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.

This metadata sheet refers to the following three shapefiles: flight_lines_ci11.shp photo_centres_ci11.shp photo_rectangles_ci2011.shp They can all be found in the following directory: \CIGIS\orthophoto\ortho2011 Together these datasets show the flight lines flown by AAM during the 2011 aerial survey of Christmas Island, the centre of each aerial photograph taken during flight and approximate photograph extent rectangles.

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.

Contains digital maps of the geology of the Northern Territory in various formats

These datasets cover approximately 790 sq km in the eastern sector of the Whitsunday Regional Council and over Hayman and Whitsunday Islands and are part of the 2009 Mackay 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 2 kilometre tiles) are: - Classified las (LiDAR Data Exchange Format where strikes are classified as ground, non-ground or building) - Unclassified las - 1 metre Digital Elevation Model (DEM) in ASCII xyz - 1 metre Digital Elevation Model (DEM) in ESRI ASCII grid - 1 metre Digital Elevation Model (DEM) in ESRI Binary grid - 0.25 metre contours in ESRI Shape - LiDAR ground-classified returns in XYZ format - LiDAR non-ground-classified returns in XYZ format

60 colour maps depicting the graticular blocks subject to revision of the territorial sea baseline. These maps cover the whole of Australia including Macquarie and Lord Howe Islands. Not for sale or public distribution Contact Manager, LOSAMBA project, EGD

Total magnetic intensity (TMI) data measures variations in the intensity of the Earth magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The data are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. This magnetic grid has a cell size of 0.002 degrees (approximately 210m). The data used to produce this grid was acquired in UNKNOWN by the WA Government, and consisted of UNKNOWN line-kilometres of data at 150.0m line spacing and 50.0m terrain clearance.

A document briefly describing levelled ship-track gravity and magnetic data available for download from the Geophysical Archive Data Delivery System (GADDS) that cover the southwest margin of Australia in the region enclosing 106-120°E and 19-37°S. The document will be provided whenever the data are downloaded from GADDS. Note that the data is archived on the Corporate Data Store

Between the 24th and 26th of August 2011 aerial photography of Christmas Island was obtained by AAM using light aircraft. The imagery has a 15cm on ground pixel size resolution. This metadata sheet refers to the full imagery mosaic of Christmas Island (CI_2011_orth.ecw). AAM also provided the imagery to Geoscience Australia as 1km ecw and tiff tiles (196 tiles), along with the raw non-georeferenced images. Only the Christmas Island full mosaic is provided in the CIGIS package. Other data can be requested from Geoscience Australia.

Geoscience Australia (GA) conducts post-disaster field surveys to understand the vulnerability of buildings and infrastructure to natural disasters. GA has developed a range of tools to assist with exposure, damage capture and analysis. In this presentation, two open-source tools are discussed. The Rapid Inventory Collection System (RICS), due to be released shortly as open-source, is a vehicular data collection system used to capture and save geo-tagged imagery of damaged structures. RICS permits 100% coverage of building damage in a disaster-affected area. This enables fieldwork to be undertaken more efficiently than a traditional foot survey. The system consists of Ethernet cameras, a GPS receiver and software written in C++. The software is multi-threaded and uses wxWidgets, wxThreads, Boost and SQLite. The software was developed by a single software engineer using an iterative development process. Each iteration included requirements gathering, design, coding, and field/system testing. RICS has proven to be very robust and has been used operationally following the 2009 Victorian Bushfires, the 2010 Kalgoorlie and Christchurch Earthquakes, 2011 Brisbane floods and Tropical Cyclone Yasi to collect geo-tagged imagery of structures and buildings. The processing of RICS imagery, alongside other information sources such as satellite imagery, can be a significant task. Ideally, all information sources should be readily available to a user on a common user interface for analysis. To enable this, the Field Data Analysis Tool (FiDAT) is being developed. FiDAT will enable a user to view a wide range of data sources on a user interface to extract data on the nature and damage to an individual structure. The data can be updated, modified, and/or corrected to accurately reflect the severity of damage to a building. FiDAT is being developed in Python using an iterative development process. The beta version of this tool is due for completion in June 2012.