Achieving a proper time-frequency (TF) resolution is the key to extract information from seismic data using TF algorithms and characterize reservoir properties using decomposed frequency components. The generalized S-transform (GST) is one of the most widely used TF algorithms. However, it is difficult to choose an optimized parameter set for the whole seismic data set. In this paper, we propose to set the parameters of the GST adaptively using the instantaneous frequency (IF) of seismic traces. Our workflow begins with building a relationship between the parameter set of the GST and IF using a synthetic wedge model. We use the IF as an indicator for the time thickness of each trace in the wedge model. We then compute the TF spectrum of each trace using the GST with different parameter sets and compare the similarity between the computed TF spectrum and theory TF spectrum. The parameter set with the largest similarity is regarded as the best parameter set for each trace in the wedge model. In this manner, we build a relationship between the parameter set and IF value. We can finally choose the optimum parameter set for the GST according to the IF values of seismic traces. We name the proposed workflow as the self-adaptive GST (SAGST). To demonstrate the validity and effectiveness of the proposed SAGST, we apply it to synthetic seismic traces and field data. Both synthetic and real data examples illustrate that the SAGST can obtain a TF representation with a high TF resolution.

The S transform (ST) is one of the most commonly used time-frequency (TF) analysis algorithms and is commonly used in assisting reservoir characterization and hydrocarbon detection. Unfortunately, the TF spectrum obtained by the ST has a low temporal resolution at low frequencies, which lowers its ability in thin beds and channels detection. In this letter, we propose a three parameters ST (TPST) to optimize the TF resolution flexibly. To demonstrate the validity and effectiveness of the TPST, we first apply it to a synthetic data and a synthetic seismic trace and then to a filed data. Synthetic data examples show that this TPST achieves an optimized TF resolution, compared with the standard ST and modified ST with two parameters. Field data experiments illustrate that the TPST is superior to the ST in highlighting the channel edges. The lateral continuity of the frequency slice produced by the TPST is more continuous than that of the ST.

Time-frequency (TF) analysis can reveal local variations in seismic data processing and interpretation, where seismic signals are nonstationary and time varying. High-quality TF representation (TFR) is important for revealing the local information about these nonstationary seismic signals and describing geological structures. Due to the Heisenberg uncertainty principle, traditional TF methods (e.g., short time Fourier transform and continuous wavelet transform) cannot get the finest time resolution and the best frequency resolution at the same time, which leads to ambiguous TFR with a negative effect on the seismic signal analysis. Concentration in frequency and time is proposed to distinguish the different TF contents of time-dependent signals with time-varying amplitude and instantaneous frequencies. We introduce this promising TF analysis tool to seismic data processing. Experiments on synthetic signals and seismic data show its validity and effectiveness, which is helpful for seismic data interpretation in the future.

In this letter, a new method is introduced for a seismic time-frequency (TF) analysis. The proposed method is called synchrosqueezing generalized S-transform (SSGST), which belongs to a postprocessing procedure of the GST. The frequency-dependent Gaussian window used in the standard S-transform may be not suitable for real applications. In order to overcome this limitation, the frequency-dependent Gaussian window is replaced by a parameterized function containing three parameters. These three parameters result in flexibility in the variation of TF resolution. Then, the synchrosqueezing transform is employed to squeeze the TF coefficients of the GST to achieve an energy-concentrated TF representation. Synthetic examples and field data show that the SSGST achieves a high resolution and has the potential in highlighting geological structures with high precision.

By building an adaptive filter bank, the empirical wavelet transform (EWT) decomposes an analyzed signal into several intrinsic mode functions (IMFs). Although some applications have certified the effectiveness of the EWT, the effectiveness of the EWT is affected obviously when analyzing nonstationary signals (e.g., seismic data). In this letter, we propose an improved EWT (IEWT) to decompose a nonstationary seismic signal into several IMFs and describe its frequency features. After computing the Fourier spectrum of the seismic signal, the scale-space representation (SSR) is used to extract the slowly varying component of the Fourier spectrum. Then, the frequency components contained in the seismic signal and the boundaries can be obtained using the central frequency information. Finally, we obtain an adaptive spectrum segmentation using detected boundaries based on the SSR. Afterward, the proposed algorithm obtains accurate and stable IMFs in decomposing the nonstationary seismic signal. To demonstrate the effectiveness of the proposed IEWT, we apply it to synthetic seismic signal and field data.

Quality factor Q can be used for hydrocarbon detection and reservoir characterization. The commonly used methods for Q estimation include the logarithm spectral ratio (LSR) method, the centroid frequency shift (CFS) method, and the peak frequency shift (PFS) method. However, all these methods are existing limitations respectively. The LSR method is sensitive to random noise. The CFS and PFS methods are lack of applicability owing to their assumptions of the source wavelet type. To overcome these limitations, we derived a new formula without any wavelet assumption. This formula establishes a relationship between the Q factor and the spectral centroid downshift concisely through the Jeffery divergence. The proposed method is named the generalized centroid frequency shift (GCFS) method. Compared with the conventional methods, the proposed method can achieve higher accuracy and better antinoise performance. In addition, the proposed method is suitable for various source wavelet types. Synthetic and field examples demonstrate the effectiveness and potential of the proposed method.