This final paper for the session presents the results of the new draft earthquake hazard assessment for Australia and compares them to the previous AS1170.4 hazard values. Draft hazard maps will be presented for several spectral periods (0.0, 0.2 and 1.0 s) at multiple return periods (500, 2500 and 10,000 years). These maps will be compared with both the current earthquake hazard used in AS1170.4 and with other assessments of earthquake hazard in Australia. In general the hazard in the draft map is higher in the western cratonic parts of Australia than it is in the eastern non-cratonic parts of Australia. Where regional source zones are included, peaks in hazard values in the map are generally comparable to those in the current AS1170.4 map. When seismicity 'hotspot zones are included, as described in the previous paper, several of them produce much higher hazard peaks than any in the AS1170.4 map. However, such hotspots do not affect as large an area as many of those in the current AS1170.4 map. Finally, hazard curves for different cities will also be presented and compared to those predicted by the method outlined in AS1170.4.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.

A key component of Geoscience Australia's marine program involves developing products that contain spatial information about the seabed for Australia's marine jurisdiction. This spatial information is derived from sparse or unevenly distributed samples collected over a number of years using many different sampling methods. Spatial interpolation methods are used for generating spatially continuous information from the point samples. These methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Machine learning methods, like random forest (RF) and support vector machine (SVM), have proven to be among the most accurate methods in disciplines such as bioinformatics and terrestrial ecology. However, they have been rarely previously applied to the spatial interpolation of environmental variables using point samples. To improve the accuracy of spatial interpolations to better represent the seabed environment for a variety of applications, including prediction of biodiversity and surrogacy research, Geoscience Australia has conducted two simulation experiments to compare the performance of 14 mathematical and statistical methods to predict seabed mud content for three regions (i.e., Southwest, North, Northeast) of Australia's marine jurisdiction Since 2008. This study confirms the effectiveness of applying machine learning methods to spatial data interpolation, especially in combination with OK or IDS, and also confirms the effectiveness of averaging the predictions of these combined methods. Moreover, an alternative source of methods for spatial interpolation of both marine and terrestrial environmental properties using point survey samples has been identified, with associated improvements in accuracy over commonly used methods.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.

The development of the Indian Ocean Tsunami Warning and mitigation System (IOTWS) has occurred rapidly over the past few years and there are now a number of centres that perform tsunami modelling within the Indian Ocean, both for risk assessment and for the provision of forecasts and warnings. The aim of this work is to determine to what extent event-specific tsunami forecasts from different numerical forecast systems differ. This will have implications for the inter-operability of the IOTWS. Forecasts from eight separate tsunami forecast systems are considered. Eight hypothetical earthquake scenarios within the Indian Ocean and ten output points at a range of depths were defined. Each forecast centre provided, where possible, time series of sea-level elevation for each of the scenarios at each location. Comparison of the resulting time series shows that the main details of the tsunami forecast, such as arrival times and characteristics of the leading waves are similar. However, there is considerable variability in the value of the maximum amplitude (hmax) for each event and on average, the standard deviation of hmax is approximately 70% of the mean. This variability is likely due to differences in the implementations of the forecast systems, such as different numerical models, specification of initial conditions, bathymetry datasets, etc. The results suggest that it is possible that tsunami forecasts and advisories from different centres for a particular event may conflict with each other. This represents the range of uncertainty that exists in the real-time situation.

Geoscience Australia (GA) has been acquiring both broadband and long-period magnetotelluric (MT) data over the last few years along deep seismic reflection survey lines across Australia, often in collaboration with the States/Territory geological surveys and the University of Adelaide. Recently, new three-dimensional (3D) inversion code has become available from Oregon State University. This code is parallelised and has been compiled on the NCI supercomputer at the Australian National University. Much of the structure of the Earth in the regions of the seismic surveys is complex and 3D, and MT data acquired along profiles in such regions are better imaged by using 3D code rather than 1D or 2D code. Preliminary conductivity models produced from the Youanmi MT survey in Western Australia correlate well with interpreted seismic structures and contain more geological information than previous 2D models. GA has commenced a program to re-model with the new code MT data previously acquired to provide more robust information on the conductivity structure of the shallow to deep Earth in the vicinity of the seismic transects.

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.

Models of seabed sediment mobilisation by waves and currents over Australia's continental shelf environment are used to examine whether disturbance regimes exist in the context of the intermediate disturbance hypothesis (IDH). Our study shows that it is feasible to model the frequency and magnitude of seabed disturbance in relation to the dominant energy source (wave-dominated shelf, tide-dominated shelf or tropical cyclone dominated shelf). Areas are mapped where the recurrence interval of disturbance events is comparable to the rate of ecological succession, which meets criteria defined for a disturbance regime. We focus our attention on high-energy, patch-clearing events defined as exceeding the Shields (bed shear stress) parameter value of 0.25. Using known rates of ecological succession for different substrate types (gravel, sand, mud), predictions are made of the spatial distribution of a dimensionless ecological disturbance index (ED), given as: ED = FA (ES/RI), where ES is the ecological succession rate for different substrates, RI is the recurrence interval of disturbance events and FA is the fraction of the frame of reference (surface area) disturbed. Maps for the Australian continental shelf show small patches of ED-seafloor distributed around the continent, on both the inner and outer shelf. The patterns are different for wave-dominated (patches on the outer shelf trending parallel to the coast), tide-dominated (patches crossing the middle-shelf trending normal to the coast) and cyclone-dominated (large oval-shaped patches crossing all depths). Only a small portion of the shelf (perhaps ~10%) is characterised by a disturbance regime as defined here. To our knowledge, this is the first time such an analysis has been attempted for any continental shelf on the earth.

The Attorney General's Departement has supported Geoscience Australia to develop inundation models for four east coast communities with the view of buildling the tsunami planning and preparation capacity of the Jurisdictions. The aim of this document and accompanying DVD is to report on the approach adopted by each Jurisdiction, the modelling outcomes and supply the underpinning computer scripts and input data.

The major tsunami disaster in the Indian Ocean in 2004, and the subsequent large events off the south coast of Indonesia and in the Solomon Islands, have dramatically raised awareness of the possibility of potentially damaging tsunamis in the Australian region. Since the 2004 Indian Ocean Tsunami (IOT), a number of emergency management agencies have worked with Geoscience Australia to help to develop an understanding of the tsunami hazard faced by their jurisdictions. Here I will discuss both the major tsunamis over the last few years in the region and the recent efforts of Geoscience Australia and others to try to estimate the likelihood of such events in the future. Since 2004, a range of probabilistic and scenario based hazard assessments have been completed through collaborative projects between Geoscience Australia and other agencies in Australia and the region. These collaborations have resulted in some of the first ever probabilistic tsunami hazard assessments to be completed for Australia and for a wide range of other countries in the southwest Pacific and Indian Oceans. These assessments not only estimate the amplitude of a tsunami that could reach the coast but also its probability. The assessments allow crucial questions from emergency managers (such as 'Just how often do large tsunamis reach our coasts?) to be quantitatively addressed. In addition, they also provide a mechanism to prioritise communities for more detailed risk assessments. This work allows emergency managers to base their decisions on the best available science and data for their jurisdiction instead of relying solely on intuition.