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  • 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.

  • The information within this document and associated DVD is intended to assist emergency managers in tsunami planning and preparation activities. The Attorney General's Department (AGD) has supported Geoscience Australia (GA) in developing a range of products to support the understanding of tsunami hazard through the Australian Tsunami Warning System Project. The work reported here is intended to further build the capacity of the Tasmanian State Government in developing inundation models for prioritised locations.

  • The tragic events of the Indian Ocean tsunami on 26 December 2004 highlighted the need for reliable and effective alert and response sysems for tsunami threat to Australian communities. Geoscience Australia has established collaborative partnerships with state and federal emergency management agencies to support better preparedness and to improve community awareness of tsunami risks.

  • Natural hazards such as floods, dam breaks, storm surges and tsunamis impact communities around the world every year. To reduce the impact, accurate modelling is required to predict where water will go, and at what speed, before the event has taken place. ANUGA is free, open source, software created to model water flow arising from these events. The resulting knowledge can be used to reduce loss of life and damage to property in communities affected by such disasters by providing vital input to evacuation plans, structural mitigation options and planning. The software was developed collaboratively by the Australian National University (ANU) and Geoscience Australia (GA) and is available at http://sourceforge.net/projects/anuga. ANUGA solves the non-linear shallow water wave equations using the finite volume method with dynamic time stepping. A major capability of ANUGA is that it can model the process of wetting and drying as water enters and leaves an area. This means it is suitable for simulating water flow onto a beach or dry land and around structures such as buildings. ANUGA is also capable of modelling complex flows involving shock waves and rapidly changing flow speeds (transitions from sub critical to super critical flows). ANUGA is a robust software package that contains over 800 unit tests. It has been validated against wave tank experiments [1] and model outputs from the 2004 Indian Ocean tsunami have compared very well with a run-up survey at Patong Beach, Thailand. This particular activity has also underpinned the results provided to Australian emergency managers managing tsunami risk. This presentation will outline the key components of ANUGA, examples for a range of hydrodynamic hazards as well as a sample of validation outputs.

  • Geoscience Australia has developed a number of open source risk models to estimate hazard, damage or financial loss to residential communities from natural hazards and is used to underpin disaster risk reduction activities. Two of these models will be discussed here: the Earthquake Risk Model (EQRM) and a hydrodynamic model call ANUGA, developed in collaboratoin with the ANU. Both models have been developed in Python using scientific and GIS packages such as Shapely, Numeric and SciPy. This presentation will outline key lessons learnt in developing scientific software in Python. Methods of maintaining and accessing code quality will be discussed (1) what makes a good unit test (2) how defects in the code were discovered quickly by being able to visualise the output data; and (3) how characterisation tests, which describe the actual behaviour of a system, are useful for finding unintended system changes. The challenges involved in optimising and parallelising Python code will also be presented. This is particularly important in scientific simulations as they use considerable computational resources and involve large data sets. This will be focus on: profiling; NumPyl using C code; and parallelisation of applications to run on clusters. Reduction of memory use by using a class to represent a group of items instead of a single item will also be discussed.

  • Natural hazards such as floods, dam breaks, storm surges and tsunamis impact communities around the world every year. To reduce the impact, accurate modelling is required to predict where water will go, and at what speed, before the event has taken place.

  • 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 (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.

  • The major tsunamis of the last few years have dramatically raised awareness of the possibility of potentially damaging tsunami reaching the shores of Australia and to the other countries in the region. Here we present three probabilistic hazard assessments for tsunami generated by megathrust earthquakes in the Indian, Pacific and southern Atlantic Oceans. One of the assessments was done for Australia, one covered the island nations in the Southwest Pacific and one was for all the countries surrounding the Indian Ocean Basin

  • This study tested the performance of 16 species models in predicting the distribution of sponges on the Australian continental shelf using a common set of environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distributions of sponge as a species group can be successfully predicted. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data - the new data were able to improve modelling performance for all the models both in terms of statistics (~10%) and in the predicted spatial distributions. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom water temperature and the resource variables that describe bottom water nutrient status were found to be useful surrogates for sponge distribution at the broad regional scale. This study demonstrates that predictive modelling techniques can enhance our understanding of processes that influence spatial patterns of benthic marine biodiversity. Ecological Informatics