Yilong Li

The 1739 M 8 Pingluo earthquake is the largest seismic event recorded in the Yinchuan basin, located in Ningxia Hui Autonomous Region, China. The East Helanshan fault (EHF), a prominent active normal fault, was suggested as its causative fault. This study inferred a reasonable combination of dynamic parameters for the EHF by revisiting the 1739 event from dynamic rupture to disaster assessment. The dynamic model agreed well with surface rupture observations and previous damage investigations. The magnitude of this earthquake is about Mw 7.6. Subsequently, four distinct epicenters were chosen as earthquake scenarios for the EHF. Our simulation revealed that earthquakes nucleated at the northeast segment of the EHF can always trigger cascading rupture of two fault segments, reaching a moment magnitude of 7.6. In contrast, earthquakes nucleated at the southwest segment of the EHF only have a magnitude of Mw 7.0. Furthermore, we estimated the potential fatalities and economic losses in the study area if these scenarios occur in the current year. For the four Mw 7.6 scenarios, the highest probability (∼52% to 56%) of the economic loss range is RMB 1000–10,000 billion yuan, and the probability of resulting in more than 1000 deaths is close to 90%. The overall impact of hypocenter-dependent scenarios on economic losses is significantly smaller than that on fatalities. This phenomenon likely related to the spatial characteristics of population and fixed asset exposure. Yinchuan, Shizuishan, and Wuhai were identified as high-seismic risk regions. Our physics-based simulations provide additional insights into the complex seismic hazard mechanisms of hanging-wall effect coupled with basin amplification. This work also established a valuable framework for assessing potential seismic hazards in basin areas governed by normal faults.

In the northern part of the South China Sea lies a tectonic belt with frequent seismic activity, which is known as the Littoral Fault Zone (LFZ). The middle part of this fault zone is adjacent to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The LFZ comprises three main fault segments characterized by complex tectonic features and high seismicity. Using the curvilinear grid finite-difference method, we first conducted a 3D spontaneous dynamic rupture simulation of potential earthquakes on a multifault model of the LFZ. Under a reasonable range of physical parameter constraints, we explored a reference dynamic model and a critical dynamic model. Then, based on the critical dynamic model that could trigger extreme earthquake scenarios, we performed dynamic rupture simulations by setting six hypocenters in areas with high static stress drops on the three main fault segments. The simulation results indicated that the easternmost fault segment (F3) which is closer to Shenzhen and Hong Kong could generate the largest possible earthquake with a magnitude up to Mw 7.47. Considering a ± 10° uncertainty in stress orientation, the potential maximum magnitude can reach Mw 7.59. Furthermore, the results suggested a low likelihood of cascading rupture across the three fault segments, due to generally low stress drops at the boundaries between faults, which hinder the rupture jumping. Thus, we concluded that the probability of the LFZ generating an extreme earthquake (Mw≥7.6) affecting the GBA is relatively low. Given these results, future efforts to mitigate seismic disasters in the GBA should especially focus on areas near the easternmost fault segment, F3.

Earthquakes occurring in the Taiwan region have the potential to cause significant damage and loss of life. To ensure an effective emergence response and mitigate the impact of seismic events, it is imperative to develop an earthquake fatality prediction model that incorporates the Taiwan region’s seismic background, geological context, and socioeconomic level. This research article analyzes 35 historically damaging earthquakes in the Taiwan region, considering their fatality records, macroseismic intensities (modified Mercalli shaking intensity scale), and population exposure, to propose a specific seismic fatality ratio applicable to the Taiwan region. The model incorporates adjustments based on the gross national income per capita to address temporal and spatial disparities resulting from socioeconomic development. The predicted results highlight the superior performance of this fatality estimation model compared to other existing models. Furthermore, the earthquake fatality estimation model for the Taiwan region differs significantly from the models employed for South China and Qinghai–Tibet, suggesting variations in geological background, earthquake-resistant structures, and seismic resilience of society between the Taiwan region and mainland China. Moreover, a comparison with the U.S. Geological Survey model for the Taiwan region reveals that previous evaluations of the Taiwan region earthquakes greatly underestimated the number of fatalities.

