COMPARISON OF DIFFERENCES IN GROUND MOTION DATA OBTAINED BY DIFFERENT SITE CONDITIONS OF THE JISHISHAN M6.2 EARTHQUAKE

doi:10.3969/j.issn.0253-4967.2025.04.20240127

Abstract

Abstract:

The China earthquake early warning network(EEW)consists of three kinds of instruments: seismometer, strong motion accelerometer, and intensity meter deployed in rock sites, soil sites, and ground sites of communication stations. Of all the deployment sites, intensity meters account for the majority, and the ground motion data observed by intensity meters plays an essential role in the early warning network, as it can affect the timely response of the early warning system and the accuracy of the early warning parameters. Since the ground site of the communication station for the intensity meters differs significantly from traditional rock and soil sites, it requires more effort to validate the consistency of ground motion data obtained under various site conditions. Therefore, to determine whether deployment conditions can exert significant influence on ground motion data, a method is proposed to carry out the data comparison work using the permutation test technique based on computer simulation, and an application example of the proposed method is also demonstrated using the early warning data obtained from the Jishishan earthquake.

The implementation of the proposed method consists of two steps. Firstly, constructing a comparison dataset. Collocated data from two different site conditions are selected, and then data features extracted from the observations obtained from the two matched sites are used to construct a data pair. A set of data pairs is formed using all the observations from collocated sites, which aims at ensuring that all the influencing factors of ground motion, except the site condition, are similar between the data pairs. Secondly, testing data differences. To test whether there are significant data differences between the two matched data pairs, a computer simulation-based permutation test is used to create a distribution of the statistic quantity and then to compare the actual statistic with a pre-set confidence level to determine whether the data difference is significant. Assuming there is no data difference between the matched data pairs, randomly resampling is performed from the matched data pairs to construct another data pair. A statistical distribution can be obtained after repeating the resampling process many times. If the occurrence probability of the statistic obtained from actual observations, which can be counted from the many times resampled results, is less than the pre-set confidence level, there is a significant difference between the two groups of data pairs; otherwise, there is no significant difference between the two groups of data pairs.

The M6.2 Jishishan earthquake, which occurred in the northwest loess-covered region in China on December 18, 2023, is used to demonstrate the application of the abovementioned method and its implementation steps. Three kinds of comparison processes are shown in the paper, including the comparison processes between data from the rock site and the soil site, from the rock site and the communication station sites, and from the soil site and the communication station sites.

Based on the results obtained by comparing the three cases mentioned above, some conclusions are drawn as follows: (1)In the loess covering region, ground motion data from the communication station sites are significantly greater than that from the soil sites, which are then significantly greater than that from the rock sites. (2)Consistency correction is required when using ground motion data from different site conditions together, as there are significant data differences among the three site conditions. (3)Although both the communication station sites and soil site can be classified as soil condition, the burying depths of the instrument base into the soil layer are different, resulting to the significant greater of the ground motion data obtained from the communication station sites than that of the soil site, which can be explained by the obvious amplification effect of the surface loess layer.

The method proposed in this paper is suitable for the quantitative analysis of complex data, and the results of the Jishishan earthquake have significant reference value for the research and related applications of early warning ground motion data.

Key words: the Jishishan earthquake, the China Earthquake Early Warning Network, ground motion data, deployment condition, method to distinguish data difference, permutation test

摘要：

中国地震预警网由部署于基岩、土层、基站地面3类场地条件的测震仪、强震仪和烈度计仪器构成, 其中场地条件为基站地面的烈度计占绝大多数。为了比较不同场地条件是否对地震动数据造成明显影响, 文中提出了利用计算机模拟置换实验对2组数据展开对比分析的方法。以积石山M6.2地震获得的预警数据为例, 文中展示了利用建议方法对基岩与基站地面、基岩与土层及土层与基站地面两两场地观测数据的比较分析过程。对该地震预警数据的分析结果表明: 在黄土地区, 基站地面观测的地震动数据大于土层场地, 土层场地观测数据大于基岩场地; 仪器安装的场地条件会对地震动数据产生显著影响; 基站地面与土层场地观测数据的显著差异与地表黄土层的放大作用有关。文中所述方法适用于复杂数据的定量比较和分析, 积石山地震数据分析结果也表明联合应用黄土区不同场地条件的预警数据时需考虑数据差异性问题。

关键词: 积石山地震, 中国地震预警网, 地震动数据, 场地条件, 数据差异性, 置换实验

REN Jia, WANG Xiu-ying, ZHAO Guo-cun, FAN Xi-wei, GAO Peng, ZHANG Shan-shan, MA Zhi-xia. COMPARISON OF DIFFERENCES IN GROUND MOTION DATA OBTAINED BY DIFFERENT SITE CONDITIONS OF THE JISHISHAN M6.2 EARTHQUAKE[J]. SEISMOLOGY AND GEOLOGY, 2025, 47(4): 1075-1089.

