在地震观测中,地震数据中普遍包含有噪声信号.由于噪声信号的干扰,地震分析的效率会受到不同程度的影响.传统的去噪方法通常需要噪声的先验知识,并且滤波时会造成部分有效信号丢失.针对这一问题,本文提出一种将自适应噪声完全集合经验模态分解(CEEMDAN)算法与Hurst指数相结合的地震数据去噪方法.首先通过CEEMDAN方法将信号分解为一系列本征模函数(IMF),然后利用Hurst指数对滤波后的IMF分量进行识别,最后对地震数据IMF分量进行重构,从而实现数据去噪.与传统方法的去噪效果对比表明,本文方法可将低信噪比波形的去噪效果提高32％,将高信噪比波形的去噪效果提高6倍.同时对地磁数据的去噪结果表明,本文方法能够较完整地将地铁噪声从地磁信号波形中滤除.

2010年6月28日至8月初,广西凌云与凤山交界地区出现一次罕见的震群活动.利用震区附近8个近距离台站所记录的数字地震波形,按照高信噪比和计算要求,挑选出此次震群中74次ML＞1.5地震可用于计算地震尾波的共179条三分向波形记录,采用Sato单次散射模型经滤波以及消除环境噪声后,计算了地震尾波传播过程途经区的品质因子 Q(f)值;拟合Q(f)值与频率f之间的关系,得到研究区Q(f)=(44.32±23.56)f0.88±017;并对震中区及其邻区地震尾波衰减的时间与空间特征进行了探讨.通过分析震区及其附近尾波衰减参数与尾波采样体之间的关系,认为该区域可能存在浅部高衰减层.此次震群是在低应力水平条件下发生的,震中区Q.值较其他台站高,其地震尾波衰减较慢,介质非均匀性水平相对较低,震中区为低衰减区域,介质较为稳定.

利用天然地震震源和人工爆破震源之间信号能量分布的差异,结合RBF神经网络技术,对2类事件进行分类,具体步骤如下:使用8个带通滤波器对事件波形进行滤波,并划分为4个波形段:P波、P波尾波、S波和S波尾波,分别计算每个滤波器信道和波形段的能量特征值,以所得32个特征参数作为输入向量,利用RBF神经网络,对地震和爆破事件进行分类识别.结果表明,基于RBF神经网络的地震事件识别方法,识别率为88.1％,具有较高的准确性,可作为地震与爆破事件识别的一个重要依据.

The focal mechanism and propagation path of natural earthquakes and karst collapse are different,so the frequency characteristics of their waveforms are different,too.The wavelet packet fractal method can effectively extract the natural earthquake and karst collapse waveform characteristics,and the radial basis function (RBF for short) neural network can well identify two kinds of events,therefore by using RBF neural network based on wavelet packet this paper takes the natural earthquake and karst collapse recorded by Guangxi Earthquake Networks Center in recent years as an example to try to identify two kinds of event waveforms.The results show that the recognition rate of natural earthquake and karst collapse event is up 89.5％,suggesting it is an effective method to identify natural earthquakes and karst collapse.

通过对2011～2013年的34个影区地震震例进行分析,总结了桂林台影区地震各个震相特征,并运用震相特征对影区地震进行分析定位,有效提高了桂林台对影区地震进行定位分析的精度和效率,对日后进行大震速报和日常分析具有借鉴意义.

On the basis of the digital records of Guilin Seismic Station from 2006 to 2011, this paper will recalculate the magnitude by using EDAS software and explore the reasons of magnitude deviation of earthquake quick report by taking the magnitude given by the " China earthquake networks observation report" as standard magnitude to give statistical analysis. Based on the comprehensive analysis, we find that: the deviation of Guilin station is correlated with the epicenter direction, epicenter distance, and magnitude size; the correction value of magnitude deviation of Guilin station needs to consider these factors by taking the average value of the correction value of epicenter direction deviation, the correction value of epicenter distance deviation, the correction value of magnitude deviation.