## 2 Methods and Data
### Data Source  
It is known from Formula (1) that, except for the already determined \(t\) and \(m\), the calculation of \(F_{\rm s}\) also requires the determination of the terrain slope and rock-soil physical mechanical parameters within the study area. Slope data were derived from pre-earthquake shuttle radar topography mission (SRTM) DEMs with a 30 m resolution, which were sourced from the official website of the United States Geological Survey (www.usgs.gov, accessed on 11 September 2022).  
In addition, the physical mechanical parameters of the rock-soil mass needed for calculating \(F_{\rm s}\) can be determined using regional geological data. This study utilized geological map data at a scale of 1:200,000 obtained from the National Geological Data Center (www.ngac.cn, accessed on 11 September 2022). In accordance with the engineering classification standard for rock mass (44), the rock-soil masses within the research area were categorized into four types (refer to Figure 3): very hard (I), harder (II), softer (III), and extremely soft (IV).  
In detail, the very hard category predominantly encompassed intermediate acidic intrusive rocks, such as granite and diorite, while the harder types included limestone,  
Figure 3: Categories of rock–soil masses.  
Figure 2: Research flowchart.  
dolomite, calcareous dolomite, and dolomitic limestone. The softer types comprised sedimentary rocks such as conglomerate, mudstone, and slate, while the extremely soft type referred to loose deposits of the Holocene series. Drawing upon the aforementioned standards and relevant studies in neighboring regions [16; 23; 24; 25], we assigned specific values to the physical and mechanical parameters of various rock-soil masses (see Table 1).  
After calculating \(F_{\mathrm{s}}\), it could be used to calculate \(A_{\mathrm{c}}\) via Formula (2). Then, using \(A_{\mathrm{c}}\) and the seismic parameters of this earthquake, the earthquake-triggered landslide risk of the study area due to seismic action could be evaluated. Given the limited number of seismic stations within the earthquake zone, only one station was situated in the study area, located at the perimeter. Consequently, the \(PGA\) data for this study were acquired through transformation and interpolation analysis by leveraging the earthquake intensity map released by the Ministry of Emergency Management of the People's Republic of China (www.mem.gov.cn, accessed on 11 September 2022) and the Chinese seismic intensity scale (refer to Figure 4) [45]. The interpolated \(PGA\) data ranged from 174 to 830 gal, exhibiting consistency with both the seismic station recordings within the earthquake zone and the seismic intensity map released by the Ministry of Emergency Management of the People's Republic of China.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rock–Soil Mass** & \(c^{\prime}\)**(kPa)** & \(\varphi^{\prime}\)**(c)** & \(\gamma\)**(kN/m\({}^{3}\))** \\ \hline I & 35 & 38 & 26 \\ II & 28 & 33 & 23 \\ III & 22 & 24 & 22 \\ IV & 15 & 10 & 16 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of the rock–soil masses in the study area.  
Figure 4: Distribution of \(PGA\).