The popularity of blended acquisition is surging in the field of seismic exploration because of its higher efficiency and lower cost. As a compromise, a sophisticated deblending framework should be applied to remove interference noise. However, blended sources are usually fired at off-the-grid (OTG) samples, and the recorded data are incomplete because of some inevitable barriers and instrument errors, increasing the challenges to apply classic deblending methods to OTG data. Typically, the binning process and data reconstruction will be introduced for OTG incomplete data before subsequent deblending. Nevertheless, the binning process may cause amplitude and phase distortion, degrading the deblending accuracy. To overcome this problem, we propose a deep learning (DL)-based method without a binning process for OTG deblending and reconstruction, namely, OTGDR, avoiding the errors related to preprocessing routines. The proposed OTGDR framework contains two components: deep image prior (DIP)-inspired coherency-enhancing network and bilinear operator-guided projection onto convex set (POCS) iteration. The DIP incorporates several fully connected (FC) layers, attention mechanism, and skip connection to extract useful features selectively for superior performance, and the following POCS aims to remove the blending and ambient noise iteratively for enhanced signal-to-noise ratio (SNR). Moreover, the proposed OTGDR is completely data-driven and does not require labels for training, which increases its generality and enables it to adapt to different datasets. In our experiments, we compare the proposed OTGDR with classic deblending methods, and the results demonstrate that OTGDR shows superior performance on OTG denoising and reconstruction in terms of fidelity and SNR.

The inherent non-uniqueness problem challenges acoustic impedance inversion, and thus it is meaningful to explore the possible solutions via advanced strategies, e.g., incorporating uncertainty estimation. At present, several generative adversarial network (GAN)-based inversion methods have been shown to offer advantages in terms of inversion accuracy. However, most of them have primarily focused on deterministic predictions, limiting their ability to explore the range of the solution space. Furthermore, the scarcity of labeled data pairs in field data tasks can reduce inversion accuracy. To address these shortcomings, we introduce a Bayesian GAN (BGAN) based onBayes by Backprop, and integrate it into a closed-loop framework. Synthetic data experiments demonstrate that the closed-loop BGAN performs better than cycle-consistent GAN (cycle-GAN) with insufficiently labeled data pairs. Moreover, unlike the cycle-GAN, the closed-loop BGAN possesses the capability of assessing prediction uncertainties. Compared with the Bayesian linearized inversion (BLI) and Monte Calor (MC) dropout methods, the closed-loop BGAN is more accurate and robust in the inversion of noisy seismic data with lower uncertainty. Therefore, the closed-loop BGAN can achieve high accuracy inversion while estimating potential solutions more reasonably. The field data example also demonstrates that compared with BLI and MC dropout, the closed-loop BGAN can obtain more reasonable inversion results with more reliable uncertainty estimation.

Estimation of porosity and saturation is crucial for shallow gas hydrate exploration in deepwater areas. Owing to the inherent nonlinearity of rock-physics models, many inversion algorithms adopt probabilistic and statistical approaches involving extensive forward simulations, which results in low computational efficiency. The conventional two-step rock-physics inversion process introduces uncertainties that may accumulate as errors. Moreover, traditional pre-stack inversion methods typically neglect absorption and attenuation effects during seismic wave propagation, thereby degrading the quality of the inversion results. To address these limitations and directly estimate porosity and saturation from nonstationary seismic records, we propose a Q-compensated multidimensional rock-physics inversion method. We derive the Jacobian matrix from the rock-physics model to employ first-order Taylor series approximations in the forward modeling. By integrating the linearized rock-physics model with the Aki-Richards equation, we establish a quantitative relationship between petrophysical parameters and seismic records within gas hydrate reservoirs, while accounting for the differential effects of propagation paths on absorption and attenuation across seismic channels. During the inversion process, we utilize shaping regularization in seislet-domain to enhance both accuracy and stability, particularly under noisy conditions. This method not only improves the lateral continuity of seismic inversion results but also mitigates inversion errors caused by noise, thereby enhancing the reliability of the estimates. Results from synthetic and field datasets demonstrate that the proposed method significantly improves both the accuracy and robustness of the inversion.

