利用小波变换提取面波群速度

发布时间：2018-05-06 06:03

本文选题：面波 + 群速度　；参考：《地震学报》2017年03期

【摘要】：选取了几种常见的小波母函数,分别提取了同一理论下的面波数据的群速度,并与理论群速度进行对比,结果表明Morlet小波提取面波群速度的效果最好.此外,将Morlet小波与常用的多重滤波提取群速度的结果进行了比较,结果表明:(1)多重滤波法非常依赖高斯滤波系数α的取值,α的取值应随面波周期的增大而减小;(2)在α取值得当的前提下,在20—35s周期范围内多重滤波法提取面波群速度的相对误差比Morlet小波小,在周期大于35s时,两者相对误差相近;(3)合适的α值的选取需在不同周期段耗费大量时间进行大量试验,这说明多重滤波法不具备自适应性;而采用小波变换分析短周期信号时,时间窗变窄,频率窗变长,当分析长周期信号时,时间窗变长,频率窗变窄,具有对信号的自适应性,这是小波变换相比多重滤波法的最大优点.
[Abstract]:Several common wavelet generating functions are selected and the group velocities of surface wave data under the same theory are extracted respectively and compared with the theoretical group velocities. The results show that Morlet wavelet is the best in extracting surface wave group velocity. In addition, the Morlet wavelet is compared with the commonly used multi-filter to extract group velocity. The results show that the multiplex filtering method relies heavily on the value of Gao Si's filtering coefficient 伪, and the value of 伪 should be reduced with the increase of surface wave period. In the range of 20-35s period, the relative error of extracting surface wave group velocity by multiple filtering method is smaller than that by Morlet wavelet. When the period is larger than 35s, the relative error of the two methods is similar to that of Morlet wavelet.) when the period is larger than 35s, the selection of the appropriate 伪 value needs a large amount of time to carry out a lot of experiments in different periods. This shows that the multiplex filtering method is not adaptive, but when wavelet transform is used to analyze the short period signal, the time window becomes narrower, the frequency window becomes longer, and when the long period signal is analyzed, the time window becomes longer, the frequency window becomes narrower, and the frequency window becomes more adaptive to the signal. This is the biggest advantage of wavelet transform compared with multiple filtering method.
【作者单位】：桂林理工大学地球科学学院;中国地质科学院地质研究所大陆构造与动力学国家重点实验室;
【基金】：国家自然科学基金(41574039) 大陆构造与动力学国家重点实验室开放基金(K201505) 广西自然科学基金重点项目(2016GXNSFDA380014)共同资助
【分类号】：P315.31

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