地震动预测模型评价研究

张博涵; 王宏伟; 任叶飞; 温瑞智

doi:10.11899/zzfy20230255

地震动预测模型评价研究以2023年2月6日土耳其地震为例

doi: 10.11899/zzfy20230255

张博涵^{1, 2,},
王宏伟^{1, 2, ,},
任叶飞^{1, 2},
温瑞智^{1, 2}

1.
中国地震局工程力学研究所地震工程与工程震动重点实验室, 哈尔滨 150080
2.
地震灾害防治应急管理部重点实验室, 哈尔滨 150080

基金项目: 国家重点研发计划项目（2019YFE0115700）；黑龙江省自然科学基金杰出青年项目（JQ2023E002）

详细信息

作者简介:
张博涵，女，生于1999年。硕士研究生。主要从事地震动预测研究工作。E-mail：zbhnefu99@163.com

通讯作者:
王宏伟，男，生于1990年。博士，副研究员。主要从事地震动特征及模拟方面的研究。E-mail：whw1990413@163.com

12 https://www.usgs.gov/
23 https://tadas.afad.gov.tr/
计量
- 文章访问数: 14
- HTML全文浏览量: 6
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-21
- 录用日期: 2024-04-15
- 修回日期: 2024-03-06
- 网络出版日期: 2025-04-18
- 刊出日期: 2025-03-30

Evaluation of Ground Motion Model: A Case Study of the Turkey Earthquake on February 6,2023

Zhang Bohan^{1, 2
,},
Wang Hongwei^{1, 2
, ,},
Ren Yefei^{1, 2},
Wen Ruizhi^{1, 2}

1.
Key laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2.
Key laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

摘要

摘要: 地震动预测模型（GMM）是利用强震动记录拟合的具有一定物理意义的函数关系式，可表征强震动参数随震级、距离、场地等因素变化规律，是预估强震动参数的有效工具。以2023年2月6日土耳其连续发生的M_W7.8、M_W7.5地震为例，选用断层距200 km以内的自由场强震动记录，对比观测值与9个GMM预测值的差异，应用似然函数法和对数似然函数法对GMM进行选择、排序和加权，得到适用于该地区大震的加权预测模型，并对该预测模型的适用性进行验证。研究结果表明，适用于土耳其地区或者包括土耳其的更大区域地震的GMM预测效果较好，验证了GMM具有区域性差异；似然函数法和对数似然函数法对于GMM的选择与排序结果具有一致性；加权预测模型较好地预测了PGA随距离的衰减规律，并且对2次地震短周期反应谱的预测精确性更高；加权预测模型显著降低了2次地震事件间残差的离散性，预测结果更加稳定，说明加权预测模型提供了整体最优的预测结果，预测模型加权方案合理有效。
- 地震动预测模型 /
- 土耳其地震 /
- 似然函数法 /
- 对数似然函数法
Abstract: A ground motion model (GMM) is a physically meaningful function derived from strong-motion records to characterize intensity measures based on magnitude, distance, site conditions, and other factors. It serves as an effective tool for predicting seismic intensity measures. This study examines the M_W7.8 and M_W7.5 earthquakes of February 6, 2023, using strong-motion records with rupture and Joyner-Boore distances of less than 200km. The observed values are compared with nine GMMs, and the likelihood function and log-likelihood function methods are applied to select, rank, and weight the models to identify the most suitable GMM for the region and assess the applicability of predictive models. The results indicate that GMMs developed for Turkey or larger regions encompassing Turkey perform best, confirming the existence of regional variations in ground motion models. Both the likelihood function and log-likelihood function methods yield consistent rankings of GMMs. The weighted prediction model effectively captures the attenuation of peak ground acceleration with distance and demonstrates greater accuracy in predicting short-period pseudo-spectral acceleration. Additionally, it significantly reduces the dispersion of between-event residuals for both earthquakes, leading to more stable predictions. These findings confirm that the weighted prediction model provides optimal overall predictions and that the weighting scheme is both reasonable and effective.
- Ground motion model /
- Turkey earthquake /
- Likelihood function method /
- Log-likelihood function method
注释:

1) 12 https://www.usgs.gov/

2) 23 https://tadas.afad.gov.tr/

HTML全文

图 1 2次土耳其地震的震中位置、震源破裂面地表投影及可用记录的台站位置

Figure 1. Epicenter locations, surface projections of the source rupture model, and the strong-motion stations captured the usable recordings in two Turkey earthquakes

下载: 全尺寸图片幻灯片

图 2 2次地震中观测记录的PGA、PGV及部分周期的PSA

Figure 2. PGA, PGV, and PSA at two periods of recordings observed in two Turkey earthquakes

下载: 全尺寸图片幻灯片

图 3 基于H值的GMM等级评估结果

Figure 3. The ranking results of ground motion models based on H values

下载: 全尺寸图片幻灯片

图 4 GMM不同周期的L值

Figure 4. L values for different periods of ground motion models

下载: 全尺寸图片幻灯片

图 5 选用GMM与加权预测模型PGA随距离（R_JB）衰减及其预测中位值

Figure 5. The decay to PGA with distance and its predicted median values for selected ground motion models and the weighted prediction model

下载: 全尺寸图片幻灯片

图 6 观测记录与预测的加速度反应谱结果对比

Figure 6. Comparison of pseudo spectral accelerations（PSA）from simulation with those from recordings

下载: 全尺寸图片幻灯片

图 7 选用GMM与加权预测模型事件间残差分布

Figure 7. Between-events residuals between the selected ground motion models and weighted prediction models

下载: 全尺寸图片幻灯片

表 1 本文选用的GMM及参数特征描述

Table 1. Selected ground motion models and their features

预测模型	适用区域	震级M_W 范围	距离R_JB 范围/km	场地参数	其他参数变量	周期范围/s	强度指标类型	来源
BSSA14	全球	3.0～7.9	0～400	V_S30	沉积层厚度、破裂断层埋深、余震等	0.01～10	RotD50	Boore等（2014）
CY14	全球	3.5～8.5	0～400	V_S30	沉积层厚度、破裂断层埋深、倾角、上下盘效应、方向性效应等	0.01～10	RotD50	Chiou等（2014）
AC10	土耳其	5.0～7.6	0～200	V_S30	—	0.01～2	GM	Akkar等（2010）
Kale15	土耳其、伊朗	4.0～8.0	0～200	V_S30	—	0.01～4	GM	Kale等（2015）
GCY16	土耳其	3.0～7.6	0～200	V_S30	上盘效应、沉积层厚度等	0.01～10	RotD50	Gülerce等（2016）
BND14	欧洲、中东	4.0～7.6	0～300	V_S30或EC8 场地类别	—	0.02～3.0	GM	Bindi等（2014）
ASB14	欧洲、中东	4.0～7.6	0～200	V_S30	—	0.01～4	GM	Akkar等（2014）
IT18	意大利	4.0～8.0	0～200	V_S30	—	0.01～10	RotD50	Lanzano等（2019）
GR20	希腊	4.0～8.0	0～300	V_S30	—	0.01～10	RotD50	Boore等（2021）