任佳, 王秀英, 赵国存, 范熙伟, 高鹏, 张珊珊, 马志霞. 积石山M6.2地震不同场地地震动观测数据的差异性分析[J]. 地震地质, 2025, 47(4): 1075-1089.

Figures/Tables 11

Fig. 1 Distribution of early warning stations within 350km epicentral distance of the Jishishan earthquake.

Table1 Information of observation points within 350km epicentral distance of the Jishishan earthquake

仪器类型	观测点数量	观测物理量	部署场地
测震仪	97	速度	基岩场地
强震仪	97	加速度	基岩场地
	302		土层场地
烈度计	1110	加速度	基站地面

Fig. 2 Variation of PGAs from collocated rock sites and ground sites of communication station with epicentral distance.

Fig. 3 Distribution of permutation test results for the mean PGA difference between collocated rock sites and ground sites of communication station.

Table2 Actual observed statistics and their occurrence probability for collocated rock sites and ground sites of the communication station

N		E		Z
M_diff/gal	p	M_diff/gal	p	M_diff/gal	p
17.59	<1×10^-4	17.08	<1×10^-4	10.17	<1×10^-4

Fig. 4 Variation of PGAs from collocated rock sites and soil sites with epicentral distance.

Fig. 5 Distribution of permutation test results for the mean PGA difference between collocated rock sites and ground sites of the communication station.

Table3 Actual observed statistics and their occurrence probability for collocated rock sites and soil sites

N		E		Z
M_diff/gal	p	M_diff/gal	p	M_diff/gal	p
6.25	<1×10^-4	6.50	0.0001	4.02	0.0002

Fig. 6 Variation of PGAs from collocated ground sites of the communication station and soil sites with epicentral distance.

Fig. 7 Distribution of permutation test results for the mean PGA difference between collocated ground sites of the communication station and soil sites.

Table4 Actual observed statistics and their occurrence probability for collocated ground sites of the communication station and soil sites

N		E		Z
M_diff/gal	p	M_diff/gal	p	M_diff/gal	p
7.61	0.0034	7.29	0.012	4.47	0.0198

