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High-resolution exploration is hampered by seismic attenuation, caused by the viscosity and heterogeneity of underground media. Conventional single-channel attenuation compensation methods can increase the corresponding vertical resolution partially. However, the lateral continuity of compensated seismic data is always ignored and the noise resistance can be improved. Therefore, multichannel attenuation compensation methods were proposed, including the algorithm with sparse curvelet coefficient constraint of the time-space (t–x) data. However, nonstationary seismic data may be affected by irregular missing traces in field cases, which severely degrades its lateral continuity and negatively affects the performance of multichannel attenuation compensation. Therefore, we concentrate on simultaneous multichannel attenuation compensation and missing trace interpolation in a unified framework. Based on the inversion framework of sparsity promotion, we propose an approach for simultaneous multichannel compensation and interpolation, utilizing sparse curvelet coefficient constraint of the recovered principal frequency-wavenumber (f–k) spectrum. The size of the principal f–k spectrum is reduced by at least half compared with that of the corresponding t–x band-limited data. It significantly reduces the computational expense of curvelet transform-based processing. Synthetic and field data experiments validate the effectiveness of the proposed method in efficiency improvement with consistent performance when compared to the multichannel method with sparse curvelet coefficient constraint of the t–x data in improving the vertical resolution and lateral continuity. Furthermore, we have discussed a potential acceleration strategy based on random sampling in the normal compensation issue. [ABSTRACT FROM AUTHOR]

When compared to traditional seismic data acquisition, irregular blended acquisition significantly promotes the acquisition efficiency. Yet, the blending noise of subsampled blended data introduces new obstacles for the subsequent processing of seismic data. Due to the predictability of linear events in the frequency–space domain, the constructed Hankel matrices exhibit low‐rank characteristics. However, the blending noise of subsampled blended data increases the rank, so deblending and interpolation can be implemented via rank‐reduction algorithms such as the singular spectrum analysis. The significant computing cost of the singular value decomposition, however, makes the traditional singular spectrum analysis inefficient. An alternative algorithm, known as the randomized singular spectrum analysis, employs the randomized singular value decomposition instead of the traditional singular value decomposition for rank‐reduction. The randomized singular spectrum analysis significantly enhances the efficiency of the decomposition process, particularly when dealing with large Hankel matrices. There still remains some random noise when using the singular spectrum analysis or randomized singular spectrum analysis for subsampled blended data, because the noise subspace and signal subspace are coupled together. Thus, we incorporate a damping operator into the randomized singular value decomposition and propose a novel damped randomized singular spectrum analysis method. The damped randomized singular spectrum analysis combines the advantages of the randomized singular value decomposition and the damping operator to enhance the computational efficiency and suppress the remaining noise. Moreover, an iterative projected gradient descent strategy is introduced to achieve deblended and interpolated seismic data for subsequent processing. Examples from synthetic data and field data are used to demonstrate the effectiveness and superiority of the proposed damped randomized singular spectrum analysis method, which enhances the accuracy and efficiency during simultaneous deblending and interpolation. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to subsurface viscosity and heterogeneity, the vertical resolution of observed seismic data is decreased after wave propagation, generating nonstationary seismic data with amplitude attenuation and phase distortion. Inverse Q filtering techniques are always used to enhance the vertical resolution of seismic data. However, the majority of inverse Q filtering methods treat attenuation compensation trace by trace, which may produce non‐robust compensation results with poor transverse continuity and amplify noise energy in noisy cases. Thus, we develop a novel sparsity‐promoting inversion‐based multichannel seismic data attenuation compensation approach by introducing a sparse constraint for curvelet coefficients of multichannel compensated data, which takes the transverse continuity of compensated data into account. Besides, the proposed method with a sparse constraint for curvelet coefficients has a better noise‐resistance property, which can attenuate the noise energy in noisy cases during attenuation compensation, improving compensation accuracy and robustness. To improve its computational efficiency, a fast iterative shrinkage–thresholding algorithm is adopted to solve the established lasso problem. Synthetic data examples with different noise levels and two post‐stack field data examples validate the effectiveness of the proposed multichannel method. Its compensation results have superior vertical resolution, transverse continuity and noise robustness in comparison to the conventional single‐channel compensation method using a damped least squares algorithm. [ABSTRACT FROM AUTHOR]

