In response to the devastating Indian Ocean Tsunami (IOT) that occurred on the 26th of December 2004, Geoscience Australia developed a framework for tsunami risk modelling. The outputs from this methodology have been used by emergency managers throughout Australia. For GA to be confident in the information that is being provided to the various stakeholders' validation of the model and methodology is required. While the huge loss of life from the tsunami was tragic, the IOT did provide a unique opportunity to record the impact of a tsunami on the coast of Western Australia. Eight months after the tsunami a post-disaster survey was conducted at various locations along the coast and maximum run-up was determined from direct observational evidence or anecdotal accounts. In addition tide gauges located in harbours along the coast also recorded the tsunami and provide a timeseries account of the wave heights and frequency of the event. This study employs the tsunami hazard modelling methodology used by Geoscience Australia (GA) to simulate a tsunami scenario based on the source parameters obtained from the Boxing Day earthquake of 2004. The model results are compared to observational evidence from satellite altimetry, inundation surveys and tide gauge data for Geraldton, a community on the Western Australian coast. Results show that the tsunami model provides good estimates of the wave height in deep water and also run up in inundated areas and it importantly matches the timing of the first wave arrivals. However the model fails to reproduce the timeseries data of wave heights observed by a tide gauge in Geraldton harbour. The model does however replicate the occurrence of a late arriving (16 hrs after first arrival) wave packet of high frequency waves. This observation is encouraging since this particular wave packet has been noted elsewhere in the Indian Ocean and caused havoc in harbours many hours after the initial waves had arrived and dissipated.

A regional scale 3D geological map of the upper crustal sequence in the West Arnhem Land region, Northern Territory, was compiled from surface mapping, limited drilling information, and liberal amounts of geological inference. Modelling of the gravity and magnetic field response of this map was proposed as a means of evaluating the viability of this geological hypothesis. A relatively good supply of mass density and magnetic property data were available to constrain the transformation of 3D geological maps into property models in preparation for potential field modelling. The presence of numerous relatively thin magnetic horizons, dykes, and sills provided many challenges for producing geologically-realistic magnetic property models at a regional scale. Modelling of the gravity field at this scale was far more straight forward and successful. A stochastic procedure was used to derive a large number of geological maps by making small changes to the highly uncertain interpretive parts of the original 3D geological map. A subset of these derived geological maps had associated mass density models that could adequately reproduce the gravity field observations. The common characteristics of the geological models in this subset were isolated using statistical techniques and used to refine our portrayal of the regional scale 3D geological features.

This Agreements ontology is designed to model 'agreements' which are social contracts that include: licenses, laws, contracts, Memoranda of Understanding, standards and definitional metadata. Its purpose is to support data sharing by making explicit the relationships between agreements and data and agreements and Agents (people and organisations). Eventually it will also help with the interplay between different classes of agreements. We think of this ontology as a 'middle' ontology, that is one which specializes well-known, abstract, upper ontologies and is able to be used fairly widely but is expected to be used particular contexts in conjunction with detailed, domain-specific, lower ontologies. We have tried to rely on: existing agent, data manipulation, metadata and licence ontologies where possible. As such we specialise the ORG and FOAF ontologies; the PROV ontology; the Dublin Core Terms RDF schema & DCAT ontology; and the ODRS vocabulary & Creative Commons RDF data models for those areas, respectively

Tsunami inundation models are computationally intensive and require high resolution elevation data in the nearshore and coastal environment. In general this limits their practical application to scenario assessments at discrete communities. This paper explores the use of moderate resolution (250 m) bathymetry data to support computationally cheaper modelling to assess nearshore tsunami hazard. Comparison with high resolution models using best available elevation data demonstrates that moderate resolution models are valid at depths greater than 10 m in areas of relatively low sloping, uniform shelf environments, however in steeper and more complex shelf environments they are only valid to depths of 20 m or greater. In contrast, arrival times show much less sensitivity to resolution. It is demonstrated that modelling using 250 m resolution data can be useful in assisting emergency managers and planners to prioritise communities for more detailed inundation modelling by reducing uncertainty surrounding the effects of shelf morphology on tsunami propagation. However, it is not valid for modelling tsunami inundation.

