基于相对强度算法和参数遍历试验的地震预测回溯性检验——以中国川滇地区为例

doi:10.3969/j.issn.0253-4967.2024.03.010

地震地质 ›› 2024, Vol. 46 ›› Issue (3): 686-698.DOI: 10.3969/j.issn.0253-4967.2024.03.010

基于相对强度算法和参数遍历试验的地震预测回溯性检验——以中国川滇地区为例

范晓易¹⁾(), 曲均浩²⁾^,^*(), 顾勤平¹⁾, 陈飞¹⁾, 王夫运³⁾

¹⁾ 江苏省地震局, 南京 210000
²⁾ 山东省地震局, 济南 250000
³⁾ 中国地震局地球物理勘探中心, 郑州 450000

收稿日期:2023-04-21 修回日期:2023-10-09 出版日期:2024-06-20 发布日期:2024-07-19
通讯作者: *曲均浩, 男, 1981年生, 研究员, 主要从事余震机理、活动构造和非天然地震识别研究, E-mail: gisqjh@126.com。
作者简介:
范晓易, 女, 1989年生, 工程师, 主要从事地震学、固体地球物理学等研究, E-mail: fanxiaoyi1007@163.com。
基金资助:
山东省自然科学基金(ZR2020KF003); 国家重点研发计划项目(2018YFE0109700); 中国地震局三结合项目(3JH-202401011)

RETROSPECTIVE TEST OF EARTHQUAKE PREDICTION BASED ON RELATIVE INTENSITY ALGORITHM AND PARAMETER TRAVERSAL TEST——AN EXAMPLE OF SICHUAN-YUNNAN REGION

FAN Xiao-yi¹⁾(), QU Jun-hao²⁾^,^*(), GU Qin-ping¹⁾, CHEN Fei¹⁾, WANG Fu-yun³⁾

¹⁾ Jiangsu Earthquake Agency, Nanjing 210000, China
²⁾ Shandong Earthquake Agency, Jinan 250000, China
³⁾ Geophysical Exploration Center of China Earthquake Administration, Zhengzhou 450000, China

Received:2023-04-21 Revised:2023-10-09 Online:2024-06-20 Published:2024-07-19

摘要/Abstract

摘要：

探索地震活动的时空分布对于地震风险评估, 尤其是对于中国川滇地区这样的地震频发区和强震危险区而言具有重要意义。相对强度算法(RI)基于统计学理论, 使用过去的地震强度评估预测同一地点的未来地震强度。其原理简单, 已多次在国内外强震预测的实践应用中取得了良好的效果。目前, 经过多年的发展完善, 该方法的预测性能愈加突出。为了辅助川滇地区的地震活动性预测工作, 文中使用相对强度算法(RI)和参数遍历试验(PTT)进行了全面的参数分析, 深入研究了RI算法在中国川滇地区的适用性, 结果表明: 由于参数选择合理(包括网格大小、异常学习时间窗长度、预测时间窗起始时间和预测时间窗长度), RI和PTT的组合研究表现出了明显优于随机猜测的预测效果, 揭示了川滇地震危险区地震预测的有效参数区间。相对强度算法能够对川滇地区的地震活动进行预测, 文中成果丰富了地震危险区地震趋势预测的参考依据。

关键词: 统计学, 相对强度方法, 参数遍历试验, 回溯性检验

Abstract:

Examining the spatial and temporal distribution of seismic activity holds significant importance for seismic risk assessment, particularly in regions prone to frequent and intense earthquakes such as the Sichuan-Yunnan region in China. It is widely recognized that earthquakes exhibit non-random patterns in both spatial and temporal dimensions.

