One of the leading challenges in hydrocarbon recovery is predicting fluid distribution throughout the reservoir, using dispersion of seismic elastic parameters to solve this problem is a method with great potential. Previous studies reveal that predicting frequency-dependent seismic elastic parameters is difficult because of their sensitivity to seismic wave amplitude. The frequency-dependent AVO inversion schemes are widely used to estimate the dispersion gradient attributes for fluid prediction. However, these methods strongly depend on the advanced spectral decomposition and the wavelet overprint effect in time-frequency information. For this reason, this study presents an innovative technique that combines prestack AVO inversion and linear Bayesian inversion algorithm to predict directly frequency-dependent P-wave velocity of multilayered medium from seismic reflection data, which can quantitatively describe the change of P-wave velocity in seismic frequency band. Furthermore, frequency-dependent elastic parameters were used to define a dispersion factor for fluid prediction in thin sandstone reservoirs. The novelty of the study is that the proposed approach introduces prestack AVO inversion to provide reliable initial model and constructs dispersive P-wave velocity inversion framework of layered medium for the first time. Additionally, the dispersive elastic parameters have more potential applications than the dispersion gradient attributes. Tests on the synthetic and real data demonstrate that the frequency-dependent P-wave velocity of multilayered medium can be estimated reasonably and stably. In this application, we use a test well to assess locally the performance of the technique.

Swell noise is a common issue in streamer seismic data. This type of noise can significantly obscure useful signals and degrade the quality of subsequent seismic data processing. Traditional filtering methods struggle to effectively suppress strong noise, while deep learning-based denoising approaches using conventional convolutional neural networks (CNNs) often suffer from signal leakage due to pooling-based downsampling. To address these issues, we propose a Haar discrete wavelet transform (HDWT) downsampling-based attention U-Net (WAUNet) to effectively suppress swell noise while mitigating signal leakage. Furthermore, to provide a high-quality training dataset, we construct a multi-noise-level augmented dataset by combining real swell noise, clean synthetic data, and processed field data. Experimental results demonstrate that, compared to the self-supervised denoising approach and Attention-UNet, the proposed method achieves superior denoising performance on both synthetic and field seismic data.

Stacking velocity characterized as the low wave-number velocity component plays a crucial role in high-precision seismic velocity modeling and imaging. In this letter, we present a new tool utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) from the field of deep reinforcement learning to obtain the stacking velocity field. MADDPG explores the energy clusters through continuous interaction with it, aiming to find the stacking velocity that maximizes the sum of stacking energy and optimizes the normal moveout gather. The method is a new way of unsupervised automatic velocity analysis by using these two criteria. We add physical constraints to the results of exploration to ensure that the picking results conform to low-frequency trends as much as possible and reduce the occurrence of reversal points. The implementation of this method closely resembles manual picking, thereby endowing the approach with a certain level of interpretability. Examples of synthetic and filed data confirm the effectiveness of the proposed method.

Seismic horizon interpretation is a fundamental task in subsurface exploration, essential for understanding formation geometry, characterizing reservoir properties, and supporting drilling operations. Traditional methods and deep learning (DL)-based approaches often face challenges in complex geological settings, leading to potential instability or failure in horizon picking. To overcome these challenges, we propose a novel method that integrates deep learning with multiple seismic attributes for robust horizon interpretation. Initially, a sparse horizon grid is constructed, guided by well log data and incorporating geological knowledge, such as structural and sedimentary system information. Using the structural similarity criterion, we select key seismic attributes that effectively distinguish horizon and non-horizon features, enabling neural networks to learn horizon patterns more accurately. The developed horizon picking network establishes a nonlinear mapping between the selected attributes and horizon classification results, improving interpretation accuracy. The proposed method is demonstrated on 3D seismic data from the Bohai Bay Basin, where it successfully provides high-density lateral interpretations of multiple horizons with varying levels of complexity. In addition to this, the method is applied to an ongoing drilling well. The prediction of the deep Archaean buried hill interface, 165 m ahead of the drill bit, showed a depth error of 2.64‰. This approach enhances both the accuracy and reliability of seismic horizon interpretation, making it a valuable tool for supporting safe and efficient drilling in complex geological formations.