The subsurface of the Earth is a complex system, one that we are yet to fully understand and model. It is hence impossible to automate the process of mapping and modelling, and the input of user experience and knowledge ('prior knowledge') is required to produce meaningful and useful outputs. This form of solution does not lend itself to a simple programmatic approach. However, by taking advantage of advances in computer technology and the application of numerical methods for modeling complex environments, we can do much to improve upon past results. Introduction As Australia's national geoscience organisation, Geoscience Australia (GA) plays an important role in the creation and delivery of fundamental geoscientific information. Studies are carried out at a wide range of scales, from a continental perspective to highly detailed local site investigations. In most situations, direct geological observations are supplemented by the inferences that can be made from geophysical measurements. Observations of the Earth's gravity and magnetic fields contain signals from subsurface materials, and extensive holdings of these measurements are commonly used to help create 3D subsurface models. With sparse hard constraints and incomplete, insufficient, noisy observations, knowledge workers or experts continue to play an important role in providing implicit prior constraints on any system to model this volume. The interface to these people becomes an important part of any set of tools for performing geological modeling of gravity and magnetic data. Users constantly demand a better experience and better outcomes when modeling the subsurface. Some of their recurring requests are for: * A simpler, more intuitive user-experience * Higher resolution * Models with larger extents * Faster processing * Inclusion of a greater number of geological and rock property constraints * Estimates of the uncertainty in the outcomes * Improved 3D visualisation * Tracking of input provenance and subsequent processing that is carried out * Organised management of 3D models Integration of the elements is a key consideration when developing solutions, as users are loathe to adopt procedures that become more involved and more difficult to understand and to piece together. Today, developments to produce world-class solutions typically take place across multiple agencies, involving many people, and at locations spread around the globe. This in itself is a challenge! We have focused our efforts on the following: * The management and delivery of rock properties * Spherical and Cartesian coordinate gravity and magnetic modelling software * Use of High Performance Computing (HPC) facilities * Use of a virtual globe application for 3D visualisation

Despite growing concerns about potential enhancement of global warming and slope failure by methane produced by gas hydrate dissociation, much uncertainty surrounds estimates of gas hydrate reservoir sizes, as well as methane fluxes and oxidation rates at the sea floor. For cold seep sediments of the eastern Mediterranean Sea, depth-dependent methane concentrations and rates of anaerobic oxidation of methane (AOM) are constrained by modeling the measured pore-water sulfate profile. The calculated dissolved methane distribution and flux are sensitive to the advective flow velocity, which is estimated from the depth distributions of conservative pore-water constituents (Na, B). Near-complete anaerobic oxidation of the upward methane flux is supported by the depth distributions of indicative biomarkers, and the carbon isotopic compositions of organic matter and dissolved inorganic carbon. Pore-water and solid-phase data are consistent with a narrow depth interval of AOM, 14-18 cm below the sediment-water interface. Based on an isotopic mass balance, the biomass of the microbial population carrying out oxidation of methane coupled to sulfate reduction at the given methane flux represents about 20% of the total organic carbon, which is a significant pool of in situ formed organic matter. Model results indicate that the asymptotic methane concentration is reached a few meters below the sediment surface. The predicted asymptotic concentration is close to the in situ saturation value with respect to gas hydrate, suggesting that the rate of shallow gas hydrate formation is controlled by the ascending methane flux. The proposed model approach can be used to predict the formation of gas hydrate, and to quantify methane fluxes plus transformation rates in surface sediments where fluid advection is an important transport mechanism.