Early scientists endeavored to predict earthquakes using statistical principles, leading to the development of various forecasting methods. Among these, the Relative Intensity(RI)and Pattern Informatics(PI)methods emerged as statistical approaches to earthquake prediction modeling. Essentially, both methods fall under the category of smoothing seismic activity models. They employ techniques to quantify temporal changes in seismic activity graphs, generating maps that highlight areas(hot spots)where earthquakes may occur during specific future periods. While the RI algorithm’s theory is straightforward, its forecasting efficacy is robust, particularly notable in predicting major earthquakes, demonstrating similar advantages to the PI algorithm. Widely adopted globally for proactive predictions across diverse tectonic systems, it has shown commendable results in seismic forecasting practices both domestically and internationally. Over years of development, its predictive performance has gained prominence. However, further research is needed to assess its suitability for predicting minor seismic events in low-seismicity zones. Additionally, its successful application hinges on background seismic activity and the selection of target magnitudes.

To aid seismic activity prediction in the Sichuan-Yunnan region and identify potential future seismic source areas, a comprehensive parameter analysis was conducted using the Relative Intensity(RI)algorithm with the parameter traversal test(PTT). The RI algorithm operates on the premise that the predicted intensity of future earthquakes in a given region closely mirrors the intensity of past earthquakes. While it may not explicitly consider the “active” and “quiet” characteristics of seismic activity, as a fundamental prediction algorithm, it often yields improved prediction outcomes when applied to assess seismic probability in regions with high seismic activity, such as the Sichuan-Yunnan region.

In this study, the statistical-based Relative Intensity(RI)algorithm is employed to calculate the relative intensity of earthquakes based on quantitative earthquake characteristics. The study involves gridding the investigated area and statistically analyzing historical earthquake occurrences within each grid unit under specific magnitude conditions to inform predictions of future earthquake frequencies. The research focuses on evaluating the influence of four key model parameters: grid size, length of the anomalous learning window, starting point of the prediction window, and length of the prediction window, on the algorithm’s prediction efficiency. Furthermore, the study investigates the applicability of the RI algorithm to the Sichuan-Yunnan regions in China. The results yield two significant findings:

(1)The integration of the Relative Intensity(RI)algorithm with the Parameter Traversal Test(PTT)yielded significantly improved results compared to random guessing, primarily due to its optimized parameter selections. These parameters include the grid size, length of the anomalous learning time window, starting time of the prediction time window, and length of the prediction time window.

(2)The parameters of the prediction model exhibit a degree of stability and demonstrate predictive capability for seismic activity in the Sichuan-Yunnan region over the next 1-5 years. The study revealed specific rules and effective parameter intervals applicable to earthquake-prone areas in Sichuan-Yunnan.

The findings suggest that the integration of the Relative Intensity(RI)algorithm with the Parameter Traversal Test(PTT)holds promise for predicting seismic activities in the Sichuan-Yunnan region. This approach enhances the pool of references available for predicting earthquake trends in regions prone to frequent and intense earthquakes. Further research on the RI algorithm is anticipated to yield a more refined numerical model for earthquake trend prediction, contributing to enhanced forecasting accuracy and preparedness in earthquake-prone areas.

Key words: statistical method, relative intensity algorithm, parameter traversal test, retrospective statistical test

范晓易, 曲均浩, 顾勤平, 陈飞, 王夫运. 基于相对强度算法和参数遍历试验的地震预测回溯性检验——以中国川滇地区为例[J]. 地震地质, 2024, 46(3): 686-698.

FAN Xiao-yi, QU Jun-hao, GU Qin-ping, CHEN Fei, WANG Fu-yun. RETROSPECTIVE TEST OF EARTHQUAKE PREDICTION BASED ON RELATIVE INTENSITY ALGORITHM AND PARAMETER TRAVERSAL TEST——AN EXAMPLE OF SICHUAN-YUNNAN REGION[J]. SEISMOLOGY AND GEOLOGY, 2024, 46(3): 686-698.

图/表 8

图1 RI算法的时间尺度划分

Fig. 1 Time scale partition of RI algorithms.

图2 “ROC值”表示方法示意图黑色实线表示评估RI算法预测效能的ROC曲线, 虚线对角线表示随机预测, “ROC值”即2条线所围面积S1-S2

Fig. 2 The schematic diagram of “ROC value” method.