Pre-stack seismic inversion is an effective approach to obtain elastic parameters for reservoir characterization in seismic exploration. However, the difficulty in achieving reliable inversion results in pre-stack seismic inversion remains due to the strong nonlinearity of the problem and the ambiguity of solutions. Deep learning (DL) excels at mapping the complex nonlinear relationship, and thus various DL-based methods have been used in seismic inversion. To address challenges posed by the nonlinearity and ambiguity in seismic inversion, a novel DL-based pre-stack seismic inversion constrained by amplitude versus offset (AVO) attributes is proposed. In this approach, the multitask learning strategy is adopted to construct a deep neural network that allows for the simultaneous processing of multiple related tasks through information shared between tasks. Moreover, the theoretical seismic forward modeling is integrated with the neural network training, enabling semisupervised learning and utilizing unlabeled data. Additionally, To mitigate the ambiguity of solutions, AVO attributes including intercept P and gradient G are introduced as constraints in the neural network training process. Experimental analyses show that the proposed method can obtain superior inversion results on both synthetic and real examples. Compared with other DL-based methods, the mean squared error of the proposed method’s inversion results on examples drops by at least 30%. Besides, the proposed method can effectively improve spatial continuity and preserve more details laterally in the field data example.

Seismic coherent noise is often found in post-stack seismic data, which contaminates the resolution and integrity of seismic images. It is difficult to remove the coherent noise since the features of coherent noise, e.g., frequency, are highly related to signals. Recently, deep learning has proven to be uniquely advantageous in image denoise problems. To enhance the quality of the post-stack seismic image, in this letter, we propose a novel deep-residual-learning-based neural network named DR-Unet to efficiently learn the features of seismic coherent noise. It includes an encoder branch and a decoder branch. Moreover, in order to collect enough training data, we propose a workflow that adds real seismic noise into synthetic seismic data to construct the training data. Experiments show that the proposed method can achieve good denoising results in both synthetic and field seismic data, even better than the traditional method.

Predicting gas-bearing zone of deep tight dolomite reservoirs from prestack seismic data is challenging and subject to great uncertainty. Machine learning especially for deep learning (DL) provides a new potential. One main limitation of the DL-based supervised methods is that they require large amounts of training data. However, well-log labels from the real deep reservoirs are very insufficient. To address this issue, we investigate a method based on convolutional neural networks (CNNs) considering transfer learning to predict gas distribution of deep tight dolomite reservoirs. The CNNs model we used contains three convolutional layers for automatic feature extraction from prestack data and one fully connected (FC) layer for gas-bearing probability prediction. A numerical model is designed based on petrophysical parameters extracted from the real target work area associated with deep tight dolomite reservoirs. The model is used to generate synthetic samples to pretrain the CNNs model. We then fix the network parameters in the first two convolutional layers and decay the learning rates of the third convolutional layer and the FC layer. Using the real samples to fine-tune the pretrained CNNs model with epoch increasing. The optimal predictor is finally trained well for gas-bearing prediction. The method is applied to a real work area of deep tight dolomite reservoir located in western China covering approximately 800 km2. Examples illustrate the roles of transfer learning on improving gas-bearing distribution of deep tight dolomite reservoirs and increasing the generalization of the method.

One of the main objectives in the hydrocarbon reservoir characterization is determining rock and fluid properties that rely extensively on inference from seismic observations. In this letter, we present a novel Bayesian prestack inversion method using frequency-dependent amplitude versus offset (AVO) analysis with the goal to directly estimate gas saturation and porosity of a target thin reservoir zone. The proposed methodology is based on an improved Markov chain Monte Carlo (MCMC) sampling algorithm, which is computationally very coefficient due to its satisfactory acceptance probability and the convergence speed of Markov chains. Using a nonlinear rock physics model (RPM), properly calibrated for the investigating area, and a seismic forward operator based on the frequency-domain propagator matrix approach in the Bayesian inversion framework, we then evaluate the full posterior probability distribution of petrophysical parameters conditioned to seismic data and available prior information, using the MCMC algorithm in which we iteratively sample within the petrophysical property space. The proposed inversion approach is validated through applications to a synthetic reservoir model and the real seismic data from gas-bearing reservoirs with strong velocity dispersion.

