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2 Acta Geophysica

2 2024

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Time-reassigned multisynchrosqueezing of the S-transform for seismic time-frequency analysis

Liu Wei, Liu Yang, Li Shuangxi

Acta Geophysica

2024

Vol. 72, no. 2

759--775

To accurately capture the time-frequency spectral anomaly, a novel time-frequency analysis (TFA) method, termed as time-reassigned multisynchrosqueezing S-transform (TMSSST), is proposed. In this study, we derive a N-order group delay (GD) estimator designed for frequency-domain S-transform to cope with the signal with fast varying instantaneous frequency (IF). By introducing an iterative reassignment procedure, the proposed TMSSST not only can produce a highly energy-concentrated time-frequency representation (TFR) but also can reconstruct the original signal with a high accuracy. Three synthetic signals are employed to validate the effectiveness of the proposed method by comparing with some classical TFA techniques such as S-transform (ST), synchrosqueezing S-transform (SSST) and time-reassigned synchrosqueezing S-transform (TSSST). It is shown that the TMSSST does a better job in addressing strongly frequency-varying signal. Application on field data further indicates the potential of highlighting subsurface geological structures and thus, facilitating seismic interpretation.

Adaptive time-reassigned synchrosqueezing transform for seismic random noise suppression

Liu Wei, Li Shuangxi, Chen Wei

Acta Geophysica

2024

Vol. 72, no. 2

829--847

Noise suppression is of great importance to seismic data analysis, processing and interpretation. Random noise always overlaps seismic reflections throughout the time and frequency, thus, its removal from seismic records is a challenging issue. We propose an adaptive time-reassigned synchrosqueezing transform (ATSST) by introducing a time-varying window function to improve the time-frequency concentration, and integrate an improved Optshrink algorithm for the suppression of seismic random noise. First of all, a noisy seismic signal is transformed into a sparse time-frequency matrix via the ATSST. Then, the obtained time-frequency matrix is decomposed into a low-rank component and a sparse component via an improved Optshrink algorithm, where the D transformation and its first derivative are further simplified to reduce the computational burden of the original OptShrink algorithm. Finally, the denoised signal is reconstructed by implementing an inverse ATSST on the low-rank component. We have tested the proposed method using synthetic and real datasets, and make a comparison with some classical denoising algorithms such as f - x deconvolution and Cadzow filtering. The obtained results demonstrate the superiority of the proposed method in denoising seismic data.