Analysis of seismogenic information of GNSS strain time series based on HHT-EEMD method in Yunnan region

doi:10.11947/j.AGCS.2022.20210120

Abstract

Abstract: The gestation and occurrence of earthquakes are essentially the inevitable result of the gradual accumulation of stress and strain energy in the crust and their sudden or slow release. Studying the changing process of strain is of great significance to the determination of earthquake risk. Based on the strain time series of GNSS grids in Yunnan region from 2013 to 2019, the Hilbert-Huang transform (HHT) and ensemble empirical mode decomposition (EEMD) analysis method was used to explore the time-frequency characteristics of GNSS strain time series before earthquakes in Yunnan region which was a different method from traditional time-frequency analysis, it was specially suitable for nonlinear and non-stationary signals, and try to mine the seismogenic information carried in the strain time-frequency signal. The paper summarizes the earthquakes corresponding to grids No.23 and No.42 as earthquake examples. The results show that EEMD can decompose signal according to the time characteristic of the data, and can fully retain the characteristics of the data itself in the decomposition process. Its decomposition is objective and adaptive, and can better analyze the change characteristics of seismic signals at different scales. In addition, the Hilbert transform can describe the subtle changes of the signal over time, reflect the instantaneous characteristics of the signal, and has applicability for the abnormal identification. Through the method of EEMD, IMF component anomaly recognition, Hilbert transform method and comprehensive dynamic analysis of strain time series, it can find some potential information on the eve of earthquakes, and provide reference for the determination of dangerous locations of strong earthquakes in Yunnan area in the future.

Key words: GNSS strain time-series, HHT, EEMD, anomaly recognition

CLC Number:

P228

GAO Han, YUAN Xiping, GAN Shu, ZHANG Ming. Analysis of seismogenic information of GNSS strain time series based on HHT-EEMD method in Yunnan region[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9): 1899-1910.

