## 1 Introduction  
Time-frequency analysis, a powerful tool for seismic data analysis, plays a significant role in oil and gas exploration and development. It maps a 1D signal in the time domain into a 2D time-frequency spectrum, which can effectively reveal important details of seismic data and provide valuable information for reservoir characterization. The commonly used methods for seismic time-frequency analysis include short-time Fourier transform (STFT), wavelet transform (WT), and S-transform (ST). STFT suffers from a fixed time-frequency resolution caused by a preselecting window length. To overcome this disadvantage, wavelet-based methods have been adopting and show obvious superiority in spectral resolution (e.g., [1]). Although WT leads to a more flexible trade-off between frequency and time resolution due to the variable length of the mother wavelet, it still displays spectral smearing due to the finite size of the operator. The S-transform (ST) proposed by [PERSON] et al. [2] can be interpreted as a special case of the Continuous Wavelet Transform (CWT) based on Morlet wavelet, but with minor phase and amplitude adjustments.. It eliminates the limitation of fixed window length in STFT and also has the multi-resolution ability of WT. In other words, ST can effectively avoid the existing deficiencies of the above two methods [3]. Nevertheless, the fixed changing trend of the basic wavelet in ST limits its practical application. Therefore, many researchers have proposed a variety of forms of generalized S transform (GST) on the basis of ST by introducing variable window parameters to improve the flexibility and adaptability of the wavelet window [4; 5; 6; 7; 8; 9]. The method based on Empirical Mode Decomposition (EMD) [10] or its extended algorithms (e.g., ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD)) are other time-frequency transforms, which have also been applied in seismic data and have achieved excellent time-frequency resolution (e.g., [11; 12; 13; 14]). Although EMD-based methods are useful for many applications, there is still a lack of mathematical foundation and a high computational cost. Synchrosqueezing transform (SST) is a relatively new technique based on the combination of time-frequency methods followed by a reassignment step [15; 16] that can be also regarded as an alternative to the EMD method considering their similar time-frequency resolution. SST can improve the energy concentration of time-frequency representation by applying a post-processing reallocation to the original representation. At each time or space location, the SST reassigns values of the initial representation based on their local oscillation. However, differing from EMD, the transformation process of SST is generated by strict mathematical derivation, which is beneficial to the understanding of the method and can be improved according to the specific application. At present, the synchrosqueezing algorithm has been extended to time-frequency representations based on the STFT [17; 18; 19], the wave packet transform [20], the curvelet transform [21], the ST [22], or the CWT [23; 24], which is used in the synthetic examples of this paper. SST was originally applied to the audio signal analysis. Subsequently, many efforts have been made to use this method for seismic data processing and interpretation. In 2014, [PERSON] et al. [25] identified channels from seismic data using SST successfully and highlighted the advantages of this method over EMD and EEMD. In 2015, they also used SST to separate the P and S waves of microseismic data [26]. Later, [PERSON] et al. (2016) [27] used it for microseismic detection. [PERSON] and [PERSON] (2017) [28] showed that SST can be used to improve the denoising of seismic data. [PERSON] et al. (2017) [15; 29] used it for attenuation estimation. The generalized S-transform (GST) introduced by [PERSON] et al.