Quality factor Q is an important parameter that accounts for the amplitude dissipation and phase distortion of seismic waves propagating in the Earth’s interior. Q estimation with improved accuracy benefits nonstationary seismic inversion, seismic imaging, fluid identification, and so on. Usually, logarithmic spectral ratio (LSR) is widely used to estimate Q based on vertical seismic profile (VSP) and poststack data. However, LSR is very sensitive to noise, and the effect of normal moveout (NMO) distorts the spectrum of the stacked seismic data, leading to an inferior Q estimation result. To weaken the effect of NMO and enhance the accuracy of Q estimation, we expand an improved LSR method in the zero-offset traveltime-local slope (e.g.,$t_{0}-p$) domain and propose a robust prestack Q estimation method based on common midpoint (CMP) gathers. The proposed method incorporates warped mapping (WM) and shaping regularization to stabilize it during Q estimation in the case of low signal-to-noise ratio (SNR). Additionally, we incorporate nonzero-offset information for Q estimation, which weakens the strong dependence on zero-offset information during prestack Q estimation. Compared with the single-gather prestack Q estimation methods (SPQEM), we make the most of the spatial coherence between the adjacent CMP to eliminate the unexpected noise-related outliers during spectral division for improved robustness and accuracy. Numerical examples are used to validate the superior performance of the proposed method, even in the presence of strong ambient noise.

Solutions to the amplitude variation with offset (AVO) inverse problem are inherently nonunique. Thus, estimating the range of potential solutions is crucial. For this reason, uncertainty inversion is more appropriate than deterministic AVO inversion. However, most studies on deep learning (DL)-based inversion focus on deterministic prediction. In general, there are few studies on uncertainty inversion, mainly focused on poststack seismic data. In this study, we propose a DL-based AVO uncertainty inversion method based on an improved variational Bayesian neural network (VBNN) for predicting multiple elastic parameters. Moreover, to mitigate the issue of insufficient labeled data in inversion tasks, we combine the improved VBNN with a semi-supervised learning framework. Synthetic data experiments demonstrate that the proposed method exhibits higher accuracy and robustness to noisy seismic data than the well-known traditional Bayesian linearized inversion (BLI) method. The proposed method also has slightly higher inversion accuracy than the state-of-the-art improved-hybrid-seismic-prior-guided neural network (IHGNN). Moreover, the uncertainty estimation confirms that the proposed method can explore potential solutions to the AVO inverse problem more effectively and reasonably than the comparison methods. Furthermore, field data AVO inverse experiments verify that the proposed method can obtain reasonable predicted results and effectively reveal uncertainty in the inversion results.

Amplitude variation with offset (AVO) refers to the amplitude variation with offset. This relationship can be used to analyze lithology and identify the oil and gas reservoirs in seismic exploration. Traditional AVO inversion is a typical ill-posed problem. When deep learning is directly used for seismic inversion, there are three main issues. First, the label data are insufficient. Second, a network trained for one working area is not applicable to other working areas. Third, there are spatial discontinuities and instability problems in the inversion results. In this letter, we propose the AVO inversion method that combines transfer learning and low-frequency component constraints. Transfer learning strategy is introduced to solve two main problems: The label data are insufficient to train the network, and the trained network is not applicable to other regions. Taking the low-frequency component as the constraint term makes the solution easier to converge to the true value. The experimental results of a typical example show that our method not only effectively improves the prediction accuracy and spatial continuity of the inversion results, but also reduces dependence on logging data.

Low frequency information in seismic data can improve seismic resolution and imaging accuracy, enhance the quality of inversion, and play an essential role in imaging algorithms such as full-waveform inversion. Sufficiently low frequency data can avoid the cycle skipping phenomenon during full-waveform inversion. During seismic data processing, the protection and reconstruction for low frequency information are therefore of great importance. In this paper, we systematically investigate the extrapolation of pre-stack viscoacoustic seismic low frequency data using a dense convolutional network to effectively establish the nonlinear relationship between high and low frequency data, and realize the extrapolation and reconstruction of viscoacoustic 0-5 Hz low frequency data using 5-30 Hz high-frequency component. And the generalizability of the method for different influencing factors such as wavelets, noise, and models is analyzed using Marmousi2 velocity model forward data. It is demonstrated that the method has high robustness and can be applied to different situations, and the accuracy is higher than that of the traditional convolutional neural networks method. The feasibility of the low frequency extrapolation method based on dense convolutional network is also verified by synthetic data, physical experiment simulation data, and field data testing, and superior to the traditional convolutional neural networks method.