Conventional acoustic full-wavefield migration (AFWM) based on one-way wave propagators is typically derived under the assumption of an acoustic medium. However, real subsurface media exhibit viscoacoustic properties, where seismic wave propagation is accompanied by amplitude attenuation and phase dispersion. As a result, AFWM and wavefield simulation approaches fail to accurately capture the viscoacoustic effects of realistic subsurface media. To address this issue, this paper derives a one-way wave propagator for viscoacoustic media based on the time-fractional viscoacoustic wave equation, and incorporates it into the framework of full-wavefield migration (FWM). A novel full-wavefield imaging method, referred to as viscoacoustic full-wavefield migration (QFWM), is proposed. The proposedQFWM method is tested and compared with AFWM methods. Numerical experiments on a simple three-layer horizontal model, a gas chimney model with a highly attenuative gas layer, and the Marmousi model demonstrate thatQFWM significantly outperforms AFWM in imaging quality and multiple wave suppression when applied to viscoacoustic data. Furthermore, real data tests validate the superiority of the proposed method over AFWM in terms of imaging quality. The proposed method highlights the critical importance of accounting for viscoacoustic effects in seismic imaging and offers a robust solution for improving the accuracy of FWM in complex subsurface media.

The popular geological anomaly (such as fault, river course, cave, and crack) identification methods, such as coherence cube, semblance, likelihood, and others, usually can achieve higher precision geological anomaly identification results when applied to the target horizon flattened seismic data, comparing to their counterparts using the target horizon-unflattened seismic data. However, these methods still face great challenges in achieving high-precision geological anomaly identification results, due to the complexity of the geological structure (or the seismic data) and the horizon tracking accuracy of the target horizon. To minimize the impact of the complexity of geological structure and the horizon tracking accuracy of the target horizon in geological anomaly identification, thereby obtaining high-precision geological anomaly identification results and providing precise labels for deep-learning-based geological anomaly identification methods, we propose a seismic facies-guided high-precision geological anomaly identification method (FHGI), basing on the concept of seismic facies and the cross-correlation algorithm. FHGI contains the flowchart of FHGI, and the seismic facies-guided trace-by-trace high-precision geological anomaly identification factor calculation (FTGC); in which FTGC consists of the target horizon-based seismic data flattening (THF), the seismic facies-guided target trace 2-D subseismic dataset generation (FTG), the cross-correlation algorithm-based target horizon further flattening (CFA), and the cross-correlation coefficient-based high-precision geological anomaly identification factor calculation (CGC). The THF aims to reduce the impact of the complexity of the geological structure and provide the input 3-D seismic data for the FTG. FTG aims to automatically generate the 2-D subseismic dataset corresponding to the target trace, thereby further reducing the impact of the complexity of the geological structure and providing the input 2-D subseismic dataset for CFA. CFA takes the target trace in the result of FTG as the reference for cross-correlation functions calculation and then uses them to further flatten the target horizon in the result of FTG, thereby minimizing the impact of the horizon tracking accuracy of the target horizon and providing the input 2-D subseismic dataset for CGC. CGC takes the target trace in the result of CFA as the reference for cross-correlation coefficient calculation and then uses them for high-precision geological anomaly identification factor calculation, thereby providing high-precision geological anomaly identification results. A public synthetic seismic dataset and actual 3-D seismic dataset examples demonstrate that FHGI has great potential as a technique for geological anomaly identification.

Accurate seismic velocity inversion is crucial for oil and gas exploration. The popular fixed-scale regular-grid adjoint-state first-arrival (or first-arrival travel-time) slope tomography seismic velocity inversion method (FFAST) (or adjoint-state first-arrival slope tomography seismic velocity inversion method with fixed-scale regular-grid) can obtain good seismic velocity inversion results, but it still faces great challenges in achieving desirable high-precision seismic velocity inversion results due to its fixed-scale regular-grid. In this article, we use the multiscale staggered grid to replace the fixed-scale regular-grid in FFAST for model parametrization and propose the multiscale staggered-grid adjoint-state first-arrival (or first-arrival travel-time) slope tomography seismic velocity inversion method (MFAST), thereby obtaining high-precision seismic velocity inversion result. The staggered-grid is composed of a finite set of fixed-scale regular-grids with spatially staggered (or overlapped) relationships, which aims to change the grid coordinate to fully sample the structure information in the velocity model space with multiple fixed-scale regular-grids. The multiscale staggered-grid is composed of multiple staggered-grids with different fixed scales, which aims to adapt to the different scale complex structures in the velocity model space; in which, the large-scale staggered-grid based MFAST aims to reconstruct the large-scale background structures, thereby providing the essential guidance (or prior) information for the small-scale staggered-grid based MFAST which aims to obtain the detailed structural information. The model parametrization with multiscale staggered-grid is achieved by performing the model parametrization with regular-grid multiple times; the mean or median value of the outputs of multiple model parametrizations with regular-grid is considered the output of MFAST in the current iteration, and used to iteratively update the velocity model obtained by MFAST in the previous iteration. The checkboard and Marmousi model testing validate the effectiveness of MFAST.