Seismic spectral decomposition applications are often restricted to the magnitude component of time–frequency spectra because it has been very challenging to make a meaningful interpretation of the phase information. In seismically thin layers, nevertheless, phase decomposition can be useful for the enhanced delineation of subsurface lateral variations, as seismic phase singularities are believed to tightly relate to the geologic features and geofluid effects. In this letter, we introduce an improved phase decomposition approach based on a high-resolution complex-spectra decomposition technique for better thin reservoir characterizations. The proposed method is applied to decompose seismic responses into the various phase components that sum to reconstruct the original traces. Via assuming the 0° phase seismic data, results from the synthetic and physical modeling examples imply that reflection anomalies associated with reservoir hydrocarbons can be magnified on the specific phase components. This magnification thus allows the reservoir geofluid variations to be better discriminated from certain lithologic influences that also substantially affect the total seismic reflection amplitudes, which are otherwise buried in the broadband responses.

Studies of frequency dependence of seismic data anomalies on partially gas-saturated reservoir have been performed for many years. Essentially, the frequency-dependent seismic signature represents a potential and largely untapped source of information for the detections of subsurface target properties. Through analyzing the anomalous feathers of amplitude variations with the angle of incidence and frequency (AVAF), both theoretically and algorithmically, it is possible to discriminate hydrocarbon from variations in other reservoir properties. For a layered structure model, however, it can be challenging to employ the conventional Zoeppritz equation-based method that may not accurately describe complex reflections considering the effects of both the layered structure of a reservoir and the attenuative and dispersive property of rocks. We introduce a novel hydrocarbon detection approach based on Bayesian inversion of frequency- and angle-dependent reflection signatures from a tight gas sandstone reservoir having strong attenuation and velocity dispersion. The proposed inversion scheme employs the propagator matrix method as a description of seismic responses for the stratified model and spectral decomposition technique to obtain multifrequency amplitude information. The synthetic test and real application show the proposed inversion approach has the potential to be useful in detections of hydrocarbon accumulation.

Predicting gas-bearing reservoirs in tight sandstone is significant but challenging. Although machine learning (ML), especially deep learning (DL) methods provide a potential for solving the issue, the major challenge of their application to gas-bearing prediction is how to generate accurate intelligent models with limited training sets. To relieve the notorious small-sample problem and the overfitting problem caused by limited well-log data, we propose the semi-supervised learning and transfer learning (SSL-TL) method for qualitative gas-bearing prediction. In the SSL-TL method, we first train the k nearest neighbor (kNN) classifier. And we choose the outputs with high confidence as the pseudo-training samples to extend the training sets of the convolutional neural networks (CNNs). Then, we pre-train the CNNs model with the pseudo-training samples, and subsequently introduce the transfer learning (TL) strategy to fine-tune the pre-trained CNNs model using the real training samples. Finally, we obtain a strong CNNs-based gas-bearing classifier. The TL strategy can make full use of the extended training sets while reducing the negative influence of the pseudo-training samples. We apply the SSL-TL method to a field data set with the limited wells. The test results show that the SSL-TL method has higher lateral continuity in gas prediction and agrees more with the known geological understanding in the studied field compared with the results of the CNNs models trained by other strategies.

Coherence attribute is an effective and widely used tool for identifying structural and stratigraphic anomalies in seismic data. By applying the coherence attribute on multiple bandwidth-, azimuth-, and offset-limited seismic data, multispectral, multiazimuth, and multioffset coherence attributes have also been proposed to enhance geologic discontinuities. Generally, geologic discontinuities, such as fault and paleochannel, usually have different dipping and meandering orientations and will be better identified along a perpendicular rather than a parallel direction in coherence computation. To address this issue, we propose a new multidirectional coherence attribute by combining an eigenstructure-based coherence algorithm with directional decomposition for discontinuity characterization. For 3-D seismic data, we compute eigenstructure-based coherence along multiple different directions to identify the seismic edges perpendicular to the chosen direction and set the minimum mean of multiple coherence images as the final multidirectional coherence attribute. Hence, the proposed multidirectional coherence attribute not only provides multiple coherence images highlighting partial edges in a certain direction but also offers a clearer coherence image highlighting whole edges in all directions than coherence without any decomposition. Furthermore, a colorful coherence image blending with several directional components intuitively contributes to interpret more geologic details. The coherence images of a 3-D physical modeling data and field data of carbonate reservoir prove the availability of the proposed method and demonstrate that multidirectional coherence attribute can serve as an improved tool to characterize the distribution of geologic discontinuity in seismic images.