Ambient noise tomography has been widely used to estimate the shear-wave velocity structure of the Earth. A key step in this method is to pick dispersions from dispersion spectrograms. Using the frequency–Bessel (F-J) transform, the generated spectrograms can provide more dispersion information by including higher modes in addition to the fundamental mode. With the increasing availability of these spectrograms, manually picking dispersion curves is highly time and energy consuming. Consequently, neural networks have been used for automatically picking dispersions. Dispersion curves are picked based on deep learning mainly for denoising these spectrograms. In several studies, the neural network was solely trained, and its performance was verified for the denoising. However, they all learn single-source data in the training of neural network. It will lead the regionality of trained neural network. Even if we can use domain adaptation to improve its performance and achieve some success, there are still some spectrograms that cannot be solved effectively. Therefore, multisources training is useful and could reduce the regionality in training stage. Normally, dispersion spectrograms from multisources have feature differences of dispersion curves, especially for higher modes in F-J spectrograms. Thus, we propose a training strategy based on domain confusion through which the neural network effectively learns spectrograms from multisources. After domain confusion, the trained neural network can effectively process large number of test data and help us easily obtain more dispersion curves automatically. The proposed study can provide a deep insight into the denoising of dispersion spectrograms by neural network and facilitate ambient noise tomography.

Several studies have shown the importance of electromagnetic ion cyclotron (EMIC) rising‐tone emissions to the rapid precipitation of energetic radiation belt electrons. Based on a large number of Van Allen Probes observations from October 2012 to July 2019, we identify EMIC rising‐tone emissions using a convolutional neural network (CNN), a modern deep learning technique. Results of training indicate that the CNN is capable of identifying EMIC rising‐tone emissions with a recall of 99.3%. The statistical analysis of the wave events identified reveals that the average occurrence rate of the events is about 0.016%, with a high occurrence rate from the forenoon to the dusk sector at L > 5. There are also events observed at L < 5, which are scattered at almost all magnetic local times. The events in the hydrogen and helium bands have comparable wave amplitudes on average, but the larger amplitude events tend to occur around noon and in the afternoon sector in the hydrogen and helium bands, respectively. In addition, the frequency sweep rate tends to increase with the wave frequency. The frequency sweep rates of the hydrogen band EMIC rising‐tone emissions are about 6 times larger than those of the helium band events. There is also a positive correlation between the wave amplitudes and the sweep rates of the hydrogen band emissions.

At present, seismic prediction technology is still not available. Scientific decision-making and rescue after an earthquake are the main means of mitigating the immediate consequences of earthquake disasters. If earthquake emergency response level, fatalities, and economic losses 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 and more conducive to the implementation of social activities such as post-disaster reconstruction and reinsurance. To achieve this goal, a rapid earthquake disaster loss estimation method is proposed herein, based on a combination of physical simulations and empirical statistics. The numerical approach was based on the three-dimensional 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 input for the earthquake disaster loss estimation model to estimate the fatality, economic loss, and emergency response level. The estimation model was developed by regression analysis of the data on human loss, economic loss, intensity distribution, and population exposure from the composite damaging earthquake catalog. We used the 2021 Ms 6.4 Yangbi earthquake as a study case to provide estimated results. The number of fatalities estimated by the model was in the range of 0–10 (five expected fatalities). The most probable economic loss range was 1–10 billion RMB (the expected economic loss was 4.862 billion RMB). 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 and 3.32 billion RMB economic losses during this earthquake, which is consistent with the model predictions.

After the outbreak of cyanobacterial water pollution crisis in Taihu Lake Basin, it not only affected the normal life and production activities of local residents, but also attracted extensive attention from the media at home and abroad. When the governments at all levels from the central to the local level attach great importance to the comprehensive water environment management in the Taihu basin, they start from the perspectives of land, science and technology, farmers, water conservancy, construction and environmental protection, and put forward a large number of engineering and non-engineering measures. Therefore, on the basis of clarifying the significance of water conservancy project construction and the current situation of water environment management in Taihu Lake basin, this paper deeply discusses how to apply water conservancy project in the comprehensive water environment management of Taihu Lake basin, and makes a comparative analysis of the benefits obtained from it. The final results show that hydraulic engineering plays an active role in the comprehensive treatment of water environment.

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.