References

[1] 洪敏, 邵德盛, 王伶俐, 等. 基于GNSS观测资料研究云南地壳形变与地震的关系[J].地震研究, 2013, 36(3):292-298. HONG Min, SHAO Desheng, WANG Lingli, et al. Research on relationship between crustal deformation and earthquake based on GNSS[J]. Journal of Seismological Research, 2013, 36(3):292-298.
[2] 徐克科, 伍吉仓. GNSS多尺度应变场估计及区域形变检测[J]. 测绘学报, 2016, 45(8):992-1000. XU Keke, WU Jicang.Estimation of GNSS multiscale strain field and detection of regional crustal deformation[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8):992-1000.
[3] 许才军, 刘洋, 温扬茂. 利用GPS资料反演汶川Mw 7.9级地震滑动分布[J]. 测绘学报, 2009, 38(3):195-201, 215. XU Caijun, LIU Yang, WEN Yangmao. Mw7.9 Wenchuan earthquake slip distribution inversion from GPS measurements[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(3):195-201,215.
[4] 姜卫平,周晓慧,刘经南, 等.青藏高原地壳运动与应变的GPS监测研究[J]. 测绘学报, 2008, 37(3):285-292. JIANG Weiping, ZHOU Xiaohui, LIU Jingnan, et al. Present-day crustal movement and strain rate in Qinghai-Tibetan Plateau from GPS data[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(3):285-292.
[5] 江在森, 马宗晋, 张希, 等. GPS初步结果揭示的中国大陆水平应变场与构造变形[J].地球物理学报, 2003, 46(3):352-358. JIANG Zaisen, MA Zongjin, ZHANG Xi, et al. Horizontal strain field and tectonic deformation of China Mainland revealed by preliminary GPS result[J]. China Chinese Journal of Geophysics, 2003, 46(3):352-358.
[6] 熊熊, 滕吉文, 郑勇. 中国大陆地壳运动的GPS观测及相关动力学研究[J]. 地球物理学进展, 2004, 19(1):16-25. XIONG Xiong, TENG Jiwen, ZHENG Yong. GPS observations for crustal movements in China continent and related geodynamic studies[J]. Progress in Geophysics, 2004, 19(1):16-25.
[7] 江在森, 杨国华, 王敏, 等. 中国大陆地壳运动与强震关系研究[J]. 大地测量与地球动力学, 2006, 26(3):1-9. JIANG Zaisen, YANG Guohua, WANG Min, et al. On crustal movement in China continent and its relationship with strong earthquakes[J]. Journal of Geodesy and Geodynamics, 2006, 26(3):1-9.
[8] 许才军, 董立祥, 施闯, 等. 华北地区GPS地壳应变能密度变化率场及其构造运动分析[J]. 地球物理学报, 2002, 45(4):497-506. XU Caijun, DONG Lixiang, SHI Chuang, et al. A study on annual accumulation of strain energy density significance by using GPS measurements in North[J]. China Chinese Journal of Geophysics, 2002, 45(4):497-506.
[9] 魏文薪, 江在森, 武艳强, 等.利用GPS数据研究川滇块体东边界主要断裂带运动特性[J]. 武汉大学学报(信息科学版), 2012, 37(9):1041-1044. WEI Wenxin, JIANG Zaisen, WU Yanqiang, et al. Motion characteristics of major faults in east boundary of Sichuan-Yunnan block obtained with GPS data[J]. Geomatics and Information Science of Wuhan University, 2012, 37(9):1041-1044.
[10] HAN Bing, ZHAO Guoze, WANG Lifeng, et al. Earthquake electromagnetic precursor anomalies detected by a new ground-based observation network[J].Journal of Geodesy and Geoinformation Science,2021,4(1):116-123.
[11] BI Y X, CHRISTODOULOU V, WILKIE G, et al. Automatic anomaly detection for swarm observations[J].Journal of Geodesy and Geoinformation Science,2021,4(1):94-108.
[12] 苏利娜, 张勇. GPS时间序列中同震和震后形变的自动识别和估计[J]. 武汉大学学报(信息科学版), 2018, 43(10):1504-1510. SU Lina, ZHANG Yong. Automatic detection and estimation of coseismic and postseismic deformation in GPS time series[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10):1504-1510.
[13] ANSARI K. Crustal deformation and strain analysis in Nepal from GPS time-series measurement and modeling by ARMA method[J]. International Journal of Earth Sciences, 2018, 107(8):2895-2905.
[14] CROWELL B W, YEHUDA B, LIU Z. Single-station automated detection of transient deformation in GPS time series with the relative strength index:a case study of cascadian slow slip[J]. Journal of Geophysical Research:Solid Earth, 2016, 121(12):9077-9094.
[15] 高涵, 洪敏, 张明, 等. 基于GNSS应变时序的云南地区构造运动与地震事件孕震模式分析[J]. 大地测量与地球动力学, 2020, 40(3):252-257. GAO Han, HONG Min, ZHANG Ming, et al. Analysis of seismogenic modes of tectonic movement and earthquake events in Yunnan:based on GNSS time series of strain anomalies[J]. Journal of Geodesy and Geodynamics, 2020, 40(3):252-257.
[16] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995.
[17] HUANG N E. Hilbert-Huang transform and its applications[M]. Singapore:World Scientific Publishing Company, 2014.
[18] HUANG N E, WU Z H, LONG S R, et al. On instantaneous frequency[J]. Advances in Adaptive Data Analysis, 2011, 1(2):177-229.
[19] LEI Yaguo, HE Zhengjia, ZI Yangyang. Application of the EEMD method to rotor fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2009, 23(4):1327-1338.
[20] WANG Tong, ZHANG Mingcai, YU Qibao, et al. Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal[J]. Journal of Applied Geophysics, 2012, 83:29-34.
[21] 张培震, 邓起东, 张国民, 等. 中国大陆的强震活动与活动地块[J]. 中国科学(D辑:地球科学), 2003(S1):12-20. ZHANG Peizhen, DENG Qidong, ZHANG Guomin, et al. Strong earthquakes and active plots in Mainland China[J]. Science in China Series D-Earth Sciences, 2003(S1):12-20.
[22] HONG Min, SHAO Desheng, WU Tengfei, et al. Short-impending earthquake anomaly index extraction of GNSS continuous observation data in Yunnan, Southwestern China[J]. Journal of Earth Science, 2018, 29(1):230-236.
[23] 邵德盛, 洪敏, 张勇, 等. 云南地区形变观测资料短临异常指标提取[J]. 武汉大学学报(信息科学版), 2017, 42(9):1223-1228. SHAO Desheng, HONG Min, ZHANG Yong, et al. Extract short impending abnormal indicator from deformation data in Yunnan Province[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9):1223-1228.
[24] ZHAO Huawu, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences, 2004, 460(2046):1597-1611.
[25] 洪敏, 邵德盛, 王伶俐, 等. 利用GNSS连续站时间序列空间相关性提取区域形变异常[J]. 大地测量与地球动力学, 2014, 34(2):175-178,182. HONG Min, SHAO Desheng, WANG Lingli, et al. Using spatial correlation between continuous GNSS time series to achieve regional deformation anomalies[J]. Journal of Geodesy and Geodynamics, 2014, 34(2):175-178,182.