ASSESSING SEVERELY AFFECTED AREAS BY EARTHQUAKE USING MOBILE SIGNALLING DATA: A CASE STUDY OF THE 2023 MS6.2 JISHISHAN EARTHQUAKE, CHINA

doi:10.3969/j.issn.0253-4967.20240101

Abstract

Abstract:

On December 18, 2023, at 23:59, a M_S6.2 earthquake struck Jishishan County in the Linxia Hui Autonomous Prefecture, Gansu Province, causing significant casualties and widespread damage to infrastructure. Rapid and accurate identification of severely affected areas in the aftermath of an earthquake is a crucial challenge for earthquake emergency management. Mobile signalling data, known for its strong responsiveness to earthquake impacts, presents a valuable tool for swiftly identifying these critical areas. By analysing abnormal variations in mobile signalling data before and after an earthquake, emergency response efforts can be better informed, providing timely and effective support for the allocation of resources and deployment of aid to the most affected regions.
This study uses the Jishishan M_S6.2 earthquake as a case study to systematically explore the effectiveness of mobile signalling data in assessing earthquake-affected areas. Through comparative analysis, correlation analysis, and spatial interpolation techniques, the findings reveal significant anomalies in mobile signalling patterns post-earthquake, with varying response intensities across different seismic zones. Following the Jishishan earthquake, mobile signalling data showed marked anomalies, characterised by an overall increase in detected devices compared to pre-earthquake levels. A strong correlation was found between changes in mobile signalling and the earthquake’s impact field. Compared to the month-on-month anomaly rate, the year-on-year anomaly rate shows a more pronounced correlation with earthquake impact variables, providing a more signalling response to the earthquake’s effects. The spatial extent of severely affected areas and actual damage can be simulated with greater accuracy by applying the Kriging interpolation method based on mobile signalling anomalies. Using the 5-minute average year-on-year anomaly rate, the interpolation simulation offers a clearer distinction between severely and moderately affected areas. Moreover, compared to the distribution of seismic intensity zones, results derived from mobile signalling data are more detailed and precise. Due to the timing of the Jishishan earthquake, the number of detected devices after the event was generally higher than before it.
Although factors such as the earthquake’s timing, terrain conditions, and the integrity of infrastructure can influence changes in mobile signalling, this data remains a highly effective tool for the rapid and accurate identification of severely affected areas. Compared to traditional assessment methods, mobile signalling data reflects real-time population distribution and offers several advantages, including spatial continuity, timely acquisition, and large data volume. These attributes make mobile signalling a promising method for quickly and precisely pinpointing severe areas affected by a disaster.

Key words: Mobile signalling, severely affected areas, rapid assessment, Jishishan earthquake

摘要：

2023年12月18日23时59分, 甘肃省临夏回族自治州积石山县发生 M_S6.2 地震, 此次地震造成了严重的人员伤亡。快速准确判断地震影响重灾区是开展应急救援工作的关键。手机信令数据对地震灾害的影响具有良好的响应关系, 可为快速判断重灾区提供有益帮助。本研究以积石山 M_S6.2 地震为例, 利用对比分析、相关分析和空间插值分析等方法, 探讨了手机信令数据变化对地震影响区的判断效果。结果表明, 手机信令数据在地震后会发生显著的异常变化, 且不同烈度区内手机信令数据响应程度不同; 手机信令数据与烈度存在显著相关关系; 基于手机信令异常变化可以较准确地模拟地震影响重灾区的具体范围和实际破坏情况。手机信令数据的异常变化对震后重灾区的准确判断及应急救援工作的高效开展具有显著应用价值。

关键词: 手机信令, 地震影响重灾区, 快速评估, 积石山地震

GUO Xin-xin, WEI Ben-yong, SU Gui-wu, QI Wen-hua, ZHANG Teng-fei. ASSESSING SEVERELY AFFECTED AREAS BY EARTHQUAKE USING MOBILE SIGNALLING DATA: A CASE STUDY OF THE 2023 M_S6.2 JISHISHAN EARTHQUAKE, CHINA[J]. SEISMOLOGY AND GEOLOGY, 2026, 48(2): 520-539.

郭欣欣, 魏本勇, 苏桂武, 齐文华, 张腾飞. 基于手机信令数据判别地震影响重灾区——以2023年积石山M_S6.2地震为例[J]. 地震地质, 2026, 48(2): 520-539.

Figures/Tables 14

Fig. 1 The location of the study area.

Table 1 Overview of Research Data

Fig. 2 Distribution of GeoHash grid sample point.

Fig. 3 Schematic diagram of the research data timeline.

Table 2 Significant numbers of GeoHash grid sample points in different intensity areas

时间范围	烈度Ⅷ (331km²)	烈度Ⅶ (1514km²)	烈度Ⅵ (6519km²)	总研究区 (8364km²)
C₁₃	20	6	66	92
C₁₅	26	15	83	124
C₁₁₀	38	27	122	187
C₂₃	22	15	65	102
C₂₅	25	23	83	131
C₂₁₀	38	38	111	187

Fig. 4 Day-on-day time variation.

Fig. 5 Year-on-year time variation.

Fig. 6 Comparison of the average number of mobile signalling.

Fig. 7 Day-on-day time variation in different intensity areas.

Fig. 8 Comparison of the average number of mobile signalling before and after two days of the earthquake.

Fig. 9 Year-on-year time variation in different intensity areas.

Fig. 10 Year-on-year abnormality rate time variation in different intensity areas.

Table 3 Correlations between the intensity of mobile signalling and other variables

变量	I_dt	D_dt
C₁₃	-0.215^*	0.214^*
C₁₅	-0.113	0.126
C₁₁₀	-0.166^*	0.223^**
C₂₃	-0.390^**	0.457^**
C₂₅	-0.339^**	0.353^**
C₂₁₀	-0.293^**	0.269^**

Fig. 11 Interpolation results of year-on-year anomaly rate.

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