下载: 导出CSV

表 2 基于H值的GMM最终等级

Table 2. Final ranking results of ground motion models based on H values

预测模型	等级	H中值	σ₁	z₀中值	σ₂	z₀均值	σ₃	z₀标准差	σ₄
BSSA14	C	0.441	0.009	0.510	0.021	0.500	0.017	1.038	0.014
CY14	B	0.405	0.009	0.378	0.020	0.362	0.017	1.182	0.014
AC10	B	0.601	0.006	−0.318	0.014	−0.351	0.012	0.785	0.013
Kale15	A	0.496	0.009	0.050	0.022	0.021	0.018	1.113	0.016
GCY16	B	0.410	0.010	0.203	0.023	0.203	0.018	1.240	0.017
BND14	A	0.526	0.008	−0.201	0.021	−0.213	0.014	0.921	0.009
ASB14	B	0.530	0.005	0.315	0.010	0.310	0.008	0.913	0.007
ITA18	B	0.563	0.004	0.258	0.010	0.236	0.010	0.829	0.010
GR20	C	0.481	0.009	−0.473	0.016	−0.594	0.016	1.145	0.016
注：σ₁、σ₂、σ₃、σ₄分别为H中值、z₀中值、z₀均值、z₀标准差的标准差。

下载: 导出CSV

表 3 基于L值的GMM最终排序

Table 3. Final ranking results of ground motion models based on L values

预测模型	L值	排序
BSSA14	1.707	7
CY14	1.725	8
AC10	1.663	5
Kale15	1.532	2
GCY16	1.695	6
BND14	1.632	3
ASB14	1.632	3
ITA18	1.473	1
GR20	2.053	9

下载: 导出CSV

表 4 选用GMM及相应权重

Table 4. Selected ground motion models and their weights

预测模型	权重
Kale15	0.336
BND14	0.314
ITA18	0.350

下载: 导出CSV

参考文献(24)

胡聿贤,张敏政,1984. 缺乏强震观测资料地区地震动参数的估算方法. 地震工程与工程振动,4(1):1−11.

Hu Y. X., Zhang M. Z., 1984. A method of predicting ground motion parameters for regions with poor ground motion data. Earthquake Engineering and Engineering Vibration, 4(1): 1−11. (in Chinese)

陶正如,陶夏新,2015. 美国2014地震区划中采用的地震动衰减关系. 世界地震工程,31(3):78−84.

Tao Z. R., Tao X. X., 2015. Ground motion attenuation relationships adopted in 2014 update of the US National Seismic Hazard Maps. World Earthquake Engineering, 31(3): 78−84. (in Chinese)

温瑞智,2016. 我国强地震动记录特征综述. 地震学报,38(4):550−563.

Wen R. Z., 2016. A review on the characteristics of Chinese strong ground motion recordings. Acta Seismologica Sinica, 38(4): 550−563. (in Chinese)

姚鑫鑫,任叶飞,岸田忠大等,2022. 强震动记录的数据处理流程:去噪滤波. 工程力学,39(S1):320−329.

Yao X. X., Ren Y. F., Kishida T. et al., 2022. The procedure of filtering the strong motion record: denoising and filtering. Engineering Mechanics, 39(S1): 320−329. (in Chinese)

Akkar S., Çağnan Z., 2010. A local ground-motion predictive model for turkey, and its comparison with other regional and global ground-motion models. Bulletin of the Seismological Society of America, 100(6): 2978−2995. doi: 10.1785/0120090367

Akkar S., Sandıkkaya M. A., Bommer J. J., 2014. Empirical ground-motion models for point- and extended-source crustal earthquake scenarios in Europe and the Middle East. Bulletin of Earthquake Engineering, 12(1): 359−387. doi: 10.1007/s10518-013-9461-4

Bindi D., Massa M., Luzi L., et al., 2014. Pan-European ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods up to 3.0 s using the RESORCE dataset. Bulletin of Earthquake Engineering, 12(1): 391−430. doi: 10.1007/s10518-013-9525-5

Boore D. M., 2010. Orientation-independent, nongeometric-mean measures of seismic intensity from two horizontal components of motion. Bulletin of the Seismological Society of America, 100(4): 1830−1835. doi: 10.1785/0120090400

Boore D. M., Stewart J. P., Seyhan E., et al., 2014. NGA-west2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. Earthquake Spectra, 30(3): 1057−1085. doi: 10.1193/070113EQS184M