图3 川滇地区2000—2021年地震的震中分布

Fig. 3 Distribution of earthquakes in Sichuan-Yunnan region from 2000 to 2021.

图4 川滇地区2000—2021年 MS5.0 以上地震的M-T图

Fig. 4 The M-T plot of earthquakes with magnitude MS≥5.0 in Sichuan-Yunnan region from 2000 to 2021.

表1 不同异常学习时间窗长的ROC值对比分析

Table1 Comparative analysis of ROC values with different anomalous learning window parameters

异常学习时间窗		预测时间窗起点(t₁)	预测时间窗长度L
类型	窗长度S		1a	2a	3a	4a	5a
增长型预测时间窗	10a	2010	-0.5	0.4533	0.205	0.1512	0.2295
	11a	2011	0.4617	0.298	0.1897	0.2493	0.2282
	12a	2012	0.2311	0.2975	0.2222	0.2058	0.2017
	13a	2013	0.0751	0.2115	0.192	0.1916	0.1849
	14a	2014	0.3112	0.2502	0.3529	0.2716	0.2922
	15a	2015	-0.013	-0.013	0.1714	0.2977	0.3573
	16a	2016	-0.5	0.3593	0.4007	0.4339	0.3706
	17a	2017	0.3516	0.3947	0.431	0.3647	0.3374
	18a	2018	0.4216	0.4537	0.3977	0.2945
	19a	2019	0.487	0.3769	0.2683
	20a	2020	0.141	0.295
	21a	2021	0.2
固定型预测时间窗	5a	2010	-0.5	0.4679	0.1529	0.1379	0.2035
		2011	0.47	0.3035	0.1712	0.1822	0.1415
		2012	0.2628	0.0722	0.2275	0.1131	0.1054
		2013	-0.147	0.0807	0.0352	0.0369	0.105
		2014	0.2551	0.1065	0.1065	0.1409	0.1946
		2015	-0.059	-0.059	0.1802	0.2967	0.3579
		2016	-0.5	0.4334	0.4285	0.4524	0.3911
		2017	0.4337	0.4209	0.4479	0.4146	0.3829
		2018	0.412	0.4558	0.4393	0.3219
		2019	0.4943	0.4559	0.2747
		2020	0.3770	0.2582
		2021	0.2308

表2 参数遍历试验的参数

Table2 Parameters used in the parameter traversal test(PTT)

类型	名称	起点	终点	步长
地震目录参数	截止震级M_C	M_L3.0
	目标震级M_T	M_S5.5
	深度h	0km	70km
计算参数	网格尺度GS	0.05°	0.4°	0.05°
	预测时间窗起点t₁	2001	2021	1a
	预测时间窗长度L	1a	5a	1a

图5 不同参数设置及计算阈值下的ROC值分布 a—d分别表示截止震级ML3.0、目标震级 MS5.5 条件下阈值为0.1、 0.2、 0.3、 0.4的结果。图d中的红圈为ROC值高值分布区域

Fig. 5 The distribution of ROC values with different parameter settings and threshold values.

图 6 优势参数下的RI“热点”分布及ROC检验结果实例图a中彩色圆圈和灰色倒三角分别代表预测时间窗和异常学习时间窗内目标震级以上的地震

Fig. 6 An example of the distribution of “hot spots” predicted by RI algorithm and the corresponding ROC test results by using the optimized parameter settings.

