## 1 Introduction  
Earthquake-triggered landslides occur when slope materials slide due to strong seismic motion. These landslides directly jeopardize the lives and property of individuals in earthquake-prone areas. For instance, the 2008 Wenchuan earthquake led to a landslide in the western region of Beichuan County, resulting in the burial of approximately 1600 people and the destruction of hundreds of houses [1]. Additionally, earthquake-triggered landslides can disrupt traffic, damage critical infrastructure, and create blockages in rivers, leading to the formation of barrier lakes in affected areas, all of which have detrimental effects on rapid rescue efforts [1, 2, 3, 4, 5, 6]. Therefore, the efficient, accurate, and rapid assessment of high-risk areas for earthquake-triggered landslides holds great significance in reducing losses caused by secondary disasters during the rapid response stage following earthquakes. Currently, there are numerous rapid assessment methods for regional earthquake-triggered landslides, which can be broadly categorized into two groups. The first category consists of indicator system methods, which involve developing evaluation techniques based on various parameters related to earthquake-triggered landslides and faults, such as the magnitude, the distance from the epicenter, and the distribution of rivers and roads. These methods include models such as logistic regression [7, 8, 9], support vector machines [10, 11], and neural networks [12, 13]. The second category is that of the Newmarkmethod, which is based on the principle of mechanical equilibrium and has been widely applied in earthquake landslide risk assessments worldwide [14]. After the 8.0-magnitude Wenchuan earthquake occurred, global scholars conducted preliminary research on the landslide inversion results of the Wenchuan earthquake based on the Newmark method, and they achieved relatively good assessment effects [15; 16; 17; 18]. [PERSON] used the critical acceleration in the Newmark method as a criterion to quickly assess the landslide risk induced by the 2013 Lushan 7.0-magnitude earthquake [19]. Researchers used the Newmark method to conduct earthquake-triggered landslide assessments for the 2014 Ludian earthquake [20; 21]. The Newmark method was also applied to the seismic landslide risk assessment of the 2015 Nepal 7.9-magnitude earthquake [22; 23]. The Newmark method was also applied in the assessment of landslide risks triggered by the 2017 Juzhaigou 7.0-magnitude earthquake [24; 25]. [PERSON] used the Newmark method to assess the landslide risk of the 2022 Qinghai Madduo 7.4-magnitude earthquake [26]. Furthermore, [PERSON] used the Newmark method to assess the seismic landslide risk of the Xianshuihe Fault zone due to seismic conditions [27]. Unlike indicator system methods, the Newmark method is not constrained by differences in the geological background and provides evaluation results with relatively clear physical significance. This method has been used in various earthquake cases around the globe [28; 29; 30; 31]. Within the Newmark method, there are two main approaches for determining earthquake landslide risk: the difference method and the cumulative displacement method. The difference method assesses risk based on the magnitude of the difference between the peak ground acceleration (_PGA_) and the critical acceleration of rock-soil mass, with a larger difference indicating a higher risk of earthquake-triggered landslides [23; 24; 26; 32; 33]. On the other hand, the cumulative displacement method uses the relationship between the _PGA_ and permanent cumulative displacement to calculate the displacement of rock-soil mass and determine landslide risk based on the magnitude of the displacement value [27; 34; 35; 36; 37]. While both methods have shown effectiveness in previous earthquake case studies, further comparative research is required to fully understand their respective advantages, disadvantages, and applicability.