The sediment-hosted Nifty Cu deposit is located 450 km east of Port Hedland in the Yeneena Basin of the Paterson Orogen in Western Australia. It is hosted within interbedded black carbonaceous shales and dolomitised micrites of the Broadhurst Formation. The host rocks have been folded and metamorphosed to lower greenschist facies in the Miles Orogeny (see also Czarnota et al. this volume). Textural relationships of the ore to host rock suggest syn-deformational (Miles Orogeny) timing of mineralisation (see also van der Wacht et al., this volume). Primary chalcopyrite preferentially replaces dolomitised micrite beds, occurs in black shales within the axial plane foliation, or as breccia infill. The ore and silica dolomite alteration envelopes trend from the keel of the Nifty Syncline and up the steeply dipping limb of the fold. There are two high grade ore trends (>1% Cu): one strikes NE-SW parallel to the fold axis and the other strikes N-S across the axis of the fold. Based on the inference that Nifty is a structurally controlled deposit that formed late, or after the establishment of the fold architecture, the question is why high grade ore is located in the keel and towards one limb of the asymmetric Nifty syncline. Assuming that post-folding dilation focussed flow of mineralising fluid(s) 2D and 3D coupled deformation/fluid flow simulations were carried out to examine why Nifty is in a syncline and what the controls on high grade ore trends may be. 2D models The 2D model geometry consists of a three layer stratigraphy folded in a series of asymmetric folds. The three-layer model represents the camp scale lithostratigraphy consisting of (i) a moderately competent and moderately permeable siltstone, (ii) a strong and permeable carbonate and (iii) a weak and impermeable shale. Contraction at hydrostatic pore pressure of this material layering resulted in focuses fluid flow down fluid pressure gradient occurred from the hinge of the syncline and up the steeply dipping limb of the fold driven by dilation higher up the limb. This dilation is a consequence of the location of a shear band that developed along the shallow dipping limb of the fold, above the competent carbonate unit, and intersected the steeply dipping limb of the syncline, adjacent to the syncline hinge. Models run using the same geometry but varying the stratigraphy to the mine sequence of shale-carbonate-shale showed focusing of fluid flow into anticlinal fold closures. This is a consequence of shear strain localisation below the competent carbonate unit and the intersection of the resultant shear band with the carbonate unit adjacent to the anticlinal fold closure. This scenario does not explain why Nifty is in a syncline. However this model may explain why the Telfer Ore deposit hosted in sediments with a similar competency contrast to this model (i.e. a sandstone unit between two weak carbonate units) is situated in a dome fold closure adjacent to the steeply dipping limb of the fold. Other models run on symmetrical folds showed similar results as the two models outlined above. However the shear bands in these models do not have preferential shallowly dipping fold limbs to localise on. 3D models A simple three layer 3D model of the Nifty syncline was constructed to examine the effects of (i) the nearby Vines Fault which was active during the Miles Orogeny as a major dextral strike-slip fault and (ii) the effects of the NW-SE directed Paterson Orogeny. The results of applying a dextral strike-slip velocity boundary velocity parallel to the NNW orientation of the Vines Fault produced high strain zones and associated dilation broadly coincident with the second direction of high grade ore trends. Deformation under the Paterson stress field i.e. perpendicular to the fold axis, resulted in shear strain localisation along inflections in the fold axis away from regions of mineralisation....

Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, especially its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.

The inversion analyses presented in our paper and now extended in this Reply were ultimately only one part of the AEM system selection process for the BHMAR project. Both Derivative and Inversion analyses are in their nature theoretical, and it is impossible, in a theoretical analysis, to capture all of the aspects relevant for real surveys with little margin for error in political time frames. In reality, neither the Derivative nor Inversion analysis provided the degree of certainty required (by the project manager and client) to ascertain whether any of the candidate AEM systems were able to map the key managed aquifer recharge targets recognized in the study area. Consequently, a decision was made to acquire data over a test line with the 2 systems (SkyTEM and TEMPEST) that performed best in the Derivative and Inversion analysis studies. This approach was vindicated with quite distinctive and very different performance observed between these two systems, especially when compared with borehole and ground geophysical and hydrogeological data over known targets. Data were inverted both with contractors' software and with reference software common to all systems and the results were compared. Ultimately, it was the test line, particularly in the near-surface (top 20metres), thatmade the SkyTEM system stand out as the best system for the particular targets in the project area. SkyTEM mapped the key multi-layered hydrostratigraphy and water quality variability in the key aquifer that defined the key MAR targets, although the TEMPEST system had a superior performance at depths exceeding 100metres. Importantly, the SkyTEM system also mapped numerous, subtle fault-offsets in the shallow near-surface. These structures were critical to mapping recharge and inter-aquifer leakage pathways. Further analysis has demonstrated that selection of the most appropriate AEM system and inversion can result in order of magnitude differences in estimates of potential groundwater resources. The acquisition of SkyTEM data was an outstanding success, demonstrating the capability of AEM systems to provide high-resolution data for the rapid mapping and assessment of groundwater and strategic aquifer storages in Australia's complex and highly salinized floodplain environments. The SkyTEM data were used successfully to identify 14 major new groundwater targets and multiple MAR targets, and these have been validated by an extensive drilling program (Lawrie et al., 2012a-e). Increasingly, the demand from clients for higher certainty in project decision making, and quantifying errors, will see development of new system comparative analysis approaches such as the Inversion analysis approach documented in our initial paper. Ultimately, system fly-offs are likely in high-profile projects where budgets permit.

During 2009-11 Geoscience Australia completed a petroleum prospectivity study of the offshore northern Perth Basin as part of the Australian Government's Offshore Energy Security Program. A significant component of the program was the acquisition of a regional 2D reflection seismic and potential field survey GA-310 in 2008/09. Basement in the northern Perth Basin is deep and generally not resolved in the reflection seismic data. This study models the observed gravity in 2.5D along two southwest trending dip-direction reflection seismic transects across WA11-18 to provide insight into the likely sediment thickness and basement topography. Three cases and ten models are examined according to assumptions about possible target depth to basement, and assumptions about Moho depth