参考文献 26

[1]	蒋长胜, 吴忠良. 2008. 对地震预测的一个统计物理算法在川滇地区的回溯性预测检验[J]. 中国科学(D辑), 38(7): 852—861.
	JIANG Chang-sheng, WU Zhong-liang. 2008. Retrospective prediction test of a statistical physical algorithm for earthquake prediction in Sichuan-Yunnan region[J]. Science in China(Ser D), 38(7): 852—861 (in Chinese).
[2]	蒋长胜, 吴忠良, 韩立波, 等. 2013. 地震序列早期参数估计和余震概率预测中截止震级M_C的影响: 以2013年甘肃岷县漳县6.6级地震为例[J]. 地球物理学报, 56(12): 4048—4057.
	JIANG Chang-sheng, WU Zhong-liang, HAN Li-bo, et al. 2013. Effect of cut off magnitude M_C of earthquake catalogue on the early estimation of earthquake sequence parameters with implication for the probabilistic forecast of aftershocks: the 2013 Minxian-Zhangxian, Gansu, M_S6.6 earthquake sequence[J]. Chinese Journal of Geophysics, 56(12): 4048—4057 (in Chinese).
[3]	蒋卉. 2014. 利用图像信息学(PI)算法对川滇地区中长期地震危险性的研究[D]. 北京: 中国地震局地球物理研究所.
	JIANG Hui. 2014. Pattern Informatics(PI)algorithm applied to the predictive analysis of intermediate-term seismic hazard in the Yunnan-Sichuan region[D]. Institute of Geophysics, China Earthquake Administration, Beijing (in Chinese).
[4]	李莹甄, 殷娜, 李小晗. 2014. 不同震级标度转换关系研究概述[J]. 地震工程学报, 36(1): 80—87.
	LI Ying-zhen, YIN Na, LI Xiao-han. 2014. Review of the conversional relationship for different magnitude scales[J]. China Earthquake Engineering Journal, 36(1): 80—87 (in Chinese).
[5]	刘洋, 张银龙, 王金宁. 2014. 地震危险性分析中地震时空统计分布研究[J]. 科技经济市场, (4): 132.
	LIU Yang, ZHANG Yin-long, WANG Jin-ning. 2014. Study on spatial-temporal statistical distribution of earthquake in seismic hazard analysis[J]. Science & Technology Economy Market, (4): 132 (in Chinese).
[6]	苏有锦, 李永莉, 李忠华, 等. 2003. 川滇地区区域地震目录完整性最小震级分析[J]. 地震研究, 26(S1): 10—16.
	SU You-jin, LI Yong-li, LI Zhong-hua, et al. 2003. Analysis of minimum complete magnitude of earthquake catalog in Sichuan-Yunnan region[J]. Journal of Seismological Research, 26(S1): 10—16 (in Chinese).
[7]	阎志德, 郭履灿, 张相民. 1985. 论中国地震震级M_L与M_S 的关系[J]. 地震地磁观测与研究, 5(6): 11—16.
	YAN Zhi-de, GUO Lü-can, ZHANG Xiang-min. 1985. Discussion on the relationship between earthquake magnitude M_L and M_S in China[J]. Seismological and Geomagnetic Observation and Research, 5(6): 11—16 (in Chinese).
[8]	张盛峰, 郑建常, 蒋长胜, 等. 2017. 图像信息学(PI)算法计算参数优化分析: 以山东及相邻地区为例[J]. 地球物理学报, 60(12): 4633—4643.
	ZHANG Sheng-feng, ZHENG Jian-chang, JIANG Chang-sheng, et al. 2017. Optimal analysis of parameter settings in pattern informatics(PI)algorithm: An example of Shandong and adjacent areas[J]. Chinese Journal of Geophysics, 60(12): 4633—4643 (in Chinese).
[9]	Chen C C, Rundle J B, Holliday J R, et al. 2005. The 1999 Chi-Chi, Taiwan, earthquake as a typical example of seismic activation and quiescence[J]. Geophysical Research Letters, 322(22): 117—137.
[10]	Holliday J R, Nanjo K Z, Tiampo K F, et al. 2005. Earthquake forecasting and its verification[J]. Nonlinear Processes Geophysics, 12(6): 965—977.
[11]	Holliday J R, Rundle J B, Tiampo K F, et al. 2006a. Using earthquake intensities to forecast earthquake occurrence times[J]. Nonlinear Processes in Geophysics, 13(5): 585—593.
[12]	Holliday J R, Rundle J B, Tiampo K F, et al. 2006b. Modification of the pattern informatics method for forecasting large earthquake events using complex eigenfactors[J]. Tectonophysics, 413(1): 87—91.