References 20

[1]	金星. 2021. 地震预警与烈度通报——理论与实践[M]. 北京: 科学出版社.
	JIN Xing. 2021. Earthquake early warning and rapid intensity reporting: Theory and practice[M]. Science Press, Beijing (in Chinese).
[2]	李飞. 2023. 基于通信基站的地震烈度速报与预警工程一般站建设[J]. 现代传输, (3): 69—72.
	LI Fei. 2023. Construction of general stations for earthquake intensity rapid reporting and early warning project based on communication stations[J]. Modern Transmission, (3): 69—72 (in Chinese).
[3]	刘传正, 梁宽, 王秀英. 2024. 积石山 M_S6.2 地震区大沙沟泥土流灾害链成因分析[J]. 地质论评, 70(3): 960—974.
	LIU Chuan-zheng, LIANG Kuan, WANG Xiu-ying. 2024. Cause analysis of disaster chain of soil flow in Dashagou Basin in Jishishan M_S6.2 magnitude earthquake area[J]. Geological Review, 70(3): 960—974 (in Chinese).
[4]	苏瑞欢, 袁道阳, 郑文俊, 等. 2024. 2023年甘肃积石山 M_S6.2 地震地表破裂及震害特征分析[J]. 地球物理学报, 67(9): 3454—3471.
	SU Rui-huan, YUAN Dao-yang, ZHENG Wen-jun, et al. 2024. Surface rupture and damage characteristics of the 2023 M_S6.2 Jishishan earthquake, Gansu[J]. Chinese Journal of Geophysics, 67(9): 3454—3471 (in Chinese).
[5]	王运生, 赵波, 吉锋, 等. 2024. 2023年甘肃积石山 M_S6.2 地震震害异常的启示[J]. 成都理工大学学报(自然科学版), 51(1): 1—8.
	WANG Yun-sheng, ZHAO Bo, JI Feng, et al. 2024. Preliminary insights into hazards triggered by the 2023 Jishishan M_S6.2 earthquake, Gansu Province[J]. Journal of Chendu University of Technology(Science & Technology Edition), 51(1): 1—8 (in Chinese).
[6]	夏坤, 张令心, 董林. 2018. 汶川地震远震区黄土场地地震反应特征分析[J]. 地震工程学报, 40(3): 504—511.
	XIA Kun, ZHANG Ling-xin, DONG Lin. 2018. Earthquake response analysis for loess sites in far-field of Wenchuan earthquake[J]. China Earthquake Engineering Journal, 40(3): 504—511 (in Chinese).
[7]	肖武军. 2023. 国家地震烈度速报与预警工程[J]. 防灾博览, (1): 18—23.
	XIAO Wu-jun. 2023. National earthquake intensity rapid reporting and early warning project[J]. Overview of Disaster Prevention, (1): 18—23 (in Chinese).
[8]	张建勇, 席楠, 徐泰然, 等. 2024. 国家中心地震烈度速报与预警技术平台[J]. 中国地震, 40(1): 54—68.
	ZHANG Jian-yong, XI Nan, XU Tai-ran, et al. 2024. The center technology platform for National Earthquake Intensity Quick Reporting and Early Warning[J]. Earthquake Research in China, 40(1): 54—68 (in Chinese).
[9]	赵国峰, 高楠, 杨大克. 2022. 国家地震烈度速报与预警工程建设进展[J]. 地震地磁观测与研究, 43(3): 165—171.
	ZHAO Guo-feng, GAO Nan, YANG Da-ke. 2022. Construction progress of the National Seismic Intensity Rapid Reporting and Early Warning project[J]. Seismological and Geomagnetic Observation and Research, 43(3): 165—171 (in Chinese).
[10]	中国地震局. 2015. 地震台站建设规范地震烈度速报与预警台站(DB/T 60-2015)[S]. 中华人民共和国地震行业标准.
	China Earthquake Agency. 2015. Specification for the construction of seismic station-Seismic intensity rapid reporting and earthquake early warning station(DB/T 60-2015)[S]. Earthquake Industry Standard of the People’s Republic of China (in Chinese).
[11]	Allen R M. 2007. The Elarms earthquake early warning methodology and application across California[C]. Berlin Heidelberg: Springer(Earthquake Early Warning Systems): 21—44.
[12]	Chung A I, Meier M A, Andrews J, et al. 2020. ShakeAlert earthquake early warning system performance during the 2019 Ridgecrest earthquake sequence[J]. Bulletin of the Seismological Society of America, 110(4): 1904—1923.
[13]	Fisher R A, Yates F. 1938. Statistical Tables for Biological, Agricultural and Medical Research[M]. London: Oliver & Boyd: 20—21.
[14]	Hesterberg T, Monaghan S, Moore D S, et al. 2003. Bootstrap methods and permutation tests: Companion Chapter 18 to the Practice of Business Statistics[M]. W H Freeman and Company, New York.
[15]	Hsiao N C, Wu Y M, Shin T C, et al. 2009. Development of earthquake early warning system in Taiwan[J]. Geophysical Research Letters, 36(5): L00B02.
[16]	Kodera Y, Hayashimoto N, Tamaribuchi K, et al. 2021. Developments of the nationwide earthquake early warning system in Japan after the 2011 M_W9.0 Tohoku-Oki earthquake[J]. Frontiers in Earth Science, 9:726045.
[17]	Murphy J R, Davis A H, Weaver N L. 1971. Amplification of seismic body waves by low-velocity surface layers[J]. Bulletin of the Seismological Society of America, 61(1): 109—145.
[18]	Peng C, Jiang P, Chen Q, et al. 2019. Performance evaluation of a dense MEMS-based seismic sensor array deployed in the Sichuan-Yunnan border region for earthquake early warning[J]. Micromachines, 10(11): 735.
[19]	Peng C, Jiang P, Ma Q, et al. 2022. Chinese Nationwide Earthquake Early Warning System and its performance in the 2022 Lushan M6.1 earthquake[J]. Remote sensing, 14(17): 4269.
[20]	Suárez G, Espinosa-Aranda J M, Cuéllar A, et al. 2018. A dedicated seismic early warning network: The Mexican seismic alert system(SASMEX)[J]. Seismological Research Letters, 89(2A): 382—391.