Jiang Chanjun, Wang Youxue, Xiong Bin, Yun Peng, Xu Jinrong, Nai Zhenlong. 2017: Measurement of surface wave group velocity using wavelet transform. Acta Seismologica Sinica, 39(3): 356-366. DOI: 10.11939/jass.2017.03.005
Citation: Jiang Chanjun, Wang Youxue, Xiong Bin, Yun Peng, Xu Jinrong, Nai Zhenlong. 2017: Measurement of surface wave group velocity using wavelet transform. Acta Seismologica Sinica, 39(3): 356-366. DOI: 10.11939/jass.2017.03.005

Measurement of surface wave group velocity using wavelet transform

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  • Received Date: August 01, 2016
  • Revised Date: November 18, 2016
  • Published Date: April 30, 2017
  • Wavelet transform is proposed to measure surface wave group velocity in this paper. Several common mother wavelets are respectively used to calculate Rayleigh wave group velocities of the same synthetic seismograms, the results are compared with the theoretical group velocities. The comparison results indicate that Morlet wavelet is the most effective one in calculating group velocities. Furthermore, we compare Morlet wavelet transform with the most common used multiple filter technique in calculating group velocity, from which the following conclusions are drawn: ① multiple filter analysis method extremely depends on the value of Gaussain filter parameter α, and smaller value of α should be chosen during long period of surface wave; ② when the value of α is chosen adaptively, multiple filter analysis method is more accurate than Morlet wavelet transform in calculating group velocities when the period of surface wave is between 20 seconds and 35 seconds, while multiple filter analysis method has the similar accuracy with Morlet wavelet transform when the period of surface wave is over 35 seconds; ③ multiple filter analysis method will cost much more time to choose adaptive value of Gaussain filter parameter α for different periods without self-adaptivity to signals; however, wavelet transform can narrow the time window and lengthen the frequency window when analyzing short period signals and lengthen the time window and narrow the frequency window when analyzing long period signals with self-adaptivity to signals, which shows the outstanding advantage of wavelet transform over the multiple filter analysis method.
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