The 16 September 2015 Illapel Earthquake and Tsunami: Post-Event Tsunami Inundation, Building and Infrastructure Damage Survey in Coquimbo, Chile

Paulik, Ryan; Williams, James; Horspool, Nick; Catalán, Patricio A.; Mowll, Richard; Cortés, Pablo; Woods, Richard

doi:10.1007/s00024-021-02734-x

Cited by 17 publications

(19 citation statements)

References 19 publications

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“…However, the situation is more complex for the Coquimbo-La Serena region. While the CoB NNM predicts inundation at a level large enough to have prompted evacuation (hazard properly categorized), none of the network models is capable to predict the measured flow depth of nearly 6 m 60 , 62 , 90 . Notably, the Hayes model 75 for the Illapel event forecasts very small flow depths (a missed alarm).…”

Section: Resultsmentioning

confidence: 99%

“…The other location is Coquimbo bay (29 57’12”S 71 20’17”W), which shows a different behavior, as it was inundated during the 2015 Illapel earthquake 60 , while large amplitudes were recorded in the local tide gauge during the 2010 Maule tsunami 58 and recently during the Hunga-Tonga-Hunga-Ha’apai transpacific tsunami 61 , but without inundation. In addition, the bay is highly resonant, and with a spatial structure of the first mode that induces the southern end of the bay to be susceptible to tsunami inundation, whereas the central and northern ends are less prone to become inundated 62 . Hence, this location allows for assessing the capabilities of the network for complex inundation behavior and inundation footprints.…”

Section: Methodsmentioning

confidence: 99%

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Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

Núñez

Catalán

Valle

et al. 2022

Sci Rep

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Tsunamis are natural phenomena that, although occasional, can have large impacts on coastal environments and settlements, especially in terms of loss of life. An accurate, detailed and timely assessment of the hazard is essential as input for mitigation strategies both in the long term and during emergencies. This goal is compounded by the high computational cost of simulating an adequate number of scenarios to make robust assessments. To reduce this handicap, alternative methods could be used. Here, an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inundation metrics extrema. However, for the context of Tsunami Early Warning, it is equally relevant to assess whether the networks can accurately predict whether inundation would occur or not, and its time series if it does. Hence, a set of 6776 scenarios with magnitudes in the range $$M_w$$ M w 8.0–9.2 were used to design several 1D CNN models at two bays that have different hydrodynamic behavior, that would use as input inexpensive low-resolution numerical modeling of tsunami propagation to predict inundation time series at pinpoint locations. In addition, different configuration parameters were also analyzed to outline a methodology for model testing and design, that could be applied elsewhere. The results show that the network models are capable of reproducing inundation time series well, either for small or large flow depths, but also when no inundation was forecast, with minimal instances of false alarms or missed alarms. To further assess the performance, the model was tested with two past tsunamis and compared with actual inundation metrics. The results obtained are promising, and the proposed model could become a reliable alternative for the calculation of tsunami intensity measures in a faster than real time manner. This could complement existing early warning system, by means of an approximate and fast procedure that could allow simulating a larger number of scenarios within the always restricting time frame of tsunami emergencies.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

Núñez

Catalán

Valle

et al. 2022

Sci Rep

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“…The maximum flow depth obtained by the models for the reference Manning value in a fraction of the Coquimbo area is shown in Figure 6 together with the results in a transect close to that used in Aránguiz et al [36]. The impact of the tsunami in Coquimbo bay was examined in field surveys (for example, see [36,42]). From these investigations, the measurements obtained are valuable as reference values for comparisons to numerical simulations.…”

