## 1 Introduction  
In recent years, natural disaster events have become more frequent, including many mega-earthquakes that have caused more serious casualties in combination with climate change [1, 2]. According to data published by the United Nations Office for Disaster Risk Reduction (UNDRR), they predicted that more than 750,000 people would be killed in earthquake disasters between 2001 and 2020, accounting for 59% of all disaster deaths. Although scientists have been searching for ways to predict earthquakes, mankind has not yet been able to fully grasp the mechanism of earthquakes and their precursors. Currently, no country in the world can accurately predict earthquakes [3]. Therefore, an accurate emergency response after an earthquake is an effective and critical means of reducing earthquake casualties. The occurrence of large earthquakes is usually accompanied by the collapse of many buildings and people who are trapped [4]. If emergency rescuers can quickly access trapped people in the disaster area after an earthquake, it can greatly improve the efficiency of emergency rescue and save more lives to a maximum extent. However, after a destructive earthquake, the disaster area often suffers from communication and transportation disruptions and lagging access to disaster information. As a result, rescuers are unable to accurately understand the distribution and number of trapped people in the disaster area in a timely manner, so the establishment of a scientific and effective pressure burial assessment system is crucial for emergency command decisions [5]. Due to the lack of specific disaster information at the early stage of rescue, some researchers have estimated the degree of damage of buildings according to the level of the earthquake disaster, so as to establish a statistical model to predict the total number of large-scale buried personnel [6; 7]. [PERSON] et al. [8] investigated the relationship between collapsed buildings and human casualties. [PERSON] et al. [9] achieved a rapid assessment of the post-earthquake blind field by constructing a correlation model between mortality and injury rates and earthquake intensity at different administrative area levels. [PERSON] et al. [10] combined urban population density with seismic hazard rating to derive potential casualties in seismic hazard risk areas for the specific planning of subsequent rescue needs and investigated the relationship between collapsed buildings and human casualties. [PERSON] et al. [11] established an empirical function for earthquake personnel losses, using the relationship between seismic parameters and the number of casualties reported from historical data. [PERSON] et al. [12] calculated the relationship between multiple earthquake deaths and buildings, and proposed the impact of different building structures on the number of casualties. [PERSON] et al. [13] proposed a method for assessing earthquake crushers based on building collapse, which was based on the relationship between the time of earthquake onset and the number of casualties. [PERSON] et al. [14] proposed a model for estimating the crush and burial rate with the a priori probability of personnel in terms of the indoor population rate and building collapse rate. [PERSON] et al. [15] analyzed the statistics of the number of casualties of historical earthquakes in China and obtained an empirical pressure burial rate calculation equation based on the building collapse rate. Utilizing remote sensing methods, [PERSON] et al. [16] suggested calculating the number of casualties based on the time of the earthquake, the structural material of each building, and the extent of destruction. [PERSON] et al. [17] proposed a robust wavelet v-SVM earthquake casualty estimation model using multi-source data.