Application of the Synchrosqueezed Wave Packet Transform in Seismic Time-frequency Analysis

Wang, Q.; Gao, Jian; Liu, N.H.

doi:10.3997/2214-4609.201601390

Cited by 1 publication

(2 citation statements)

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“…Most of the abovementioned methods have been used in seismic signal processing (Huang et al, 2016;Wang and Gao, 2017;Wang et al, 2018). In addition to this, Herrera et al (2014) applied the SST to time-frequency analysis of seismic signals for the first time and compared it with CWT and complete ensemble empirical mode decomposition (CEEMD) in detail, which verified the superiority of this method in the time-frequency analysis of seismic signals.…”

Section: Introductionmentioning

confidence: 81%

“…In essence, it recalculates a position close to the real coordinates of the time-frequency energy spectrum so as to rearrange the energy according to it. In recent years, SST has been applied in the rearrangement of various original time-frequency representations, including the continuous wavelet transform (CWT) (Daubechies et al, 2011), wave packet transforms (Wang and Gao, 2017), STFT (Oberlin et al, 2014), S transform (Huang et al, 2016), and generalized S transform (Wang et al, 2018). In addition, some new techniques based on the notion of the SST have been proposed, such as the high-order synchrosqueezed transform (Liu W. et al, 2018), concentration of frequency and time (ConceFT) (Daubechies et al, 2016), synchroextracting transform (Yu et al, 2017), and others (Li and Liang, 2012;Thakur et al, 2013;Liu N. et al, 2018;Xue et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Application of high-resolution processing in seismic data based on an improved synchrosqueezing transform

Wang

Cheng

2022

Front. Earth Sci.

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This study proposes a high-resolution processing technique for seismic data based on the improved synchrosqueezing transform (SST). The SST rearranges the complex spectrum of the wavelet transform along the frequency axis. However, the energy is not concentrated in the position with the faster frequency change rate. To overcome these problems, the proposed method first transformed the seismic signal into an analytical one via Hilbert transformation and then determined the phase correction value (x0(t)) before and after the transformation. This was achieved using the instantaneous frequency attribute (ω(t)) of the analytical signal and a specific frequency (ω0) selected as the dominant frequency of the input data to adjust the change rate of the signal frequency before the SST falls close to zero, which can improve the time–frequency resolution after compression transformation. The essence of this method only adjusts the phase of the signal before and after transformation, without affecting the inverse expression of the SST. The one- and two-dimensional model data results show that the proposed method can better identify the time–frequency distribution characteristics of seismic signals and improve their resolution and the focusing effect in the time–frequency domain. The proposed method shows good prospects for application in gas reservoir detection and identification.

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