The anelastic properties of geophysical media lead to amplitude loss and phase distortion of the seismic waves propagating through the subsurface strata, which significantly affects accurate seismic migration and reasonable interpretation of seismic data. To accurately restore the true information of subsurface media, it is now a consensus among geophysicists to no longer consider subsurface media as acoustic cases, but to incorporate the viscosity of subsurface media. To obtain precise imaging results using the time-fractional viscoacoustic wave equation based on the constant Q model, we derived frequency-domain decoupling of amplitude loss and phase dispersion in the forward simulation and developed two Q-compensated reverse time migration (Q-RTM) methods in the frequency domain by using two imaging conditions involving cross correlation imaging condition (CCIC) and deconvolution imaging condition (DIC), considering the attenuation effect of viscosity media. Numerical experiments on a simple three-layer model and a gas chimney model demonstrate the feasibility and effectiveness of the developed strategies. Compared with conventional acoustic RTM schemes, Q-RTM can enhance the resolution of imaging results and compensate for attenuated seismic waves. The superiority of the suggested approach is further confirmed by applying real seismic data. In summary, the proposed method, after fully considering the realistic information of the viscoacoustic medium, achieves imaging results with a wider frequency spectrum compared to conventional RTM in nonattenuating media, filling the gap in frequency-domain RTM with constant Q model.

Seismic facies analysis is a crucial foundation for basin-fill studies and oil and gas exploration. With its rapid development, CNN-assisted interpretation is becoming increasingly popular. However, CNN models are often considered "black boxes" that lack transparency. To understand how CNN models classify seismic facies and visualize the contribution of each seismic attribute to the final predictive scoring, we have investigated class activation map (CAM) techniques and an explainable tool called Shapley additive explanations (SHAP) value. Based on real seismic data collected in the Sichuan basin, we compared the visualization performances of CAM and SHAP methods and found that the SHAP tool has better visualization capabilities than CAM methods, which only produce heat maps with positive values. Using SHAP values, we identified the importance of each seismic attribute and refined redundant attributes. This approach establishes a connection between seismic attributes and sedimentary environments and is a prime example of the capability of deep learning to discover knowledge beyond human experience. We applied the selected seismic attributes to generate a refined CNN model and compared it to the original CNN model, demonstrating the superiority of our proposed strategy. When we compared the predicted seismic facies using the refined CNN model based on SHAP features, the conventional K-means, SVM and Gaussian Naive Bayes methods, it is observed that our predicted map aligns well with geological knowledge with less prediction errors, demonstrating the effectiveness and feasibility of our developed strategy.

Sparse impedance inversion is widely applied for hydrocarbon prediction. Due to the low sparseness of traditional sparse constraints, pseudolayer and low-resolution problems still exist. To overcome this barrier, an impedance inversion method based on the reweighted L1-norm sparse constraint is proposed. Different from the traditional L1-norm constraint, which considers the location information of impedance boundaries, the reweighted L1-norm uses the amplitude information of impedance boundaries. The amplitude information can improve the sparseness, which helps inversion obtain more precise boundaries of impedance and weaken the pseudolayer phenomenon. Both the reweighted L1-norm constraint and initial model constraint are used to construct the objective function. The alternating direction method of multipliers (ADMM) algorithm is utilized to obtain the inversion algorithm. Both synthetic and field data tests prove that the impedance boundary of the proposed method is more accurate, and the pseudolayer phenomenon is weakened.

In seismic interpretation, horizon picking is a very essential but time-consuming and challenging task. Most existing auto-picking algorithms have been proposed to improve the horizon interpretation efficiency. Recently, deep learning approaches have shown promising performance in horizon identification. However, feeding directly seismic time series or images into a deep learning network only uses the amplitude information of seismic signal, which limits the classification accuracy. In this letter, we propose to learn more distinctive characteristics in the time–frequency domain from the continuous wavelet transform (CWT) coefficients. More importantly, we develop a novel two-branch convolutional neural network (TB-CNN) for horizon picking: a CWT branch can mine the time–frequency features in 2-D CWT coefficients of seismic time series. At the same time, a spatial branch further explores the local spatial features in seismic images. The features of the two branches are then fused to perform classification. The output is the class scores of voxels being horizon or background. Finally, we extract the horizon surface by finding all voxels with the highest score values of the horizon class in the vertical temporal direction. We conduct experiments on both synthetic and field data. The results show that the proposed method can effectively fuse the spatial features and time–frequency features to yield higher performance than the traditional 3-D auto-tracking method.

We propose a stable seismic Q estimation approach by employing quantum mechanics-based signal representation. For Q estimation, we project a seismic trace onto a specific basis composed of wave functions constructed through the resolution of the Schroedinger equation of non-relativistic quantum mechanics at first. Then, based on the specific basis, we derive the quantum mechanics-based Q estimation approach in the local frequency-projection coefficient domain. The Planck constant and the control factor are the two key factors for the quantum mechanics-based Q estimation method. Compared with the traditional methods, the quantum mechanics-based Q estimation approach shows more stability and noise robustness. The synthetic and field data applications illustrate the effectiveness and the superiority of the proposed method. The quantum mechanics-based Q estimation method offers a new field and provides a complementary way for measuring seismic attenuation.

In our work, the deep learning technique has been used to develop an automatic method for identifying the first arrival times of seismic waves. This method introduces transfer learning to train a deep neural network, given a limited number of continuous wavelet transform (CWT) feature images as input. The application of the CWT for feature extraction, aimed at detecting abrupt changes in the amplitude, phase, and frequency produced by first arrivals as a whole rather than any single characteristic, provides the most informative images. First, we apply the CWT to each seismic trace to obtain the CWT feature images and split them into a set of subimages. Then, a pretrained convolutional neural network (CNN) is fine-tuned with limited labeled subimages. The resulting model can be used to predict probability distributions of noise, first-break, and post first-break. Finally, the first arrival times are extracted from the peaks of the probability distributions. We have tested the performance of the method using vibroseis, dynamite, and air gun shot records, which include various types of seismic waves and noise. More accurate and robust results can be obtained with the proposed method compared with the short-time and long-time average (STA/LTA) algorithm and the adaptive multiband picking algorithm (AMPA).