A rapid-response earthquake fatality estimation model for mainland China

Li, Yilong; Xin, Danhua; Zhang, Zhenguo

doi:10.1016/j.ijdrr.2021.102618

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…We used the Mainland China earthquake fatalities estimation model developed by Li et al [ 12 ], which is based on data of seismic intensity and losses for historical earthquakes. Clearly, the performance of this model depends on the completeness of data, and their integrity and consistency across events.…”

Section: Methodsmentioning

confidence: 99%

“…The catalog includes basic seismological data, social and economic loss data, population exposure data, and intensity distribution data for each earthquake event. Based on data recorded for the 377 earthquake events in the MCCDE-CAT ( Figure 2 ), Li et al used a logarithmic linear fatality ratio function with two free parameters to develop a set of earthquake fatality estimation models via regression analysis [ 12 ]. A large number of earthquake cases were used to verify that the model could estimate >85% of the values within an order of magnitude of the recorded values.…”

Section: Methodsmentioning

confidence: 99%

“…According to the relevant provisions in the National Earthquake Emergency Plan (NEEP) (see Data Availability Statement) for Mainland China, the Chinese government classifies the earthquake emergency response into four levels as follows: Level IV (≤10 fatalities), Level III (10 < Number of fatalities ≤ 50), Level II (50 < Number of fatalities ≤ 300), and Level I (>300 fatalities). To rapidly evaluate the response level after an earthquake, we constructed a normal cumulative distribution function based on the estimated fatalities and fitting residual calculated using this method (its rationality has been verified using the normality test [ 12 ]). The probability P of a specific fatality range was calculated as follows:

where Φ represents the normal cumulative distribution function with respect to the expected value, ln( E i ), and the log residual,

.…”

Section: Methodsmentioning

confidence: 99%

“…The probability P of a specific fatality range was calculated as follows:

where Φ represents the normal cumulative distribution function with respect to the expected value, ln( E i ), and the log residual,

. For more details on the development and validation of this estimation model, see [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, to rapidly obtain such an estimate after an earthquake in Mainland China, we opted to rely on an empirical method. Li et al developed an earthquake fatality estimation model based on the Mainland China Composite Damaging Earthquake Catalog (MCCDE-CAT) [ 1 , 12 ]. Based on many earthquake events estimations, Li et al verified that the model has an optimal evaluation ability for large earthquakes that resulted in tens of thousands of fatalities, such as the Wenchuan earthquake, and small earthquakes that resulted in relatively few fatalities [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Rapid Estimation of Earthquake Fatalities in Mainland China Based on Physical Simulation and Empirical Statistics—A Case Study of the 2021 Yangbi Earthquake

Zhang

Wang

et al. 2022

IJERPH

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At present, earthquakes cannot be predicted. Scientific decision-making and rescue after an earthquake are the main means of mitigating the immediate consequences of earthquake disasters. If emergency response level and earthquake-related fatalities can be estimated rapidly and quantitatively, this estimation will provide timely, scientific guidance to government organizations and relevant institutions to make decisions on earthquake relief and resource allocation, thereby reducing potential losses. To achieve this goal, a rapid earthquake fatality estimation method for Mainland China is proposed herein, based on a combination of physical simulations and empirical statistics. The numerical approach was based on the three-dimensional (3-D) curved grid finite difference method (CG-FDM), implemented for graphics processing unit (GPU) architecture, to rapidly simulate the entire physical propagation of the seismic wavefield from the source to the surface for a large-scale natural earthquake over a 3-D undulating terrain. Simulated seismic intensity data were used as an input for the fatality estimation model to estimate the fatality and emergency response level. The estimation model was developed by regression analysis of the data on human loss, intensity distribution, and population exposure from the Mainland China Composite Damaging Earthquake Catalog (MCCDE-CAT). We used the 2021 Ms 6.4 Yangbi earthquake as a study case to provide estimated results within 1 h after the earthquake. The number of fatalities estimated by the model was in the range of 0–10 (five expected fatalities). Therefore, Level IV earthquake emergency response plan should have been activated (the government actually overestimated the damage and activated a Level II emergency response plan). The local government finally reported three deaths during this earthquake, which is consistent with the model predictions. We also conducted a case study on a 2013 Ms7.0 earthquake in the discussion, which further proved the effectiveness of the method. The proposed method will play an important role in post-earthquake emergency response and disaster assessment in Mainland China. It can assist decision-makers to undertake scientifically-based actions to mitigate the consequences of earthquakes and could be used as a reference approach for any country or region.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

where Φ represents the normal cumulative distribution function with respect to the expected value, ln( E i ), and the log residual,

.…”

Section: Methodsmentioning

confidence: 99%

“…The probability P of a specific fatality range was calculated as follows:

where Φ represents the normal cumulative distribution function with respect to the expected value, ln( E i ), and the log residual,

. For more details on the development and validation of this estimation model, see [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rapid Estimation of Earthquake Fatalities in Mainland China Based on Physical Simulation and Empirical Statistics—A Case Study of the 2021 Yangbi Earthquake

Zhang

Wang

et al. 2022

IJERPH

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Rapid estimation of disaster losses for the M6.8 Luding earthquake on September 5, 2022

Wang,

Li,

Zhang

et al. 2023

Sci. China Earth Sci.

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A Physics-Based Seismic Risk Assessment of the Qujiang Fault: From Dynamic Rupture to Disaster Estimation

Li,

Wang,

Zhang

et al. 2024

Int J Disaster Risk Sci

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This study achieved the construction of earthquake disaster scenarios based on physics-based methods—from fault dynamic rupture to seismic wave propagation—and then population and economic loss estimations. The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry, stress field, rock properties, and terrain. Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved. The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment. Additionally, there are significant variations in human losses, even though the economic losses are similar across different scenarios. Dali Bai Autonomous Prefecture, Chuxiong Yi Autonomous Prefecture, Yuxi City, Honghe Hani and Yi Autonomous Prefecture, and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks, with Yuxi and Honghe being particularly vulnerable. Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault. Notably, although the fault is within Yuxi, Honghe is likely to suffer the most severe damage. These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.

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A rapid-response earthquake fatality estimation model for mainland China

Cited by 12 publications

References 14 publications

Rapid Estimation of Earthquake Fatalities in Mainland China Based on Physical Simulation and Empirical Statistics—A Case Study of the 2021 Yangbi Earthquake

Rapid Estimation of Earthquake Fatalities in Mainland China Based on Physical Simulation and Empirical Statistics—A Case Study of the 2021 Yangbi Earthquake

Rapid estimation of disaster losses for the M6.8 Luding earthquake on September 5, 2022

A Physics-Based Seismic Risk Assessment of the Qujiang Fault: From Dynamic Rupture to Disaster Estimation

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