High-resolution characterization of subsurface hydrocarbon reservoirs critically depends on the integration of seismic and well log data. However, the precision of well-to-seismic tie procedures is often compromised by inaccuracies in time-depth conversion curves, stemming from imprecise migration velocity errors, scale discrepancy and the inherent domain disparity between seismic data (time-domain) and well log data (depth-domain). Traditional methods rely on iterative wavelet estimation and manual adjustments, leading to wavelet-depth curve coupling problems, reduced accuracy, and significant time consumption. To address these limitations, we propose a novel automated time-depth conversion curve correction method. This approach leverages key feature matching in the time-frequency domain of well log reflection coefficients and borehole-side seismic traces, incorporating dual constraints: stratigraphic constraints in the time domain and spectral notch points in frequency domain. By directly aligning with well log reflection coefficients, this time-frequency joint analysis eliminates the need for wavelet estimation as well as iterative and interactive processes. Experiments performed using both synthetic and field data demonstrate the validity and effectiveness of the proposed method compared to traditional iterative matching method and interactive commercial software.

As an important elastic parameter, seismic acoustic impedance is usually obtained through poststack inversion. However, there are usually two problems that limit the quality of the inversion results. First, conventional inversion methods typically use regularization terms to enhance the stability of the inversion results, and effective regularization terms are particularly important for accurately inverting seismic impedance. Second, most inversion methods adopt a trace-by-trace inversion strategy, resulting in poor lateral continuity when connecting the inversion results of all traces into a 2-D profile, especially for processing noisy data. To address these two problems, we propose a structure-oriented minimum entropy stabilizer for acoustic impedance inversion that enhances the lateral continuity of the inversion results while restoring the blocky structures of the strata and improving the resolution of the inversion results. The stabilizer consists of a structure-oriented regularization (SOR) operator and the minimum entropy norm. The SOR operator is constructed using the local dip estimated from the seismic data by the plane-wave destruction (PWD) algorithm and constrains the inverted impedance along the structural trend, making it more consistent with geological features. The minimum entropy norm restores the blocky structures and enhances resolution by imposing sparse constraints on the temporal and spatial derivatives of the impedance. Based on synthetic and field seismic data, we compare the inversion results of the proposed method with those of conventional Tikhonov regularization and total variation (TV) regularization methods. The results show that the proposed method exhibits superior performance, especially in processing noisy data.

Seismic inversion is a critical research area in seismic data interpretation. Given the powerful feature extraction and representation capabilities of deep neural network (DNN), it has been widely adopted in the seismic reservoir parameters inversion. However, the majority of DNN-based inversion methods use 1-D models due to the scarcity of well-logging labels, which are only 1-D time series. The performance of higher-dimensional DNN-based inversion methods depends on the quality of the initial inversion results, leading to an interdependence between the model and data in the time and space dimensions. Here, we propose a model and data co-evolution method for seismic reservoir parameters inversion. It employs a 1-D DNN model-based closed-loop model to generate initial reservoir inversion results. Then, the evolutionary 2-D model learns spatial structural features constrained by the initial reservoir inversion results to improve the spatial continuity. We tested the proposed method on synthetic seismic data with multiple fault structures, achieving the lowest inversion error and highest inversion accuracy. It also exhibits the highest accuracy in real seismic data with the structural features of underground rivers being more pronounced.

Quality factor (Q) estimation is critical for the processing of nonstationary seismic data and is an important indicator of oil and gas. Traditional methods for Q value estimation require the identification of the top and bottom of each constant Q layer, which can be challenging in the processing of field seismic data. Deep-learning (DL)-based Q inversion methods leverage the powerful nonlinear fitting capabilities of deep network to automatically obtain interval Q estimates directly from the input seismic data. However, these methods possess so-called “black box” characteristics and lack interpretability, thereby limiting their practical application. To address these issues, this study proposes a physics-driven neural network (PDNN) that integrates physical knowledge with deep neural networks, embedding the frequency-shift method for Q value calculation into the computational layers of the network. Our approach uses nonstationary seismic signals and their corresponding logarithmic time-frequency amplitude spectrum (LTFAS) as input. The neural network decouples the dynamic wavelets and reflection coefficients to obtain the LTFAS of dynamic wavelets. Furthermore, a network layer is designed based on the frequency-shift method to generate the interval Q curve. Experiments on both synthetic and field data demonstrate that the neural network constrained by physical knowledge can alleviate the instability in interval Q calculations, yielding more stable Q estimates. Additionally, this approach enhances the interpretability and generalization capabilities of DL methods, offering significant practical value.

Based on the Bayesian framework, pre-stack inversion aims to find the solution with the maximum posterior probability under the prior constraint. The prior constraint is usually a mathematical expression of the inverted parameters, and guides the updating of the inversion results. To obtain a stable, high-resolution and high-fidelity inversion result, we introduce a new prior constraint named ‘amplitude entropy’ to help perform the pre-stack inversion. The amplitude entropy can make the chaos of the parameters to be inverted close to those of the known well-logging data. Compared with the traditional L2 prior constraint, amplitude entropy can improve the resolution of the inversion results, and compared with the conventional L1 prior constraint, it can obtain a solution that is more consistent with the geological characteristics. In addition, multi-component seismic data contains richer lithology and fluid information than single-component data. Therefore, in this paper, we directly develop the pre-stack inversion method based on the multi-component seismic data. Furthermore, due to the high nonlinearity of the objective function under the amplitude entropy constraint, the quantum annealing (QA) algorithm is employed to solve the objective function of the joint inversion and find the final solution. Synthetic and field data examples demonstrate that the pre-stack inversion method with the amplitude entropy constraint is effective and stable, especially for processing seismic data with a low signal-to-noise ratio.

Seismic imaging technology, which analyzes seismic wave propagation and reflection to gather data on underground geological structures, is vital for geological exploration. Due to factors such as the anisotropy of subsurface media, migration velocity errors, and drift of seismic streamers in marine environments, observation points at the same position exhibit horizontal and vertical displacements in different common offset gathers (COGs), thereby diminishing stacking coherence and compromising imaging quality. Consequently, the nonflattened seismic events in common image gathers (CIGs) extracted from COGs can lead to false amplitude variations with offset. Traditional CIG enhancement methods like cross-correlation matching encounter challenges such as slow inference speed, limited accuracy, the capability to predict only a single directional displacement, and difficulty in parameter tuning. Therefore, based on optimizing local normalized cross-correlation matching, an interpretable deep learning method to enhance CIGs using a self-supervised optical flow estimation network is proposed. Experiments performed using both synthetic and field data demonstrate the validity and effectiveness of the method.

The time–frequency analysis methods are powerful tools in seismic interpretation and bright spot identification. The S-transform (ST), as a hybrid of the short-time Fourier transform (STFT) and continuous wavelet transform (CWT), can achieve progressive time–frequency resolution. However, limited by its linear-frequency-dependent term, the ST obtains a deviated frequency distribution. By removing this term, the frequency form of unscaled ST (FUST), as a variation of the ST, is proposed to preserve the reliable frequency distribution. The fly in the ointment is that the FUST decreases the temporal resolution of the low-frequency components, which may not be suitable for seismic reflection interpretation and reservoir location. In addition, the ST cannot tailor the time–frequency resolution for particular applications. To solve these problems, a simple and effective method is proposed by substituting the basis of the FUST. The proposed method can obtain the desired time resolution by adjusting two adjustable parameters. The corresponding inverse transform is also derived to guarantee its energy conservation and inevitable property. Numerical experiments and real data examples show better performance of the proposed method in improving temporal resolution and reservoir location over the ST and FUST.