scholarly journals Electromagnetic Anomalies around The Wenchuan Earthquake and Their Relationship with Earthquake Preparation

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Xuemin Zhang ◽  
Xuhui Shen
Keyword(s):  
Wenchuan Earthquake ◽  
Coupling Mechanism ◽  
Preparation Process ◽  
Mechanism Analysis ◽  
Electromagnetic Parameters ◽  
Earthquake Preparation ◽  
The Wenchuan Earthquake ◽  
Electromagnetic Precursors ◽  
The Relationship

Electromagnetic precursors before the Wenchuan earthquake on May 12, 2008 were collected and summarized on the basis of related published papers. The relationship between electromagnetic anomalies and different earthquake preparation stages was analyzed, and an entire seismic preparation process was constructed according to corresponding anomalies in different electromagnetic parameters. It is illustrated that stereo electromagnetic observation is useful in the understanding of earthquake preparation mechanism. It is inevitable that a lot of problems exist in anomaly distinguishing and coupling mechanism analysis, which needs further studies in future.

Comparison of Injury Epidemiology Between the Wenchuan and Lushan Earthquakes in Sichuan, China

2014 ◽  
Vol 8 (6) ◽  
pp. 541-547 ◽  
Author(s):  
Yang Hu ◽  
Xi Zheng ◽  
Yong Yuan ◽  
Qiang Pu ◽  
Lunxu Liu ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Brain Injuries ◽  
Medical Center ◽  
Seismic Intensity ◽  
Lushan Earthquake ◽  
Anatomical Location ◽  
Trauma Centers ◽  
Patient Admissions ◽  
The Wenchuan Earthquake ◽  
The Relationship

AbstractObjectiveWe aimed to compare injury characteristics and the timing of admissions and surgeries in the Wenchuan earthquake in 2008 and the Lushan earthquake in 2013.MethodsWe retrospectively compared the admission and operating times and injury profiles of patients admitted to our medical center during both earthquakes. We also explored the relationship between seismic intensity and injury type.ResultsThe time from earthquake onset to the peak in patient admissions and surgeries differed between the 2 earthquakes. In the Wenchuan earthquake, injuries due to being struck by objects or being buried were more frequent than other types of injuries, and more patients suffered injuries of the extremities than thoracic injuries or brain trauma. In the Lushan earthquake, falls were the most common injury, and more patients suffered thoracic trauma or brain injuries. The types of injury seemed to vary with seismic intensity, whereas the anatomical location of the injury did not.ConclusionsGreater seismic intensity of an earthquake is associated with longer delay between the event and the peak in patient admissions and surgeries, higher frequencies of injuries due to being struck or buried, and lower frequencies of injuries due to falls and injuries to the chest and brain. These insights may prove useful for planning rescue interventions in trauma centers near the epicenter. (Disaster Med Public Health Preparedness. 2014;8:541-547)

Catastrophic debris flows triggered by the 20 August 2019 rainfall, a decade since the Wenchuan earthquake, China

Landslides ◽  
2021 ◽  
Author(s):  
Fan Yang ◽  
Xuanmei Fan ◽  
Srikrishnan Siva Subramanian ◽  
Xiangyang Dou ◽  
Junlin Xiong ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Debris Flows ◽  
The Wenchuan Earthquake

scholarly journals Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5191
Author(s):  
Chang Li ◽  
Bangjin Yi ◽  
Peng Gao ◽  
Hui Li ◽  
Jixing Sun ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Remote Sensing Images ◽  
Machine Learning Methods ◽  
The Wenchuan Earthquake ◽  
Causative Factors ◽  
Multiple Structures ◽  
Sample Set ◽  
Larger Sample ◽  
Structure Layer ◽  
Earthquake Area

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.

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