In seismic data acquisition, because of several factors, such as surface barriers, receiver failure, noise contamination and budget control, seismic records often exhibit irregular sampling in the space domain. As corrupted seismic records have a negative effect on seismic migration, inversion and interpretation, seismic trace interpolation is a key step in seismic data pre‐processing. In this paper, we propose a high‐efficiency and high‐precision seismic trace interpolation method for irregularly spatially sampled data by combining an extreme gradient boosting decision tree and principal component analysis in a semi‐supervised learning method. The adjacent trace number, sampling number and amplitudes of the effective seismic data were taken as features to build the training data set for the extreme gradient boosting decision tree. Principal component analysis is applied to remove redundant information and accelerate the training speed. This is different from the traditional trace interpolation method in that the proposed method is data‐driven; therefore, it does not require any assumptions. Compared with other deep learning‐based trace interpolation methods, the proposed method has fewer control parameters and learning labels and a smaller training cost. Experiments using synthetic and field data demonstrated the validity and flexibility of this trace interpolation method. [ABSTRACT FROM AUTHOR]

Traditional inverse Q filtering methods for post-stack seismic data attenuation compensation (AC) have the drawback of instability or under-compensation. Besides, the quality factor (Q) should be known as a prerequisite, which is commonly estimated using the attribute difference between the reference and observed wavelets of pre-stack vertical seismic profile (VSP) data. The alternating iterative AC and Q estimation method is also researched for post-stack data, while the instability or huge computation becomes a defect. In this letter, we propose a simultaneous AC and Q estimation method for nonstationary post-stack seismic data based on semi-supervised learning. Specifically, we choose the long short-term memory algorithm which is sensitive to time series and can characterize seismic signal nonlinearly with high accuracy. The proposed AC and Q estimation method employs the Q information from well-logs and the compensated high-resolution data for supervised learning, and uses nonstationary seismic data beyond wells for self-supervised learning, without the wavelet extraction procedure. The synthetic data analysis and field data applications prove the feasibility of the designed semi-supervised method in improving the vertical resolution and Q estimation. The field data impedance inversion after AC further demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

Deep learning has been used in seismic exploration to solve seismic inversion problems, however it requires sufficient and diverse training samples and labels to obtain satisfactory results. Insufficient training labels are a common problem since labels usually come from well‐logging data, which are limited and sparsely distributed. This can result in a trained network with poor generalizability. A novel complete synthetic dataset‐driven method utilizing convolutional neural network is presented for seismic amplitude‐versus‐offset attributes P and G inversion. Gaussian simulation sampling with physical constraints is used to generate a complete elastic parameter dataset by traversing the entire elastic parameter model space. By randomly combining elastic parameters from the full elastic parameters model space, sufficient synthetic pre‐stack amplitude‐versus‐offset gathers and attribute datasets are generated for training the convolutional neural network. Compared with the limited real data‐driven convolutional neural network, the complete synthetic dataset‐driven convolutional neural network has better generalizability. Broadband training labels improve the accuracy and resolution of the complete synthetic dataset‐driven convolutional neural network's inversion results beyond that of the conventional least‐square data‐fitting method. The complete synthetic dataset‐driven convolutional neural network is robust for processing noise‐contaminated seismic data, but if the frequency band of the labels for training the network is too wide and the signal‐to‐noise ratio of pre‐stack amplitude‐versus‐offset gathers is too low, the quality of the inversion results will reduce. The Marmousi II model and field data examples show that the novel complete synthetic dataset‐driven convolutional neural network can extract higher‐resolution amplitude‐versus‐offset attributes P and G. [ABSTRACT FROM AUTHOR]

