Institute of Geology, China Earthquake Administration, Beijing 100029, China.
Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China.
Int J Environ Res Public Health. 2022 Aug 27;19(17):10697. doi: 10.3390/ijerph191710697.
Seismic disasters are sudden and unpredictable, often causing massive damage, casualties and socioeconomic losses. Rapid and accurate determination of the scale and degree of destruction of the seismic influence field in an affected area can aid in timely emergency rescue work after an earthquake. In this study, the relationship between the changes in four types of mobile signaling data and the seismic influence field was explored in the 2017 Jiuzhaigou earthquake-hit area, China, by using the methods of comparative analysis, regression analysis and spatial autocorrelation analysis. The results revealed that after the earthquake, the number of mobile signaling significantly decreased. The higher the intensity, the more obvious the reduction of mobile signaling data and the later the recovery time. The Loginmac and WiFi data showed greater sensitivity than Gid and Station. There was a significant correlation between the changes in the mobile signaling numbers and the seismic intensity, which can more accurately reflect the approximate extent of the seismic influence field and the degree of actual damage. The changes in mobile signaling can provide a helpful reference for the rapid determination of seismic influence fields.
地震灾害具有突发性和不可预测性，往往会造成巨大的破坏、人员伤亡和社会经济损失。快速准确地确定受灾地区地震影响场的规模和破坏程度，有助于地震后的及时紧急救援工作。本研究以中国 2017 年九寨沟地震灾区为例，采用对比分析、回归分析和空间自相关分析等方法，探讨了四种类型的移动信号数据与地震影响场的关系。结果表明，地震后移动信号数量显著减少。震级越高，移动信号数据的减少越明显，恢复时间越晚。Loginmac 和 WiFi 数据比 Gid 和 Station 更敏感。移动信号数量的变化与地震烈度之间存在显著相关性，能够更准确地反映地震影响场的大致范围和实际破坏程度。移动信号的变化可为地震影响场的快速确定提供有益参考。