Reliable estimate of the anelastic attenuation factor- Q from seismic records is highly desirable for improving seismic resolution. However, the conventional equivalent- Q or horizontal interval- Q estimation ignores that Q-distribution should hold the same ability for the subsurface structure characterization as seismic data. To pursue an accurate Q-model, we propose a technique for joint reflectivity and structural interval- Q estimation by using nonstationary sparse inversion. We designed a structural interval- Q model by dividing the seismic data into several structural layers with the interpreted horizon(s). Attenuations in each layer are close to each other and can be described by an equivalent- Q or gradient- Q. Based on the attenuation theory, the nonstationary sparse inversion is solved iteratively, where, at each iteration, the equivalent- Q of only one layer is optimized by searching for the corresponding optimum inverted reflectivity, leading to a structural interval- Q model. The main advantages of our method are its objectivity and accuracy because of the integration of the prior structural information from interpreted horizons into joint reflectivity-estimation and Q-estimation. The test of synthetic and field data clearly illustrates that the proposed method enables high-precision structural interval- Q estimation and sufficiently compensates for Q-related attenuation.

Noise attenuation has been a long-standing but still active topic in seismic data processing. The deep convolutional neural networks (CNNs) have been recently adopted to remove the learned random noise from noisy seismic data, but it is still difficult to improve the generalization ability of learned denoisers due to the limited diversity of training data sets. In this letter, we investigate an end-to-end deep denoising CNNs (DCNNs) with a novel data generation method involving multidimensional geological structure features for seismic denoising. To learn an optimized network denoiser, seismic amplitude data are extracted from 3-D synthetic seismic data along three directions (i.e., two spatial directions and one temporal direction) to prepare a training data set. Compared with using seismic data from only a certain single direction to generate all training samples, this strategy enables DCNNs to learn abundant geological structural information from three directions, and helps DCNNs have a better performance on noise reduction. Another 3-D synthetic seismic data and 3-D real land data examples with plentiful faults and fluvial channels are used to illustrate that the optimized network denoiser can be directly extended to attenuate random noise. The denoising results demonstrate that DCNNs learned from the multidimensional geological structures can accomplish the self-adaptive random noise attenuation, and meanwhile preserve spatial geological structures.

It is an effective way in seismic imaging to make full use of lateral seismic response to break through the limitation of vertical resolution and to improve the accuracy of interpretation. On zero-crossing-time (ZCT) amplitude slices, there is a clearer imprint of underground beds than on non-ZCT slices, providing an important foundation for characterizing interbedded thin beds. However, picking ZCTs is time-consuming with significant manual efforts. In the assumption of horizontally layered media with lateral invariance, we deduce the variation of the cluster number on ZCT and non-ZCT slices with the number of thin beds. Furthermore, based on the statistical analysis on all cluster numbers of a 3-D seismic data set, ZCT, and non-ZCT slices are distinguished according to the difference of the cluster number. As a result, all ZCTs are picked automatically. Considering the influence of noise, the method provides the estimated values and the estimated intervals for all ZCTs to improve reliability. No label is required with this unsupervised learning method. The feasibility and practicability of the proposed method have been verified with numerical and real data experiments.

