Spectral decomposition of seismic data with improved synchrosqueezing time-frequency transform
Shang, S. and Fu, S., 2022. Spectral decomposition of seismic data with improved synchrosqueezing time-frequency transform. Journal of Seismic Exploration, 31: 53-64. Spectral decomposition is a novel signal analysis tool for seismic data. Resolution of traditional time-frequency transform methods is limited by Heisenberg uncertainty principle. By assigning complex coefficients along frequency or scale axis, synchrosqueezing algorithm is a way to sharpen time-frequency representation towards its ideal representation. Whereas, traditional synchrosqueezing algorithm is not very suitable for signal which contains strong frequency modulated modes. Time-frequency respresentation needs to be further sharped to meet the needs of seismic signal analysis and interpretation. In this paper, we introduce an improved synchrosqueezing algorithm named second-order synchrosqueezing transform into seismic spectral decomposition. With computation of second-order derivatives of the phase of STFT, we can obtain an invertible and sharper time-frequency representation than traditional synchrosqueezing algorithm. The method is applied to synthetic signal and field seismic data. Results show its effectiveness.
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