Section: Illapel Event 2015 321 Offshore Assessment Based On Experime...mentioning

confidence: 92%

Systematic Comparison of Tsunami Simulations on the Chilean Coast Based on Different Numerical Approaches

et al. 2022

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Tsunami inundation estimates are of crucial importance to hazard and risk assessments. In the context of tsunami forecast, numerical simulations are becoming more feasible with the growth of computational power. Uncertainties regarding source determination within the first minutes after a tsunami generation might be a major concern in the issuing of an appropriate warning on the coast. However, it is also crucial to investigate differences emerging from the chosen algorithms for the tsunami simulations due to a dependency of the outcomes on the suitable model settings. In this study, we compare the tsunami inundation in three cities in central Chile (Coquimbo, Viña del Mar, and Valparaíso) using three different models (TsunAWI, Tsunami-HySEA, COMCOT) while varying the parameters such as bottom friction. TsunAWI operates on triangular meshes with variable resolution, whereas the other two codes use nested grids for the coastal area. As initial conditions of the experiments, three seismic sources (2010 Mw 8.8 Maule, 2015 Mw 8.3 Coquimbo, and 1730 Mw 9.1 Valparaíso) are considered for the experiments. Inundation areas are determined with high-resolution topo-bathymetric datasets based on specific wetting and drying implementations of the numerical models. We compare each model’s results and sensitivities with respect to parameters such as bottom friction and bathymetry representation in the varying mesh geometries. The outcomes show consistent estimates for the nearshore wave amplitude of the leading wave crest based on identical seismic source models within the codes. However, with respect to inundation, we show high sensitivity to Manning values where a non-linear behaviour is difficult to predict. Differences between the relative decrease in inundation areas and the Manning n-range (0.015–0.060) are high (11–65%), with a strong dependency on the characterization of the local topo-bathymery in the Coquimbo and Valparaíso areas. Since simulations carried out with such models are used to generate hazard estimates and warning products in an early tsunami warning context, it is crucial to investigate differences that emerge from the chosen algorithms for the tsunami simulations.

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“…Tsunami fragility curves commonly rely on relatively large samples of empirical or modelled impact damage data, yet such quantitative empirical data for infrastructure vulnerability are rare and have only recently been a focus of post-event impact assessment (MLIT, 2012; Paulik et al, 2019Paulik et al, , 2021 and physical modelling studies (C Chen and Melville, 2015; Cheng Chen et al, 2017Chen et al, , 2018Rossetto et al, 2014). For infrastructure networks, transportation components, namely roads, bridges, utility poles and port structures, have been previously analysed for fragility function development from empirical eld surveys and physical modelling (Chua et al, 2020;Eguchi et al, 2013; Kawashima and Buckle, 2013; Koks et al, 2019;Maruyama and Itagaki, 2017;Shoji and Moriyama, 2007;Williams, et al, 2020b;Williams, et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Infrastructure network component vulnerability to damage from the 2015 Illapel tsunami, Coquimbo, Chile

Williams

Paulik

Aránguiz

et al. 2022

Preprint

Self Cite

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This study presents critical infrastructure network component vulnerability in the 2015 Illapel tsunami at Coquimbo, Chile. We analyse road and utility pole vulnerability to physical damage based on modelled tsunami hazard intensity (depth, velocity and hydrodynamic force) and network component characteristics. A Random Forest Model and Spearman’s Rank correlation test are applied to analyse variable importance and monotonic relationships between tsunami hazards and network component attributes with damage levels. These models and tests revealed scour and debris impacts are highly important and strongly correlated with road and utility pole damage levels, while flow depth correlates higher with damage levels relative to flow velocity and hydrodynamic force. A cumulative link model methodology is used to develop fragility curves, The fragility curves in response to flow depth revealed roads have higher vulnerability than those analysed in previous tsunami events (e.g. 2011 Tohoku, 2018 Sulawesi, 2015 Illapel Tsunami), while utility poles demonstrate lower vulnerability. Although we identify flow depth as the hydrodynamic hazard intensity metric of highest importance for causing road and utility pole physical damage, fragility curves representing multiple hazard intensities provides a holistic curve suite for analysing infrastructure network damage and disruption from future tsunami events.

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The 16 September 2015 Illapel Earthquake and Tsunami: Post-Event Tsunami Inundation, Building and Infrastructure Damage Survey in Coquimbo, Chile

Cited by 17 publications

References 19 publications

Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks

Systematic Comparison of Tsunami Simulations on the Chilean Coast Based on Different Numerical Approaches

Infrastructure network component vulnerability to damage from the 2015 Illapel tsunami, Coquimbo, Chile

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