ygli@mines.edu At Geoscience Australia (GA), Australia's Commonwealth Government geoscientific agency, we perform gravity and magnetic modeling at a range of scales, from broad regional crustal studies with thousands of kilometer lateral extent and tens of kilometer vertical extent, to detailed local studies with kilometer or less lateral extent and meters to hundreds of meters vertical extent. To achieve greater integration and coherence, and to better understand the geological significance of this work, we are investing in a number of development projects; * Spherical coordinate gravity and magnetic modeling, * Modeling using High Performance Computing facilities, * Utilizing rock property data as an input into the modeling and interpretation of gravity and magnetic data, * Better management of geoscience data and models, and * Visualization of spatial data in a Virtual Globe format. In collaboration with the Colorado School of Mines (CSM) and the China University of Geosciences (CUG), we are developing a capability to model gravity and magnetic data in a spherical coordinate framework. This will provide more accurate calculations and permit us to integrate the results into a single framework that more realistically reflects the shape of the Earth. Modeling gravity and magnetic data in a spherical coordinate framework is far more compute intensive than is the case when performing the corresponding calculations in a Cartesian (rectangular) coordinate framework. To reduce the time required to perform the calculations in a spherical coordinate framework, we will be deploying the modeling software on the National Computational Infrastructure (NCI) High Performance Computing (HPC) facility at the Australian National University (ANU). This will also streamline the management of these software relative to the other main option of establishing and maintaining HPC facilities in-house. We are a participant in the Deep Exploration Technologies Cooperative Research Centre (DET CRC). In combination with this involvement, we are expanding our support for systematic management of rock property data, and developing a better understanding of how these data can be used to provide constraints for the modeling work. We are also using the opportunities afforded through the DET CRC to make progress with documentation and standardization of data storage and transfer formats so that the tasks of management, discovery and delivery of this information to users are simplified and made more efficient. To provide the foundations of integration and analysis of information in a spatial context, we are utilizing and customizing 3D visualization software using a Virtual Globe application, NASA World Wind. This will permit us to view the full range of information types at global to local scales in a realistic coordinate framework. Together, these various development activities will play an important role in the on-going effort by Geoscience Australia to add value to the potential field, rock property, and geological information that we possess. We will then be better able to understand the geology of the Australian region and use this knowledge in a range of applications, including mineral and energy exploration, natural hazard mitigation, and groundwater management.

The digital elevation model (DEM) grid covers the whole of the Cocos (Keeling) Islands. It was provided by AAM in 1km by 1km tiles which were then joined together using ESRI ArcMap. Each grid cell (1m x 1m) contains the height, in metres, of the ground surface derived from the 2011 LiDAR aerial survey data.

Tropical Cyclone (TC) Yasi crossed Queensland's Cassowary Coast during the night of the 2nd and 3rd of February, 2011. The cyclone was forecast by BoM (2011) to be a severe storm with wind gusts forecast to exceed the design gust wind speeds for houses set out in AS4055. Following the passage of the cyclone, it was evident that the severe wind and large coastal storm surge had caused significant damage to the region's building stock. Geoscience Australia (GA), together with collaborators from the National Institute of Water and Atmospheric Research, New Zealand (NIWA), Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and Maddocks & Associates, undertook a survey of damage to the region's buildings caused by severe wind and storm surge.

The IAG Working Group (WG) 'Integration of Dense Velocity Fields in the ITRF' was created in 2011 as follow-up of the WG 'Regional Dense Velocity Fields' (2007-2011). The goal of the WG group is to densify the ITRF (International Terrestrial Reference Frame) using regional GNSS solutions as well as global solutions. This was originally done by combining several cumulative position/velocity solutions as well as their residual position time series submitted to the WG by the IAG regional reference frame sub-commissions (APREF, EUREF, SIRGAS, NAREF) and global (ULR) analysis centers. However, several test combinations together with the comparison of the residual position time series demonstrated the limitations of this approach. In June 2012, the WG decided to adopt a new approach based on a weekly combination of the GNSS solutions. This new approach will mitigate network effects, have a full control over the discontinuities and the velocity constraints, manage the different data span and derive residual position time series in addition to a velocity field. All initial contributors have agreed to submit weekly solutions and in addition initial contacts have been made with other sub-commissions particularly Africa in order to extent the densified velocity field to all continents. More details on the WG are available from http://epncb.oma.be/IAG/.

Australia has a rich uranium endowment. Amongst other favourable geological conditions for the formation of uranium deposits, such as the presence of intracratonic sedimentary basins, Australia is host to widespread uranium-rich felsic igneous rocks spanning a wide range of geological time. Many known uranium deposits have an empirical spatial relationship with such rocks. While formation of some mineral systems is closely associated with the emplacement of uranium-rich felsic magmas (e.g., the super-giant Olympic Dam deposit), most other systems have resulted from subsequent low temperature processes occurring in spatial proximity to these rocks. Approximately 91% of Australia's initial in-ground resources of uranium occur in two main types of deposits: iron-oxide breccia complex deposits (~ 75%) and unconformity-related deposits (~ 16%). Other significant resources are associated with sandstone- (~ 5%) and calcrete-hosted (~ 1%) deposits. By comparison, uranium deposits associated with orthomagmatic and magmatic-hydrothermal uranium systems are rare. Given the paucity of modern exploration and the favourable geological conditions with Australia, there remains significant potential for undiscovered uranium deposits. This paper discusses mineral potential of magmatic- and basin-related uranium systems.

Map showing the whole extent of Australia's Maritime Jurisdiction. Produced for the Australian Customs Service (Border Protection) with simplified legend showing 2012 confirmed CS and unconfirmed areas. Represented in LOSAMBA base products data as "simplified_maritime_jurisdiction_November2012.jpg" files. Also in directory for Border Protection - Task 661 - GeoCat73979. Also Refer to latest GeoCat 74928 (without SAR zone)

Double-sided A3 map showing Australia's major petroleum resources and pipelines (one side) and current onshore and offshore petroleum exploration licences plus the location of the proposed offshore 2012 acreage release areas (flip-side). This map is mainly used as a promotional tool for the international NAPE exhibition.