Time–frequency (TF) analysis (TFA) plays an important role in seismic hydrocarbon reservoir identification, which is attributed to its ability to effectively identify the oil and gas seismic response characteristics of geological bodies in different frequency bands. In this letter, we introduce a novel TFA method termed high-order synchroextracting transform (SET) and apply it to the high-precision identification of gas-bearing reservoirs. Under the premise of short-time Fourier transform (STFT), this method defines a new synchroextracting operator (SEO) based on high-order approximations of signal amplitude and phase. Furthermore, only the TF information highly correlated with the TF characteristics of the signal is extracted from the STFT spectrum by using the SEO. Therefore, for a wider variety of the nonstationary signal, a highly energy-concentrated TF representation can be effectively obtained. The application of STFT and different-order SET on 1-D synthetic signal and field seismic data verifies the effectiveness of the proposed method.

Seismic reflectivity inversion is widely applied to improve the seismic resolution to obtain detailed underground understandings. Based on the convolution model, seismic inversion removes the wavelet effect by solving an optimization problem. Taking advantage of the sparsity property, the ℓ ₁ -norm is commonly adopted in the regularization terms to overcome the noise/interference vulnerability observed in the lp-losses minimization. However, no one has provided a deterministic conclusion that ℓ ₁ -norm regularization is the best choice for seismic reflectivity inversion. Instead of using an unproved fixed regularization norm, we propose an optimal seismic reflectivity inversion approach. Our method adaptively adopts an ℓ _p -loss-ℓ _q -regularization (i.e., ℓ _p,q -regularization) for p = 2, 0 <; q <; 1 to estimate a more accurate and detailed reflectivity profile. In addition, we employ a K-fold cross-validation (CV)-based approach to obtain the optimal damping factor λ to further improve the seismic inversion results. The letter starts with the introduction of nonconvex constraint for seismic inversion and the necessity of the ℓ _q -norm regularization. Then, the majorization-minimization and CV algorithms are briefly described. The performance of the proposed seismic inversion approach is evaluated through synthetic examples and a field example from the Bohai Bay Basin, China.

In this paper, a new time-frequency analysis method—Synchrosqueezing Generalized S-transform (SSGST)—is proposed to meet the needs of high-resolution seismic signal processing and interpretation. The basic wavelet of the generalized S-transform (GST) in the paper is a modulated harmonic wave with four undetermined parameters that can be constructed by adjusting the four parameters to make the GST more suitable for seismic signals processing. The SSGST method squeezes and reconstructs the complex coefficient spectra of GST results along the frequency direction so that the energy distributions on the time-frequency spectra are concentrated around the real instantaneous frequency of the signal; thus, the time-frequency resolution can be improved. Based on mathematical theory, the basic principle of the new transformation method is given, and the mathematical expressions of the positive transformation and lossless inverse transformation of the method are strictly deduced. The experimental results of numerical signals illustrate that the proposed method can correctly decompose signals with different spectral characteristics into a high time-frequency resolution spectrum and can recovery the original signal from the time-frequency spectrum with satisfying reconstructing accuracy. Application on field seismic data shows the superiority of the new method in seismic time-frequency analysis for hydrocarbon detection.