Research On Rapid Assessment Method Of Most-affected Areas By Earthquakes Based On Mobile Signaling Data

Earthquake disasters are characterized by their suddenness and unpredictability,often causing massive destruction,numerous casualties,and significant socio-economic losses.Rapid assessment of most-affected areas after an earthquake is crucial for the implementation of emergency relief efforts and is an integral part of post-earthquake rescue operations.Mobile signaling data,as a product of current internet technology,contains abundant user information and offers advantages such as spatial continuity,real-time acquisition,and large data volume.To a certain extent,mobile signaling data can reflect the real-time distribution of the population.Due to damage to communication facilities or power systems,earthquake-stricken areas,particularly heavily affected regions,may experience a phenomenon known as "mobile silence." Based on changes in mobile signaling,valuable assistance can be provided for the swift assessment of most-affected areasThis study takes the examples of the 7.0 magnitude Jiuzhaigou earthquake on August 8,2017,and the 6.8 magnitude Luding earthquake on September 5,2022 in Sichuan Province.Based on the mobile signaling data collected during these two earthquakes,various methods such as comparative analysis,correlation analysis,regression analysis,and spatial interpolation analysis were employed to explore the relationship between changes in mobile signaling data and the affected areas of the earthquakes.The study also attempted to establish a rapid assessment method for mostaffected areas based on mobile signaling data and validated the results.The research findings are as follows:(1)Due to the impact of the earthquake,mobile phone signaling data undergoes significant abnormal changes compared to the pre-earthquake period.There are varying degrees of abnormal responses between different signaling indicators and different data precisions of the same indicator.Mobile signaling data can accurately reflect the extent of earthquake damage,and the degree of abnormal changes in mobile signaling data varies in different intensity zones.The magnitude of abrupt changes in mobile signaling data is greater in high-intensity zones compared to low-intensity zones.Additionally,the recovery of mobile signaling data after the earthquake can also reflect the degree of earthquake impact.In areas with higher intensity levels,the recovery trend of mobile signaling data occurs later.(2)There is a significant correlation between the change in the quantity of mobile phone signaling data and the intensity of the earthquake.However,the correlation levels differ for different signaling indicators at different data precisions.Specifically,compared to 5-digit GeoHash precision data,the correlation between 6-digit GeoHash precision mobile phone signaling data and earthquake intensity is higher.Among the four different signaling indicators,the mobile device indicator exhibits relatively better correlation with intensity.Based on correlation analysis,a rapid assessment model for earthquake intensity zones was constructed,and its accuracy and effectiveness were validated using cross-validation methods.The results indicate that the intensity assessment model based on the average rate of change in mobile device signaling data within the first four minutes of the earthquake demonstrates high accuracy and applicability.By utilizing this assessment model,the distribution of earthquake intensity zones can be evaluated quickly and with reasonable accuracy.(3)Based on the quantity changes of mobile phone signaling data at different data precision and time precision,the Kriging interpolation method was utilized to simulate the location and extent of most-affected areas caused by the earthquake.The interpolation results were then validated by overlaying them with the officially published intensity impact field and distribution of population fatalities.The results demonstrate that the interpolation based on the abnormal changes in mobile phone signaling data accurately simulates the specific range and actual extent of damage in most-affected areas.Specifically,the interpolation results based on the average rate of change in mobile device signaling data within the first five minutes of the earthquake can more accurately reflect the location and extent of most-affected areas,thereby correcting the intensity map and assisting in precise post-earthquake emergency relief efforts.In conclusion,mobile signaling data offers several advantages,including wide coverage,high timeliness,and quick acquisition.It can provide real-time insights into population distribution changes and exhibits a strong response relationship with the impact of earthquake disasters.Mobile signaling data can rapidly and accurately reflect the extent of earthquake damage,making it an effective assessment indicator for the rapid assessment of most-affected areas Therefore,by further exploring mobile signaling data,valuable insights can be obtained to facilitate the quick and accurate assessment of most-affected areas in the aftermath of an earthquake,thereby aiding in the prompt and precise implementation of emergency relief efforts.