利用HHT-EEMD方法分析云南区域GNSS应变时序孕震信息

Abstract

地震的孕育和发生本质上都是地壳内部应力、应变能逐渐积累并突然或缓慢释放的结果, 研究应变的变化过程对于地震危险性的判定具有重要意义。本文基于云南区域2013—2019年GNSS格网应变时间序列, 利用专门适用于非线性非平稳信号处理的热门时频分析方法—整体经验模态分解(ensemble empirical mode decomposition, EEMD)的希尔伯特-黄变换(Hilbert-Huang transform, HHT)分析方法, 探索云南区域中强地震前GNSS应变时序的时-频-能量分布特征, 尝试挖掘应变时频信号中所携带的孕震信息。利用23号、42号格网对应的地震进行震例分析, 结果显示: EEMD具有分频剖面的类似特征, 它能够依据数据的时间特征尺度进行信号分解, 较好地剖析信号在不同频率尺度上的变化特征; Hilbert变换能够通过瞬时频率、瞬时振幅等方式突出信号的局部瞬时特性, 在固有模态分量(intrinsic mode functions, IMF)异常曲线识别无效的情况下仍能凸显异常; 通过EEMD、残差趋势项分析、IMF异常识别和Hilbert变换综合动态分析应变时间序列的分析方法, 能够在部分地震前夕发现一些潜在异常信息, 为未来云南区域强震危险地点的判定提供一定的参考。

Alternate 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.