Reexamining the Seismological Implications of the Present-day Stress State of the Yingxiu-Beichuan Fault after the Wenchuan Earthquake

2016 ◽  
Vol 90 (2) ◽  
pp. 567-577 ◽  
Author(s):  
QIN Xianghui ◽  
CHEN Qunce ◽  
FENG Chengjun ◽  
DU Jianjun ◽  
WU Manlu ◽  
...  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Deyu Yin ◽  
Yun Dong ◽  
Qifang Liu ◽  
Yuexin She ◽  
Jingke Wu ◽  
...  

In order to reproduce the rupture history of the 2008 Mw8.0 Wenchuan earthquake, the teleseismic and strong-motion records are adopted. Based on a multiple-segment, variable-slip model, the finite fault inversion method is utilized to recover the rupture process. The results are as follows: (1) the rupture duration of the Wenchuan earthquake is about 100 s, and the released seismic moment is 1.24 × 1021 N·m, equal to the moment magnitude Mw8.0. There are 5 asperities on the fault plane, indicating that the earthquake is composed of at least 5 subevents. (2) The slip is mainly distributed on the Beichuan fault, indicating that the Beichuan fault is the main rupture fault. On the southern part of the Beichuan fault, the dislocation underside the Longmenshan area and Hongkou-Yingxiu near-surface area is dominated by thrust, and the maximum slip is 11.8 m. Slip between the Yuejiashan and Qingping area is dominated by thrust. On the northern part of the Beichuan fault, the area under Beichuan is dominated by thrust, the slip under Nanba is thrust and strike, near Qingchuan, the slip turns into the strike slip, and the maximum slip is 13.1 m. The dislocation under Bailu is also dominated by thrust, with maximum slip 8.9 m. (3) The rupture of the Wenchuan earthquake is mainly a unilateral rupture to the northeast. The rupture started at the low dip angle part of the Beichuan fault, and after 3 s, it propagated to the Pengguan fault. After 10 s, the largest asperity under Longmenshan in the south section of the Beichuan fault began to break, lasting for about 24 s. Then, the Xiaoyudong fault was triggered by the Pengguan fault, and the bilateral rupture of the high dip angle part of the Beichuan fault started at about 6 s. South section of the Beichuan fault began to break at about 35 s, and at 43 s, 63 s, and 80 s, the rupture extended to Beichuan, Nanba, and Qingchuan areas.


2015 ◽  
Vol 58 (2) ◽  
Author(s):  
Pan Xiong ◽  
Xuhui Shen ◽  
Xingfa Gu ◽  
Qingyan Meng ◽  
Liming Zhao ◽  
...  

<p>The paper has developed Robust Satellite data analysis Technique (RST) to detect seismic anomalies in the case of the Wenchuan earthquake occurred on May 12, 2008, using the bi-angular Advanced Along-Track Scanning Radiometer (AATSR) gridded brightness temperature (BT) data based on spatial/temporal continuity and confutation analysis. The proposed method has been applied to analyze the Wenchuan earthquake with longitude from 95°E to 111°E and latitude from 23°N to 39°N, and a full data-set of 7 years data from 2003 to 2009 during the months of April and May has been analyzed. Combining with the rupture structure data of the Wenchuan earthquake, the analyzed results indicate that: the main structure activity (the Yingxiu-Beichuan fault) characteristics of the Wenchuan earthquake can be identified and extracted using the bi-angular AATSR nadir and forward brightness temperature data, and the seismic infrared radiation anomalies detected by the AATSR nadir brightness temperature is more close to the Yingxiu-Beichuan fault.</p>


Landslides ◽  
2021 ◽  
Author(s):  
Fan Yang ◽  
Xuanmei Fan ◽  
Srikrishnan Siva Subramanian ◽  
Xiangyang Dou ◽  
Junlin Xiong ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5191
Author(s):  
Chang Li ◽  
Bangjin Yi ◽  
Peng Gao ◽  
Hui Li ◽  
Jixing Sun ◽  
...  

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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