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AbstractStrengthening the study of trapped personnel distribution in an earthquake not only ensures the efficiency and orderliness of earthquake rescue but also maximizes the survival probability of trapped people and reduces casualties. Through distinguishing the use functions of buildings, this study constructed an assessment model of people trapped in a collapsed single building due to an earthquake and evaluated the risk of individuals trapped in Xichang area, China. The results showed that the risk level of people trapped in collapsed buildings in Xichang area is high. Under the influence of seismic intensity IХ, the proportion of people trapped in collapsed buildings has reached approximately 1–2% of the total regional population. The risk level of trapped people in the daytime and at nighttime caused by the same earthquake scenario is significantly different. The number of potentially trapped people at night is nearly two times greater than that during the day. The low seismic performance of buildings is the most important factor causing the high risk of people trapped. The use function of buildings also has an important effect on the risk assessment of the trapped population distribution. Some suggestions are provided for the local risk reduction of people trapped in earthquakes.

AbstractRapid determination of the most-affected areas after an earthquake can help with emergency relief efforts, which is also an essential part of postearthquake relief work. Mobile signaling data contains rich user information, which helps determine the most-affected areas. This study investigated the 2022 Mw 6.6 Luding earthquake in Sichuan, China; the relationship between mobile signaling data and the seismically most-affected area was explored using comparative, regression, and spatial interpolation analysis. The results revealed that mobile signaling data changed abnormally following the earthquake and differed across high- and low-intensity zones. There was a significant correlation between mobile signaling data and intensity. Additionally, the most-affected areas estimated using mobile signaling data were comparable to the actual seismic damage. Therefore, mobile signaling data can provide a reference for determining the most-affected areas and assist in emergency relief efforts after an earthquake.