Boore D. M., Stewart J. P., Skarlatoudis A. A., et al., 2021. A ground‐motion prediction model for shallow crustal earthquakes in Greece. Bulletin of the Seismological Society of America, 111(2): 857−874. doi: 10.1785/0120200270

Chiou B. S. J., Youngs R. R., 2014. Update of the chiou and youngs NGA model for the average horizontal component of peak ground motion and response spectra. Earthquake Spectra, 30(3): 1117−1153. doi: 10.1193/072813EQS219M

Delavaud E., Scherbaum F., Kuehn N., et al., 2012. Testing the global applicability of ground‐motion prediction equations for active shallow crustal regions. Bulletin of the Seismological Society of America, 102(2): 707−721. doi: 10.1785/0120110113

Efron B. , Tibshirani R. J. , 1993. An introduction to the bootstrap. New York: Chapman & Hall.

Gülerce Z., Kargoığlu B., Abrahamson N. A., 2016. Turkey-adjusted NGA-W1 horizontal ground motion prediction models. Earthquake Spectra, 32(1): 75−100. doi: 10.1193/022714EQS034M

Heath D. C., Wald D. J., Worden C. B., et al., 2020. A global hybrid V_S30 map with a topographic slope–based default and regional map insets. Earthquake Spectra, 36(3): 1570−1584. doi: 10.1177/8755293020911137

Kale Ö., Akkar S., 2013. A new procedure for selecting and ranking ground‐motion prediction equations (GMPEs): the Euclidean distance‐based ranking (EDR) method. Bulletin of the Seismological Society of America, 103(2A): 1069−1084. doi: 10.1785/0120120134

Kale Ö., Akkar S., Ansari A., et al., 2015. A ground‐motion predictive model for Iran and turkey for horizontal PGA, PGV, and 5% damped response spectrum: investigation of possible regional effects. Bulletin of the Seismological Society of America, 105(2A): 963−980. doi: 10.1785/0120140134

Kowsari M., Halldorsson B., Hrafnkelsson B., et al., 2019. Calibration of ground motion models to Icelandic peak ground acceleration data using Bayesian Markov Chain Monte Carlo simulation. Bulletin of Earthquake Engineering, 17(6): 2841−2870. doi: 10.1007/s10518-019-00569-5

Lanzano G., Luzi L., Pacor F., et al., 2019. A revised ground‐motion prediction model for shallow crustal earthquakes in Italy. Bulletin of the Seismological Society of America, 109(2): 525−540. doi: 10.1785/0120180210

McNamara D. E. , Wolin E. , Powers P. M. , et al. , 2020. Evaluation of ground‐motion models for U. S. geological survey seismic hazard models: 2018 anchorage, Alaska, M_w 7.1 subduction zone earthquake sequence. Seismological Research Letters, 91 (1): 183−194.

Mousavi M., Ansari A., Zafarani H., et al., 2012. Selection of ground motion prediction models for seismic hazard analysis in the Zagros region, Iran. Journal of Earthquake Engineering, 16(8): 1184−1207. doi: 10.1080/13632469.2012.685568

Scherbaum F., Cotton F., Smit P., 2004. On the use of response spectral-reference data for the selection and ranking of ground-motion models for seismic-hazard analysis in regions of moderate seismicity: the case of rock motion. Bulletin of the Seismological Society of America, 94(6): 2164−2185. doi: 10.1785/0120030147

Scherbaum F., Delavaud E., Riggelsen C., 2009. Model selection in seismic hazard analysis: an information-theoretic perspective. Bulletin of the Seismological Society of America, 99(6): 3234−3247. doi: 10.1785/0120080347

Wang D., Xie L. L., Abrahamson N. A., et al., 2010. Comparison of strong ground motion from the Wenchuan, China, earthquake of 12 May 2008 with the next generation attenuation (NGA) ground-motion models. Bulletin of the Seismological Society of America, 100(5B): 2381−2395. doi: 10.1785/0120090009

施引文献

资源附件(0)

访问统计