基于手机信令数据判别地震影响重灾区——以2023年积石山MS6.2地震为例

doi:10.3969/j.issn.0253-4967.20240101

地震地质 ›› 2026, Vol. 48 ›› Issue (2): 520-539.DOI: 10.3969/j.issn.0253-4967.20240101

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

郭欣欣¹⁾(), 魏本勇¹^,²⁾^,^*(), 苏桂武¹^,²⁾, 齐文华¹^,²⁾, 张腾飞¹⁾

¹⁾ 中国地震局地质研究所, 北京 100029
²⁾ 中国地震局地震与火山灾害重点实验室, 北京 100029

收稿日期:2025-08-05 修回日期:2025-10-10 出版日期:2026-04-20 发布日期:2026-05-14
通讯作者: * 魏本勇, 男, 1982年生, 博士, 研究员, 主要从事地震灾害风险评估、灾害认知与响应、灾后恢复和地方减灾研究, E-mail: bywei1982@ies.ac.cn。
作者简介:
郭欣欣, 女, 1997年生, 现为中国地震局地质研究所第四纪地质学专业在读博士研究生, 研究方向为地震灾害风险分析, E-mail: 18536062880@163.com。
基金资助:
国家自然科学基金(42177453); 中国地震局地质研究所基本科研业务专项(IGCEA1905); 西藏自治区科技计划项目(XZ202402ZD0001)

ASSESSING SEVERELY AFFECTED AREAS BY EARTHQUAKE USING MOBILE SIGNALLING DATA: A CASE STUDY OF THE 2023 M_S6.2 JISHISHAN EARTHQUAKE, CHINA

GUO Xin-xin¹⁾(), WEI Ben-yong¹^,²⁾^,^*(), SU Gui-wu¹^,²⁾, QI Wen-hua¹^,²⁾, ZHANG Teng-fei¹⁾

¹⁾ Institute of Geology, China Earthquake Administration, Beijing 100029, China
²⁾ Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China

Received:2025-08-05 Revised:2025-10-10 Online:2026-04-20 Published:2026-05-14

摘要/Abstract

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

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

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.

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.

图/表 14

图1 研究区区位图

Fig. 1 The location of the study area.

表1 研究数据概况

Table 1 Overview of Research Data

图2 GeoHash格网样本点分布

Fig. 2 Distribution of GeoHash grid sample point.

图3 研究数据时间范围示意图

Fig. 3 Schematic diagram of the research data timeline.

表2 不同烈度区不同时间有意义的GeoHash格网样本数量

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

图4 环比时序变化情况图a中, 设备数量的横坐标为具体时间; 图b中, 环比异常率的横坐标, 1代表震后1min(00:00)相对震前1min(23:58)的变化率; 2代表震后2min(00:01)相对震前2min(23:57)的变化率; 以此类推, 120代表震后120min(01:59)相对震前120min(21:59)的变化率

Fig. 4 Day-on-day time variation.

图5 同比时序变化情况

Fig. 5 Year-on-year time variation.

图6 手机信令采集设备数量平均值对比震前平均数量为地震当天21:59—23:58时间段采集设备数量平均值; 震后平均数量为地震当天00:00—01:59时间段采集设备数量平均值; 变化率为震后平均数量相对于震前平均数量的变化率

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

图7 不同烈度区环比时序变化

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

图8 地震前、后2d手机信令采集设备数量平均值对比变化率1为地震当天23:59—01:59时间段采集设备数量平均值相对于地震前一天21:59—22:58采集设备数量平均值的变化率; 变化率2为地震当天23:59—01:59时间段采集设备数量平均值相对于地震前一天23:59—01:59采集设备数量平均值的变化率

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

图9 不同烈度区同比时序变化

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

图10 不同烈度区同比异常率时序变化

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

表3 手机信令异常率与地震影响变量相关关系

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^**

图11 利用手机信令同比异常率判断地震影响重灾区

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

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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

ASSESSING SEVERELY AFFECTED AREAS BY EARTHQUAKE USING MOBILE SIGNALLING DATA: A CASE STUDY OF THE 2023 M_S6.2 JISHISHAN EARTHQUAKE, CHINA

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基于手机信令数据判别地震影响重灾区——以2023年积石山MS6.2地震为例

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

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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

ASSESSING SEVERELY AFFECTED AREAS BY EARTHQUAKE USING MOBILE SIGNALLING DATA: A CASE STUDY OF THE 2023 M_S6.2 JISHISHAN EARTHQUAKE, CHINA