Normal moveout (NMO) correction is a routine step in seismic data processing, which has an important impact on other seismic processing procedures, seismic inversion, and interpretation. We propose a goal-oriented inversion-based NMO correction method using a convex l2,1-norm. The proposed method corrects the superresolution multichannel offset-dependent reflectivity rather than the bandlimited data itself sample by sample, block by block, or wavelet by wavelet. Therefore, the proposed method can essentially reduce the amplitude and even phase distortion introduced by data-based NMO correction methods in the presence of strong wavelet interference. We impose two goal-oriented constraints including both the temporal sparsity and the horizontal continuity of reflectivity, which are approximately represented by a convex l2,1-norm, on the geometric moveout relationship from offset-dependent trajectories to zero offsets to build a new objective function for NMO correction. The goal-desired temporal sparsity of reflectivity can induce the superresolution solution; meanwhile, the goal-desired horizontal continuity introduces a reasonable intrinsic structure to further limit the solution space and is particularly suitable to processing interfering reflections. Attributing to these two additional constraints, the new NMO correction method can flatten the interfering events and the intersecting events with favorable offset-dependent amplitude and phase variations even in the presence of noise. Synthetic and real data examples are adopted to verify the performance of our method. The results show that goal-oriented inversion-based NMO correction using the l2,1-norm is a potentially effective, stable, and high-quality NMO correction tool, especially for strong wavelet interference and at far offsets.

The deep exploration has become the focus of attention in the field of earth sciences. Coherence is a routine measure to identify structural and stratigraphic anomalies, such as faults, channels, and fractures in subsurface. However, deep seismic data typically suffer from a low signal-to-noise ratio and a weak reflection amplitude, thus it may not provide a better insight for seismic attribute analysis. The phase information has the ability to detect subtle changes in subsurface but it is sensitive to noise, thereby masking some stratigraphic features in the full-bandwidth data. To address these two issues, we propose a multispectral phase-based geosteering coherence method by combining coherence and spectral decomposition for deep stratigraphic feature characterization. The proposed method can effectively select and utilize the phase components of favorable spectral bands, which can detect different scale geologic discontinuities and reduce or avoid the effect of random noise in deep seismic data. Furthermore, corendering the coherence images of three different frequency components using red-green-blue blending can detect more geologic details in subsurface. The examples including 3-D physical modeling data and real seismic data set of carbonate reservoir from western deep formation are employed to demonstrate the effectiveness of the proposed method. The coherence attributes obtained from the proposed method can detect the weak or hidden geologic details clearer than the geosteering coherence calculated from the broadband seismic data, and it may serve as a future tool for detecting the distribution of geologic abnormalities in deep exploration.

Full waveform inversion (FWI) plays a central role in the field of seismic processing due to the advantage in recovering the properties of the subsurface from seismograms. However, it suffers from more local minima convergence and more serious nonlinearity than the conventional seismic inversion method mainly caused by the lack of low-frequency information in the seismic data or the low-wavenumber components in the starting model. In this letter, we present a novel technique for seismic FWI based the angle difference identity for cosine, which builds an internal connection between high- and low-frequency signals. Thus, we can use the high-frequency information to get a plausible recovery of the low-wavenumber velocity. In addition, we choose the amplitude-independent objective function, which is given by maximizing the cross correlation between the modeled and observed data, to deal with the imperfect amplitude matching in the real case. Finally, a synthetic example of SEG/EAGE salt model is employed to demonstrate the validity of the proposed method, when low-frequency signals are absent.

Time-frequency (TF) analysis is a useful tool for seismic data processing and interpretation. We introduce sparse Bayesian learning (SBL) to TF analysis and propose a new SBL-based high-resolution TF method. The method decomposes the seismic trace into a series of Ricker wavelets using SBL-based sparse representations and subsequently implements Wigner-Ville distribution (WVD) on the decomposed wavelets to produce TF spectra. By iteratively solving a Bayesian maximum posterior and a type-II maximum likelihood, SBL-based decomposition can sequentially obtain an optimal number of Ricker wavelets with different peak frequencies or phases from a preset wavelet dictionary, and can simultaneously invert for the associated sparse TF pseudoreflectivity with the prediction uncertainty. The WVD of SBL-based decomposed wavelets can assemble TF distribution of the reconstructed signals to approximately characterize WVD of the original data. Therefore, the linear stack of WVD of all decomposed independent wavelets is immune from both the notorious cross-term interferences of the traditional WVD and random noise. Synthetic data example involving thin beds and laboratorial physical modeling data example involving several known multicave combinations are used to demonstrate the effectiveness of the proposed SBL-based TF analysis method and illustrate its advantages over WVD and the orthogonal matching pursuit-based TF analysis method. The 3-D real seismic data example is adopted to test its application potential for interpreting deep channels and the karst slope fracture zone. The results show that the proposed SBL-based TF method is a potentially effective, stable and high-resolution seismic TF analysis tool even in the presence of thin beds.

