Abstract
In this paper, we propose a novel seismic time-frequency analysis method via the time-reassigned synchrosqueezing transform (TSST), in which the time-frequency coefficients are reassigned in the time direction rather than in the frequency direction as the short-time Fourier-based synchrosqueezing transform (FSST) does. Such a technique can not only produce a highly concentrated time-frequency representation (TFR) for a wide variety of strongly frequency modulated signal, but also allow for the reconstruction of the modes with a high accuracy. Numerical experiments on synthetic signal and field data demonstrate the effectiveness of this new method, and show that the proposed method is more suitable for extracting seismic time-frequency feature and identifying the thin layers compared with the traditional FSST, which offers the potential in highlighting subtle geological structures.
Highlights
Extracting useful information from large amounts of recorded data is important for a large number of real-world applications [1]–[3]
We proposed a new seismic Time-frequency analysis (TFA) method based on the timereassigned synchrosqueezing transform (TSST), in which the reassignment operation is implemented in the time direction [31], rather than frequency direction as the synchrosqueezing transform (SST) and Fourier-based synchrosqueezing transform (FSST) do
The contributions of the paper can be summarized as below: (1) we first investigate the time-reassigned property of TSST and achieve seismic time-frequency feature extraction via the TSST, (2) our method shows the excellent potential in subtle geological structures characterization compared to the conventional short-time Fourier transform (STFT) and FSST approaches
Summary
Extracting useful information from large amounts of recorded data is important for a large number of real-world applications [1]–[3]. We proposed a new seismic TFA method based on the TSST, in which the reassignment operation is implemented in the time direction [31], rather than frequency direction as the SST and FSST do. This results in the perfect energy concentration on the TFR for the signal with fast varying IF. The σ is often related with the time-frequency energy concentration, while the γ controls the computation accuracy with mode retrieval It could result in a blurred TFR and the inaccurate signal reconstruction if the parameters are not appropriately selected. The σ and γ can be obtained by several trials
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