Natural Hazards and Earth Systems Science

A component of the PNG-Australia Volcanological Services Support (VSS) Project funded by AusAID

<div>On January 15, 2022, an ongoing eruption at the Hunga volcano generated a large explosion which resulted in a globally observed tsunami and atmospheric pressure wave. This paper presents time series observations of the event from Australia including 503 mean sea level pressure (MSLP) sensors and 111 tide gauges. Data is provided in its original format, which varies between data providers, and a post-processed format with consistent file structure and time-zone. High-pass filtered variants of the data are also provided to facilitate study of the pressure wave and tsunami. For a minority of tide gauges the raw sea level data cannot be provided, due to licence restrictions, but high-pass filtered data is always provided. The data provides an important historical record of the Hunga volcano pressure wave and tsunami in Australia. It will be useful for research in atmospheric and ocean waves associated with large volcanic eruptions. <b>Citation:</b> Davies, G., Wilson, K., Hague, B. et al. Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha’apai volcano tsunami. <i>Sci Data</i> <b>11</b>, 114 (2024). https://doi.org/10.1038/s41597-024-02949-2

Papua New Guinea (PNG) is situated at the edge of the Pacific “ring of fire” and is exposed to frequent large earthquakes and volcanic eruptions. Earthquakes in PNG, such as 2018 Hela Province event (M7.5), continue to cause loss of life and widespread damage to buildings and infrastructure. Given its high seismic hazard, PNG would benefit from a dense seismic monitoring network for rapid (near real-time), as well as long-term, earthquake hazard and risk assessment. Geoscience Australia (GA) is working with technical agencies of PNG Government to deliver a Department of Foreign Affairs and Trade (DFAT) funded technical disaster risk reduction (DRR) program to increase community resilience on the impact of natural hazards and other secondary hazards. As part of this program, this study explores the feasibility of establishing a low-cost, community-based seismic network in PNG by first verifying the performance of the low-cost Raspberry Shake 4D seismograph, which includes a three-component strong-motion MEMs accelerometer and one (vertical) short-period geophone. A Shake device was deployed at the Rabaul Volcanological Observatory (RVO) for a period of one month (May 2018), relaying data in real-time via a 3G modem. To assess the performance of the device, it was co-located with global seismic network-quality instruments that included a three-component broadband seismometer and a strong motion accelerometer operated by GA and RVO, respectively. A key challenge for this study was the rather poor data service by local telecommunication operators as well as frequent power outages which caused repeated data gaps. Despite such issues, the Shake device successfully recorded several earthquakes with magnitudes as low as mb 4.0 at epicentral distances of 600 km, including earthquakes that were not reported by international agencies. The time-frequency domain comparisons of the recorded waveforms with those by the permanent RVO instruments reveal very good agreement in a relatively wide frequency range of 0.1-10 Hz. Based on the estimated noise model of the Shake device (seismic noise as well as instrument noise), we explore the hypothetical performance of the device against typical ground-motion amplitudes for various size earthquakes at different source-to-site distances. Presented at the 2018 Australian Earthquake Engineering Society (AEES) Conference

The present study reports on recent developments of the Indonesia Tsunami Early Warning System (InaTEWS), especially with respect to the tsunami modeling components used in that system. It is a dual system: firstly, InaTEWS operates a high-resolution scenario database pre-computed with the finite element model TsunAWI; running in parallel, the system also contains a supra real-time modeling component based on the GPU-parallelized linear long-wave model easyWave capable of dealing with events outside the database coverage. The evolution of the tsunami scenario database over time is covered in the first sections. Starting from the mere coverage of the Sunda Arc region, the current state contains scenarios in 15 fault zones. The study is augmented by an investigation of warning products used for early warning like the estimated wave height (EWH) and the estimated time of arrival (ETA). These quantities are determined by easyWave and TsunAWI with model specific approaches. Since the numerical setup of the models is very different, the extent of variations in warning products is investigated for a number of scenarios, where both pure database scenarios and applications to real events are considered.

The Government of Indonesia has committed to deploying a network of 500 strong-motion sensors throughout the nation. The data from these sensors have the potential to provide critical near-real-time information on the level of ground shaking and potential impact from Indonesian earthquakes near communities. We describe the implementation of real-time ‘ShakeMaps’ within Indonesia's Agency of Meteorology, Climatology and Geophysics (BMKG). These ShakeMaps are intended to underpin real-time earthquake situational awareness tools. The use of the new strong-motion network is demonstrated for two recent earthquakes in northern Sumatra: the 2 July 2013 Mw 6.1 Bener Meriah, Sumatra and the 10 October 2013 Mw 5.4 Aceh Besar earthquakes. The former earthquake resulted in 35 fatalities, with a further 2400 reported injuries. The recently integrated ShakeMap system automatically generated shaking estimates calibrated by BMKG's strong-motion network within 7 min of the Bener Meriah earthquake's origin, which assisted the emergency response efforts. Recorded ground motions are generally consistent with theoretical models. However, more analysis is required to fully characterize the attenuation of strong ground motion in Indonesia.

