This study tests three models for generating stochastic earthquake-tsunami scenarios on subduction zones by comparison with deep ocean observations from 18 tsunamis in 2006-2016. It focusses on the capacity of uncalibrated models to generate a realistic distribution of hypothetical tsunamis, assuming the earthquake location, magnitude and subduction interface geometry are approximately known, while details of the rupture area and slip distribution are unknown. <p>Modelling problems like this arise in tsunami hazard assessment, and when using historical and paleo-tsunami observations to study pre-instrumental earthquakes. Tsunamis show significant variability depending on their parent earthquake's properties, and it is important that this is realistically represented in stochastic tsunami scenarios. To clarify which aspects of earthquake variability should be represented, three scenario generation approaches with increasing complexity are tested: a simple fixed-area-uniform-slip model with earthquake area and slip deterministically related to moment magnitude; a variable-area-uniform-slip model which accounts for earthquake area variability; and a heterogeneous-slip model which accounts for both earthquake area variability and slip heterogeneity. The models are tested using deep-ocean tsunami time-series from 18 events (2006-2016) with moment magnitude $M_{w} > 7.7$. <p>For each model and each observed event a `corresponding family of model scenarios' is generated which includes random scenarios with earthquake location and magnitude similar to the observation, with no additional calibration. For an ideal model (which perfectly characterises the variability of tsunamis) the 18 observed events should appear like a random sample of size 18, created by taking one draw from each of the 18 `corresponding family of model scenarios'. This idea facilitates the development of statistical approaches to test the models. <p>Firstly a goodness-of-fit criterion is developed to identify random scenarios `most similar' to the observed tsunamis, and assess the capacity of different models to produce good-fitting scenarios. Both the heterogeneous-slip and variable-area-uniform-slip models show similar capacity to generate tsunamis similar to observations, while the fixed-area-uniform-slip model performs much more poorly in some cases. Secondly the observed tsunami stage ranges are tested for consistency with the null hypothesis that they were randomly generated by the model. The null hypothesis cannot be rejected for the heterogeneous-slip model, whereas both uniform-slip models exhibit a statistically significant tendency to produce small tsunamis too often. <p>Finally the statistical properties of random earthquake scenarios are compared against those earthquake scenarios that best fit the observations. For the variable-area-uniform-slip models the best-fitting model scenarios have higher slip on average than the random scenarios, highlighting biases in this model. Such biases are not evident in the heterogeneous-slip model. The techniques developed in this study can be applied to test random tsunami scenario generation techniques, identify and partially correct their biases, and provide better justification for their use in applications.

This poster shows earthquakes occurring in Australia in 2012 with a background of earthquakes occurring in Australia over the past 10 years. Also included are images produced as part of the analysis of the Ernabella, Moe and Tamworth Earthquakes as well as the yearly summary of earthquake occurrences in Australia.

In the early hours of 23 May 1960, an earthquake and tsunami struck Chile. There were no morning television shows and newspapers had already been printed for the day. Tsunami warning systems for Australia did not exist and there were no tools nor knowledge to help Australia prepare. Thankfully, we live in different times.

Following the 26 December 2004 tsunami, the Intergovernmental Oceanographic Commission (IOC) carried out National Assessment missions for 16 countries in the Indian Ocean to advance the establishment of an Indian Ocean Tsunami Warning and Mitigation System. The missions assessed each country's requirements for effective and durable tsunami response, and their capacity to strengthen capabilities for tsunami early warning and response. The IOC, funded by AusAID, carried out a National Assessment mission for Timor Leste from 26th November to 1st December 2007. The mission team consisted of experts from the IOC, the World Meteorological Organization (WMO), UN International Strategy for Disaster Reduction (ISDR), UN Development Program (UNDP), Geoscience Australia (GA) and the Australian Bureau of Meteorology (BOM). GA and BOM carried out site investigations for a proposed seismic station and sea level monitoring stations that may be installed as part of the Australian Tsunami Warning System (ATWS).