Previous studies have demonstrated that P-wave velocity dispersion at seismic frequencies is often related to hydrocarbons, which results in frequency-dependent P-wave reflection coefficients. This effect is neglected in the conventional amplitude-versus-angle (AVA) inversion, or reduced in most AVA inversion involving seismic dispersion due to the linearization of either the forward modeling or the inversion objective function. As a consequence, there are times when nonnegligible error exists in the inverted dispersion-associated result, which is probably nonnegligible in some cases. In this letter, we adopt the propagator matrix forward modeling derived from the wave equation and the particle swarm optimization (PSO)-based inversion to solve the uncollapsed objective function to avoid any linearization operator at the cost of probably more computational complexity and inversion ill-posedness. To address the ill-posedness, in both the forward modeling and inversion, we introduce rock-physics constraints of frequency-independent S-wave velocity and limited variations of P-wave velocity with frequency. Furthermore, we statistically derive the average frequency-dependent P-wave velocity attribute, the P-wave velocity dispersion intensity attribute, and the characteristic frequency attribute corresponding to the maximum velocity dispersion gradient from multiple experimental inverted dispersive P-wave velocity results. These attributes can be directly applied to detect hydrocarbons. A synthetic data example and a real data example through a drilling well are used to demonstrate that PSO-based prestack stochastic inversion method with rock-physics constraints is effective, and that the statistical attributes derived from the multiple inverted dispersive P-wave velocities can be utilized to favorably indicate gas reservoirs.

Traditional 1-D instantaneous phase (IP) is a routine attribute for detecting structural discontinuities of seismic images. The phase attribute has the ability to detect subtle changes, but it is meanwhile sensitive to noise. Furthermore, the traditional IP attribute is calculated trace by trace and thus cannot effectively utilize geological constraints. The sensitivity of noise and unavailability of geological constraints limit the practical applications of IP attributes. To address these two issues, this letter proposes a 3-D geosteering phase attribute derived from IP. At first, we implement the local stack on IP traces along both the time direction and the trajectory direction of the events to construct new stacked phase traces. Then, we compute the covariance of neighboring stacked phase traces along different spatial directions and extract the directional phase information from the resulting complex-valued covariance. Finally, we derive the so-called geosteering phase attributes by taking the maximal value among the extracted directional phases to approximately characterize the discontinuity measurement perpendicular to the structural trend in a 3-D curved plane. The examples including 3-D synthetic, physical modeling, and real seismic images are adopted to demonstrate the effectiveness of the proposed geosteering phase attributes. The results illustrate that the new geosteering phase attributes can be used as an effective and robust tool for the automatic detection of faults, channels, and even large-scale fracture groups.

Fracture monitoring is crucial for many geo-industrial applications, such as carbon dioxide storage and hydrocarbon exploration in tight reservoirs, because fractures can form storage space or leaking paths for geological sealing. We propose a fracture identification framework for geo-industrial applications by exploiting seismic reflection anisotropy and automatic multisensitive attribute fusion. Anisotropy maps extracted from different seismic attributes are automatically selected and fused according to the correlation between the predicted anisotropy strengths and the measured fracture densities at well locations. Through seismic anisotropy extraction and automatic multisensitive attribute fusion, we can acquire a more comprehensive evaluation of different fracture types in a reservoir. The proposed fracture identification framework is successfully applied to a deep, tight sandstone reservoir in Southwest China. The predicted fracture distribution is closely related to the local structures in the target reservoir. The orientations of the most predicted fractures are consistent with the local maximum principal stress direction in this area, which is good for the opening and fluid filling of fractures. The fracture identification results will be used to guide hydrocarbon exploration activities in this region, such as exploration well deployment.

