Description of construction and use of static geological models for use in the evaluation of CO2 storage potential using the Petrel Sub-basin as an example

The Australian National GNSS Infrastructure consists of the Continuously Operating Reference Stations (CORS) of the Australian Regional GNSS network (ARGN), operated by Geoscience Australia (GA), and the AuScope network operated collaboratively by GA and the State and Territory geodetic agencies. Developed to support the geospatial sector and Earth science applications, this national infrastructure underpins the national datum, the Geocentric Datum of Australia (GDA), and contributes to the Global Geodetic Observing System (GGOS) products and services, which includes the International Terrestrial Reference Frame (ITRF). To ensure this infrastructure meets needs of its users a quality management system has been developed that includes procedures for site selection, monumentation design, routine data management, and data fitness-for-purpose assessment. This presentation overviews Geoscience Australia's approach to quality management including our approach to monitoring the impact of: equipment configuration change; antenna malfunction; crustal deformation; and processing strategy and modelling changes. Some examples are given based on experience within the Asia Pacific Reference Frame (APREF) community.

Abstract for indonesian Geophysics Conference (HAGI)

In this study, airborne electromagnetics (AEM), high resolution LiDAR, and drilling (100 bores) were acquired to map and assess groundwater resources and managed aquifer recharge options in the River Darling Floodplain. Neotectonic faulting and uplift has previously been described along the north-western margin of the Murray Basin along the adjacent Darling Lineament, however no evidence of neotectonics had previously been identified in the study area. Initial inversions of the AEM data revealed a multi-layered conductivity structure broadly consistent with the hydrostratigraphy identified in drilling. However, initial laterally and spatially constrained inversions showed only moderate correlations with ground data in the near-surface (~20m). As additional information from drilling and ground and borehole geophysical surveys became available, various horizontal and vertical constraints were trialled using a new Wave Number Domain Approximate Inversion procedure with a 1D multi-layer model and constraints in 3D. The resultant 3D conductivity model revealed that an important Pleistocene aquitard (Blanchetown Clay) confining the main aquifer of interest (Calivil Formation), has an undulating surface, which is locally sharply offset. An interpreted top surface suggests that it has been affected by significant warping and faulting, as well as regional tilting due to basin subsidence or margin uplift. Overall, the top surface of the Blanchetown Clay varies in elevation by 60m. Many of the sharp offsets in the conductivity layers are coincident with lineaments observed in the LiDAR data, and with underlying basement faults mapped from airborne magnetic data. The identification of neotectonics in this area was made possible through the acquisition of high resolution AEM data, and the selection of appropriate horizontal and vertical constraints in inversion procedures. Recognition of faulting in the unconsolidated sedimentary sequence helps explain the rapid recharge of underlying Pliocene aquifers, with neotectonics recognised as a key component of the hydrogeological conceptual model.

Aquifer characterisation and groundwater resources of Dili, East Timor

Land cover data are an essential input into a wide array of models including land surface process models and weather/climate models. The Dynamic Land Cover Dataset is the first nationally consistent and thematically comprehensive land cover reference for Australia. It provides a basis for reporting on change and trends in vegetation cover and extent. The Dynamic Land Cover Dataset Version 2 is a suite of of ISO (ISO 19144-2) compliant land cover maps across the Australian landmass. The series of maps presents land cover information for every 250m by 250m area of the country for rolling two year intervals from 2001. Each map has been generated by applying a sophisticated time series analysis technique known as Dynamic Markov Chain modeling to two years of MODIS Enhanced Vegetation Index (EVI) data. The Dynamic Markov Chain modeling was used to classify each pixel based on the way that pixel has behaved over a two year period. The maps contain 33 land cover classes which reflect the structural character of vegetation, ranging from cultivated and managed land covers (crops and pastures) to natural land covers such as closed forest and open grasslands. The series of maps have been compared with over 30,000 independent ground data points provided by State, Territory and Federal Government agencies. The sequence of maps shows how Australian land cover is changing over time.