We present the first national probabilistic tsunami hazard assessment (PTHA) for Indonesia. This assessment considers tsunami generated from near-field earthquakes sources around Indonesia as well as regional and far-field sources, to define the tsunami hazard at the coastline. The PTHA methodology is based on the established stochastic event-based approach to probabilistic seismic hazard assessment (PSHA) and has been adapted to tsunami. The earthquake source information is primarily based on the recent Indonesian National Seismic Hazard Map and included a consensus-workshop with Indonesia's leading tsunami and earthquake scientists to finalize the seismic source models and logic trees to include epistemic uncertainty. Results are presented in the form of tsunami hazard maps showing the expected tsunami height at the coast for a given return period, and also as tsunami probability maps, showing the probability of exceeding a tsunami height of 0.5m and 3.0m at the coast. These heights define the thresholds for different tsunami warning levels in the Indonesian Tsunami Early Warning System (Ina-TEWS). The results show that for short return periods (100 years) the highest tsunami hazard is the west coast of Sumatra, the islands of Nias and Mentawai. For longer return periods (>500 years), the tsunami hazard in Eastern Indonesia (north Papua, north Sulawesi) is nearly as high as that along the Sunda Arc. A sensitivity analysis of input parameters is conducted by sampling branches of the logic tree using a monte-carlo approach to constrain the relative importance of each input parameter. These results can be used to underpin evidence-based decision making by disaster managers to prioritize tsunami mitigation such as developing detailed inundation simulations for evacuation planning.

The Mwp method provides a rapid estimate of the moment magnitude of an earthquake based on the P-wave arrival. In this paper we present a variation of this method that addresses two problems that are encountered when applying this method in practice. The first is that the magnitude of very large earthquakes that could generate an ocean wide tsunami is generally underestimated. The second is that the method relies on the magnitude of the first significant maximum after the P-arrival in the integrated displacement (ID) seismogram. Identification of the "correct" first maximum generally has to be performed by an analyst, which introduces a subjective step in the algorithm. In this paper we present a variation of the Mwp method that estimates the asymptotic value of the ID caused by the P arrival, rather than the first maximum. Since asymptotic behaviour of the ID is never observed in practice because of seismic background noise, the new method is based on a comparison of the seismic noise signal before the arrival and the signal of the arrival itself. The new algorithm allows a fully automatic and unambiguous moment estimate. We apply the algorithm to observations of 30 strong (Mw>6.0) earthquakes around Australia, and compare the result with the moment magnitudes of these earthquakes as published by the USGS. It is found that the new algorithm is more accurate than the standard Mwp method, especially for very large (Mw>7.5) earthquakes.

Landslides can happen on the seafloor, just like on land. Areas of the seafloor that are steep and loaded with sediment are more prone to undersea landslides, such as the edge of the continental slope. When an undersea landslide occurs (perhaps after a nearby earthquake) a large mass of sand, mud and gravel can move down the slope. This movement will draw the water down and may cause a tsunami that will travel across the ocean.

This 35 page booklet provides teachers and students with an understanding of what a tsunami is, what generates tsunamis, where they occur, what happens when they reach land, how tsunamis impact Australians, and the role Geoscience Australia plays in providing the Australian Tsunami Warning System. In order to engage students this booklet also includes fun educational activities with answers. Suitable for both upper primary and high school science and geography teachers.

Pacific island countries face a tsunami threat that consists of a complex mix of tsunamis from local, regional and distant sources. Assessment of risk on these islands requires the ability to model tsunami inundation, and such modelling is complicated by the fact that they are often surrounded by shallow coral reef systems whose influence on tsunami propagation is poorly understood. These islands also suffer from a lack of both bathymetry and topography data of sufficient resolution to accurately model tsunami inundation. Geoscience Australia and the Pacific Islands Applied Geoscience Commission (SOPAC) have been developing a capacity for tsunami inundation modeling in support of risk assessment for Pacific islands that relies on remote sensing for nearshore bathymetric data coverage, including shallow reef platforms. This technique uses a physics-based modeling approach that estimates bathymetry from multispectral imagery, based on an optimisation driven per-pixel estimation of a set of environmental variables, including water column depth, from a semi-analytical expression of sub-surface remote sensing reflectance. Using this approach we have developed models for shallow bathymetry for off Nuku'alofa in Tongatapu and Gizo in the Solomon Islands, and merged these models with available swath bathymetry and global bathymetry data to produce bathymetry grids suitable for modelling tsunami inundation. We have attempted to validate these models against data for the 2006 Tonga (Mw=8.0) and 2007 Solomon Islands (MW=8.1) earthquakes, respectively.

Legacy product - no abstract available