Regardless of successful applications of the convolutional neural networks (CNNs) in different fields, its application to seismic waveform classification and first-break (FB) picking has not been explored yet. This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave. We use representative subimage samples with two types of labeled waveform classification to supervise CNNs training. The goal is to obtain the optimal weights and biases in CNNs, which are solved by minimizing the error between predicted and target label classification. The trained CNNs can be utilized to automatically extract a set of time-space attributes or features from any subimage in shot gathers. These attributes are subsequently inputted to the trained fully connected layer of CNNs to output two values between 0 and 1. Based on the two-element outputs, a discriminant score function is defined to provide a single indication for classifying input waveforms. The FB is then located from the calculated score maps by sequentially using a threshold, the first local minimum rule of every trace and a median filter. Finally, we adopt synthetic and real shot data examples to demonstrate the effectiveness of CNNs-based waveform classification and FB picking. The results illustrate that CNN is an efficient automatic data-driven classifier and picker.

Seismic data are increasingly required to be high quality for the continuous improvement of the degree of exploration. From the viewpoint of inversion, the utilization of more information is an effective way to improve the signal-to-noise ratio of seismic data. In this letter, we adopt simultaneous sparsity constraints of the first-order differences of signals along the time direction and two spatial directions, described by minimizing the Cauchy function, as a combined constraint (or regularization) term imposed on the time-domain data misfit to propose an inversion-based 3-D seismic denoising method. In this way, the redundancies among time slices and seismic sections along two spatial directions are simultaneously considered, and the edges along the spatial directions can be preserved. Through analyzing the first-order derivative of the sum of the data misfit term and the designed combined regularization term (or the objective function), we derive that the relationship between data and desired signal samples in the range of the first-order neighborhood can be expressed as a linear system with seven data-dependent coefficients. Furthermore, it can be inferred that the sparsity constraints of signal differences along different dimensional directions of 3-D data have some complementary functions of noise reduction and signal preservation. We use a 3-D synthetic data set, a 3-D real poststack data set, and a 3-D real prestack data set to determine that the proposed method is an effective amplitude-preservation denoising tool with an acceptable computational cost.

Attenuating random noise is a fundamental yet necessary step for subsequent seismic image processing and interpretation. We introduce a sparse Bayesian learning (SBL)-based seismic denoise method by using the physical wavelet as the basis function. The physical wavelet estimated from seismic and well logging data can appropriately describe the characteristics of the seismic data. Thus, it is an appropriate choice of basis function. Moreover, the tradeoff regularization parameter for determining denoise quality can be adaptively estimated according to the updated data misfit and sparseness degree during the iterative process of the SBL algorithm. The motivation behind the denoise method using sparse representations is that seismic signals can be sparsely represented by using several physical wavelets, whereas noise cannot. Both synthetic and real seismic data examples are adopted to demonstrate the effectiveness of the method.

In seismic exploration, the wavelet-filtering effect and Q-filtering (amplitude attenuation and velocity dispersion) effect blur the reflection image of subsurface layers. Therefore, both wavelet- and Q-filtering effects should be reduced to retrieve a high-quality subsurface image, which is significant for fine reservoir interpretation. We derive a nonlinear time-variant convolution model to sparsely represent nonstationary seismograms in time domain involving these two effects and present a time-variant deconvolution (TVD) method based on sparse Bayesian learning (SBL) to solve the model to obtain a high-quality reflectivity image. The SBL-based TVD essentially obtains an optimum posterior mean of the reflectivity image, which is regarded as the inverted reflectivity result, by iteratively solving a Bayesian maximum posterior and a type-II maximum likelihood. Because a hierarchical Gaussian prior for reflectivity controlled by model-dependent hyper-parameters is adopted to approximately represent the fact that reflectivity is sparse, SBL-based TVD can retrieve a sparse reflectivity image through the principled sequential addition and deletion of Q-dependent time-variant wavelets. In general, strong reflectors are acquired relatively earlier, whereas weak reflectors and deep reflectors are imaged later. The method has the capacity to avoid false artifacts represented by sequential positive or negative reflectivity spikes with short two-way travel time, which typically occur within stationary deconvolution outcomes. Synthetic, laboratorial, and field data examples are used to demonstrate the effectiveness of the method and illustrate its advantages over SBL-based stationary deconvolution and TVD using an l2-norm or an l1-norm regularization. The results show that SBL-based TVD is a potentially effective, stable, and high-quality imaging tool.