Co-seismic landslide is one of the most destructive secondary earthquake disasters and can cause serious damage on a large scale, especially in mountainous areas. Understanding the risk of co-seismic landslides is the basis for predicting the risk, risk and risk of co-seismic landslides in potential earthquake areas, and is of great significance to earthquake prevention and disaster resistance. However, there are many factors that control the occurrence of coseismic landslides, including meteorological hydrology, topography, geological structure, stratigraphic lithology, degree of rock mass fracture, coseismic faults, earthquakes, site intensity, human activities, land use, etc. The various factors influence and are interdependent. To this end, predecessors developed information quantity method, principal component analysis method, frequency ratio method, multivariate statistical method, logistic regression method, Monto Carlo method, fuzzy mathematics method, neural network method, etc. to conduct evaluations of the proneness, risk and risk of co-quake landslides. However, due to unclear main control factors, existing research often requires collecting massive data from various factors. Based on the discussion of the statistical relationship between various factors and the spatial distribution of co-seismic landslides, evaluation of the proneness, risk and risk of co-seismic landslides in potential seismic areas is carried out, making it difficult to efficiently and accurately realize the spatial distribution prediction of co-seismic landslides.
In response to the above problems, Engineer Zou Yu, Institute of Geology and Geophysics, Chinese Academy of Sciences, Researcher Qi Shengwen , associate researcher Guo Songfeng, etc., took the Mw6.1 magnitude earthquake that occurred in Ludian County, Yunnan Province, China as an example to conduct research. The earthquake was caused by the hidden fault (Baoguan-Xiaohe Fault) that was not mapped in the early stage, and the distribution of co-seismic landslides did not spread along both sides of the seismic fault, such as the situation revealed by the Wenchuan earthquake. Based on the established database of coseismic landslides and impact factor, the researchers revealed the main factors of spatial distribution of coseismic landslides through spatial analysis. By analyzing the pre- and post-seismic landslide data, combined with image interpretation and field survey, a total of 1,414 landslides were identified in the research area with an area of 704.7 km2, and a spatial database of Ludian earthquake landslides was established (Figure 1). A detailed geological background map of the tectonics and lithologies of the research area was drawn. The research uses the geographical information system (GIS) to analyze the spatial characteristics of landslides. Through the spatial analysis of GIS, the correlation between landslide occurrence and factors such as slope, elevation, slope direction, earthquake intensity, distance from seismic fault (hereinafter referred to as "fault distance"), distance from epicenter, lithology, distance from non-seismic faults, distance from rivers, and distance from highways.
researchers used spatial analysis methods to analyze the relationship between various factors and the proneness of co-seismic landslides. On this basis, they used the area method under the curve to study the correlation between various factors. Through statistical analysis, it was found that slope and fault distance are two controlling factors for the proneness of coseismic landslides. With the increase of fault distance, the development density of coseismic landslides showed a significant exponential downward trend, but with the increase of slope, coseismic landslide density showed a good Weber cumulative distribution (Figure 2 and Figure 3). The influence of other factors is controlled by these two factors, and the contribution to the proneness of coseismic landslides is reflected through the two factors of slope and fault distance. Taking elevation as an example, the relationship between slope, fault distance and elevation was studied and analyzed. In the elevation area within the range of 700-1200 m, the percentage of slope slope below 10° is about 9%, and the percentage of slope slope below 28% is about 20°; while in the elevation area within the range of 2700 m, the percentage of slope slope below 10° is about 30%. The percentage below 20° is approximately 74%. It can be seen that as the elevation increases, the slope gradually decreases (Figure 4). Meanwhile, in the elevation area within 700-1200 m, the percentage of fault distance within 3 km is about 29%, and the percentage within 6 km is about 52%; in the elevation area of 2700 m, the percentage of fault distance within 3 km is about 0 and the percentage within 6 km is about 18%. It can be seen that as the elevation increases, the fault distance gradually increases (Figure 5). This study elaborates on the relationship between slope, fault distance and elevation, revealing that as the elevation increases, the phenomenon of landslide density gradually decreases is the result of the control of slope and fault distance (Figure 6).
This study shows that for hidden coseismic faults, their coseismic landslides are prone to occur similarly to non-hidden coseismic faults. For example, the Wenchuan earthquake (Qi et al, 2010), the slope and fault distance are its control factors. This study is of great significance for the prediction and evaluation of the proneness of co-seismic landslides. Research results related to
were published in Engineering Geology. The research work has been funded by projects such as the second Qinghai-Tibet Plateau Comprehensive Scientific Expedition and Research.
Figure 1 Ludian earthquake location map and co-seismic landslide database
Figure 2 Relationship between landslide density and fault distance
Figure 3 Relationship between landslide density and slope
Figure 4 Relationship between elevation and slope
Figure 5 Relationship between elevation and fault distance
Figure 6 Relationship between landslide density and elevation
Source: Institute of Geology and Geophysics, Chinese Academy of Sciences
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