Old, Flat and Red: the Origins of the Australian Landscape Colin Pain, Geoscience Australia, Lisa Worrall, Geoscience Australia, and Brad Pillans, Research School of Earth Science, Australian National University

Indonesia is very prone to natural disasters, especially earthquakes, due to its location in a tectonically active region. In 2009, the 02 September 2009 Tasikmalaya and the 30 September 2009 earthquakes, with magnitude Mw 7.0 and 7.9, which are an intraslab earthquake, caused the deaths of thousands of people, severe infrastructure destruction and considerable economic loss. Then, the 02 October 2009 Kerinci earthquake, with magnitude Mw 6.6, is a crustal earthquake killed 3 people and destroyed hundreds of houses. Thus, both intraslab and crustal earthquakes are important sources of earthquake hazard in Indonesia.

One of the important inputs to a probabilistic seismic hazard assessment is the expected rate at which earthquakes within the study region. The rate of earthquakes is a function of the rate at which the crust is being deformed, mostly by tectonic stresses. This paper will present two contrasting methods of estimating the strain rate at the scale of the Australian continent. The first method is based on statistically analysing the recently updated national earthquake catalogue, while the second uses a geodynamic model of the Australian plate and the forces that act upon it. For the first method, we show a couple of examples of the strain rates predicted across Australia using different statistical techniques. However no matter what method is used, the measurable seismic strain rates are typically in the range of 10-16s-1 to around 10-18s-1 depending on location. By contrast, the geodynamic model predicts a much more uniform strain rate of around 10-17s-1 across the continent. The level of uniformity of the true distribution of long term strain rate in Australia is likely to be somewhere between these two extremes. Neither estimate is consistent with the Australian plate being completely rigid and free from internal deformation (i.e. a strain rate of exactly zero). This paper will also give an overview of how this kind of work affects the national earthquake hazard map and how future high precision geodetic estimates of strain rate should help to reduce the uncertainty in this important parameter for probabilistic seismic hazard assessments.

In November 2012, the Australian Government finalised a national network of Commonwealth Marine Reserves (CMR) covering 3.1 million km2 and representing the full range of large scale benthic habitats known to exist around mainland Australia. This network was designed using the best available regional-scale information, including maps of seabed geomorphic features and associated Key Ecological Features. To support the management objectives of the marine reserves, new site-specific information is required to improve our understanding of biodiversity patterns and ecosystem processes across a range of spatial scales. In this context, the Marine Biodiversity Hub (funded through the National Environmental Research Program) recently completed a collaborative 'voyage of discovery' to the Oceanic Shoals CMR in the Timor Sea. This area was chosen because it hosts globally significant levels of biodiversity (including endemic sponge and coral taxa), faces rapidly increasing pressures from human activities (offshore energy industry, fishing) yet is recognised as one of the most poorly known regions of Northern Australia. Undertaken in September 2012 on board RV Solander, the survey acquired biophysical data on the shallow seabed environments for targeted areas within the Oceanic Shoals CMR, with a focus on the carbonate banks that characterise this tropical shelf and are recognised as a Key Ecological Feature. Data collected included 500 km2 of high resolution (300 kHz) multibeam sonar bathymetry and acoustic backscatter across four grids, plus seabed sediment samples, underwater tow-video transects (~1 km length), pelagic and demersal baited underwater video, epifaunal and infaunal samples and water column profiles at pre-determined stations. Station locations were designed to provide a random but spatially balanced distribution of sample sites, with weighting toward the banks. This design also facilitated observations of patterns of benthic biodiversity at local to feature-scale and transitions associated with depth-gradients and exposure to tidal currents. Results reveal the banks are broadly circular to elliptical with steep sides, mantled by muddy sand and gravel with areas of hard ground. Rising to water depths of 50-70 m, the banks support benthic assemblages of sponges and corals (including hard corals at shallower sites) which in turn support other marine invertebrates. In strong contrast, the surrounding seabed is characterised by barren, mud-dominated sediments in 70-100 m water depth, although infaunal samples reveal diverse biological communities beneath the seafloor. While the bank assemblages are locally isolated, the potential exists for connectivity between shoals via tide-driven larval dispersal. Ongoing work is aimed at identifying species to determine overlap between bank communities, as well as modelling the sources, pathways and sinks for larvae as a proxy for understanding the physical processes controlling the patterns of biodiversity across the Oceanic Shoals CMR at multiple scales.