[13]	Jia K, Zhou S Y, Zhuang J C, et al. 2014. Possibility of the independence between the 2013 Lushan earthquake and the 2008 Wenchuan earthquake on Longmen Shan fault, Sichuan, China[J]. Seismological Research Letters, 85(1): 60—67.
[14]	Jiang C S, Wu Z L. 2010. PI forecast for the Sichuan-Yunnan region: Retrospective test after the May 12, 2008, Wenchuan earthquake[J]. Pure and Applied Geophysics, 167(6-7): 751—761.
[15]	Jiang C S, Wu Z L. 2011. PI forecast with or without de-clustering: an experiment for the Sichuan-Yunnan region[J]. Natural Hazards and Earth System Sciences, 11(3): 697—706.
[16]	Keilis-Borok V I, Rotwain I M. 1990. Diagnosis of time of increased probability of strong earthquakes in different regions of the world: algorithm CN[J]. Physics of Earth and Planetary Interiors, 61(1-2): 57—72.
[17]	Nanjo K Z. 2010. Earthquake forecast models for Italy based on the RI algorithm[J]. Annals of Geophysics, 53(3): 117—127.
[18]	Nanjo K Z, Holliday J R, Chen C C, et al. 2006a. Pattern informatics and its application for optimal forecasting of large earthquakes in Japan[J]. Pure and Applied Geophysics, 163(11-12): 2417—2432.
[19]	Nanjo K Z, Holliday J R, Chen C C, et al. 2006b. Application of a modified pattern informatics method to forecasting the locations of future large earthquakes in the central Japan[J]. Tectonophysics, 424: 351—366.
[20]	Nanjo K Z, Holliday J R, Chen C C, et al. 2006c. Forecasting locations of future large earthquakes by a pattern informatics method: A review[J]. Proceedings of the Institute of Statistical Mathematics, 54(2): 281—297.
[21]	Rhoades D A, Evison E F. 2004. Long-range earthquake forecasting with every earthquake a precursor according to scale[J]. Pure and Applied Geophysics, 161(1): 47—72.
[22]	Rundle J B, Tiampo K F, Klein W, et al. 2002. Self-organization in leaky threshold systems: The influence of near-mean field dynamics and its implications for earthquakes, neurobiology and forecasting[J]. Proceedings of the National Academy of Sciences, 99(S1): 2514—2521.
[23]	Rundle J B, Turcotte D L, Shcherbakov R, et al. 2003. Statistical physics approach to understanding the multi-scale dynamics of earthquake fault systems[J]. Reviews of Geophysics, 41(4): 1019.
[24]	Tiampo K F, Rundle J B, Mcginnis S, et al. 2002. Mean field threshold systems and phase dynamics: An application to earthquake fault systems[J]. Europhysics Letters, 60(3): 481.
[25]	Wu Y H, Chen C C, Li H C. 2016. Conditional probabilities for large events estimated by small earthquake rate[J]. Pure and Applied Geophysics, 173(1): 183—96.
[26]	Wu Y H, Chen C C, Rundle J B. 2008. Precursory seismic activation of the Pingtung(Taiwan)offshore doublet earthquakes on 26 December 2006: A pattern informatics analysis[J]. Terrestrial Atmospheric and Oceanic Sciences, 19(6): 743—749.

基于相对强度算法和参数遍历试验的地震预测回溯性检验——以中国川滇地区为例

RETROSPECTIVE TEST OF EARTHQUAKE PREDICTION BASED ON RELATIVE INTENSITY ALGORITHM AND PARAMETER TRAVERSAL TEST——AN EXAMPLE OF SICHUAN-YUNNAN REGION

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 26

相关文章 0

编辑推荐

Metrics

本文评价