Historical reports of earthquake effects from the period 1681 to 1877 in Java, Bali and Nusa Tenggara are used to independently test ground motion predictions in Indonesia’s 2010 national probabilistic seismic hazard assessment (PSHA). Assuming that strong ground motion occurrence follows a Poisson distribution, we cannot reject Indonesia’s current PSHA for key cities in Java at 95% confidence. However, the results do suggest that seismic hazard may be underestimated for the megacity Jakarta. Ground motion simulations for individual large damaging events are used to identify plausible source mechanisms, providing insights into the major sources of earthquake hazard in the region and possible maximum magnitudes for these sources. The results demonstrate that large intraslab earthquakes have been responsible for major earthquake disasters in Java, including a ~Mw 7.5 intraslab earthquake near Jakarta in 1699 and a ~Mw 7.8 event in 1867 in Central Java. The results also highlight the potential for large earthquakes to occur on the Flores Thrust. We require an earthquake with Mw 8.4 on the Flores Thrust to reproduce tsunami observation from Sulawesi and Sumbawa in 1820. Furthermore, large shallow earthquakes (Mw > 6) have occurred in regions where active faults have not been mapped identifying the need for further research to identify and characterize these faults for future seismic hazard assessments. <b>Citation:</b> Jonathan Griffin, Ngoc Nguyen, Phil Cummins, Athanasius Cipta; Historical Earthquakes of the Eastern Sunda Arc: Source Mechanisms and Intensity‐Based Testing of Indonesia’s National Seismic Hazard Assessment. <i>Bulletin of the Seismological Society of America </i>2018; 109 (1): 43–65. doi: https://doi.org/10.1785/0120180085

Many earthquakes in Indonesia have caused a large number of fatalities. Disaster risk-reduction of fatalities requires a representative fatality model derived from fatality data caused by historical earthquakes in Indonesia. We develop an empirical fatality model for Indonesia by relating macroseismic intensity to fatality rate using compiled subdistrict level fatality rate data and numerically simulated ground shaking intensity for four recent damaging events. The fatality rate data are compiled by collecting population and fatality statistics of the regions impacted by the selected events. The ground shaking intensity is numerically estimated by incorporating a finite fault model of each event and local site conditions approximated by topographically-based site amplifications. The macroseismic intensity distribution of each event is generated by using ShakeMap software with a selected pair of ground motion predictive equation (GMPE) and ground motion to intensity conversion equation (GMICE). The developed fatality model is a Bayesian generalized linear model where the fatality rate is assumed to follow a mixture of a Bernoulli and a gamma distribution. The probability of zero fatality rate and the mean non-zero fatality rate is linked to a linear function of shaking intensity by the logit and the log link functions, respectively. We estimate posterior distribution of the parameters of the model based on the Hamilton Monte Carlo algorithm. For validation of the developed model we calculate fatalities of the past events from the EXPO-CAT catalog and compare the estimates with the EXPO-CAT fatality records. While the developed fatality model can provide an estimate of the range of fatalities for future events it needs on-going refinement by incorporation of additional fatality rate data from past and future events.

Tsunami hazard modelling for Tonga shows the potential impacts of tsunami generated by a very large earthquake on the nearby Tongan Trench.

Indonesia is located in one of the most seismically active regions in the world and often experiences damaging earthquakes. In the past the housing sector has sustained more damage and losses than other sectors due to earthquakes. This is often attributed to the fact that the most common houses in Indonesia are non-engineered, built with poor quality workmanship, poor quality materials and without resilient seismic design features. However little effort has been made to quantify how fragile these houses are, or how the fragility of these houses may vary according to location or wealth. It is not possible to derive empirical fragility functions for Indonesia due to insufficient damage data. The aim of this study is to determine whether existing earthquake fragility functions can be used for common houses in Indonesia. Scenario damage analyses were undertaken several times using different sets of fragility functions for the 2006 Yogyakarta and 2009 Padang events. The simulated damage results were then compared to the damage observed post event to determine whether an accurate damage prediction could be achieved. It was found that the common houses in Yogyakarta and Central Java vary according to age, location and wealth and can be reasonably well represented by existing fragility functions. However, the houses in Padang and surrounding West Sumatra did not vary in a predictable manner and are more fragile than anticipated. Therefore, the fragility of the most common houses in Indonesia is not uniform across the country. This has important implications for seismic damage and risk assessment undertaken in Indonesia. <b>Citation:</b> Weber, R., Cummins, P. & Edwards, M. Fragility of Indonesian houses: scenario damage analysis of the 2006 Yogyakarta and 2009 Padang earthquakes. <i>Bull Earthquake Eng</i> (2024). https://doi.org/10.1007/s10518-024-01930-z