During seismic data acquisition, receivers with finer trace intervals can be distributed to record seismic signals. The distance between neighbouring shots, on the other hand, can be significant, resulting in regularly missing shots in the common receiver gathers, which may cause significant spatial aliasing and reduce the precision of later seismic imaging. Traditional interpolation algorithms have several issues and limitations for seismic data with spatial aliasing due to prior assumptions and human–computer interactions. As a result, we present a new deep learning method that includes the generation of adaptive training data and the design of a consistent convolution kernel. Deep learning has the ability to characterize seismic data in a nonlinear manner that is ideal for accurate seismic interpolation. Because the common shot gathers and common receiver gathers have similar features due to the spatial reciprocity of Green's function of the wave equation if all shots have the same physical signatures, the common shot gathers with a refined trace interval are adaptively extracted as the training dataset to guarantee the interpolation performance in the common receiver gathers. With regularly subsampled data as input an improved U‐Net is created and trained to match the desired output, that is, the completed data. The trained network can be deployed to the test common receiver gathers to rebuild regularly missing shots. We develop a novel consistent convolution kernel to ensure high accuracy of missing shot reconstruction while accounting for differences between prestack unmigrated seismic data and images. Using numerically subsampled synthetic and field data, the effectiveness and validity of the developed consistent convolution kernel and the upgraded U‐Net with adaptive training data for missing shot interpolation are demonstrated. [ABSTRACT FROM AUTHOR]

Blended acquisition improves the efficiency of seismic data acquisition sharply, and deblending algorithms are still open to prepare separated data. Most deblending methods are suitable for on-the-grid blended data. However, blended data in field cases are always at off-the-grid samples which pose great challenges in providing accurate deblended results. A binning strategy can assign an off-the-grid sample at its nearest on-the-grid sample approximately with the amplitude and phase bias caused by the existing distance between them. However, the subsequent deblending accuracy is low, especially when the amplitude and phase biases are large. With true off-the-grid data constraints, we introduce a Kaiser window tapered $\mathrm{sin}c$ interpolator to link off-the-grid samples and on-the-grid samples during the procedure of compressive sensing-based functional construction. Full expressions of the interpolator and its adjoint operator are provided to generate an iterative thresholding algorithm for off-the-grid blended data deblending. Separated on-the-grid data can be obtained accurately in an iterative manner. The deblending performance of artificially off-the-grid blended data demonstrates the validity of the proposed method quantitatively no matter whether the amplitude and phase biases are large or small. Field examples of off-the-grid blended data further prove the effectiveness of the proposed method to provide accurate on-the-grid separated data. [ABSTRACT FROM AUTHOR]

Marine seismic data with towered streamers have played an important role in marine exploration. However, the distance between adjacent sources and the distance between adjacent receivers/channels are inconsistent (i.e., like regularly missing shots) and near-offset information is unrecorded, which can decrease the performances of surface-related multiple elimination (SRME) and seismic migration. Traditional algorithms to provide prestack seismic data with consistent trace interval and to recover near-offset data have some drawbacks, including low efficiency of computation and super-parameter selection by trial and error. Thus, we propose a novel self-supervised deep learning (DL) algorithm to reconstruct regularly missing shots and recover near-offset information with an improved U-net by combining U-net and residual learning of ResNet. Via the spatial reciprocity of Green’s function, common shot gathers (CSGs) have similar features as common receiver gathers (CRGs). The reconstruction performances of regularly missing shots in CRGs can be guaranteed by using the network that is trained and validated by adaptively extracted CSGs. To reconstruct near-offset information of CSGs, we first construct pseudo-seismic data with the dip approaching 0 at near-offset parts by a rotation-truncation strategy. Pseudo-seismic data can be regarded as seismic data with approximate near-offset information to train and validate the designed network, which is later used to reconstruct near-offset information for CSGs. Finally, field marine seismic data with towered streamers is used to demonstrate the validity and effectiveness of the proposed self-supervised algorithm in reconstructing regularly missing shots and recovering near-offset information, which are beneficial for subsequent processing of seismic data. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) has the potential to recover high-resolution physical parameters of the Earth from seismic data. Considering the attenuation effects of the subsurface, we develop a new visco-acoustic FWI method in the time domain with a known Q model. The seismic wavefield is modelled by solving a decoupled fractional Laplacian visco-acoustic wave equation, which can better describe amplitude attenuation and phase distortion during wave propagation. To weaken the initial model dependence of visco-acoustic FWI, we introduce the optimal transport-based misfit functional which can measure the data residual globally. Numerical examples on the 2D Marmousi model demonstrate the superiority of the proposed method compared with the L2 norm-based method which measures the misfit of observed and synthetic seismic data locally, generating cycle-skipping issue. The inversion results of seismic data without low-frequency components further demonstrate the validity and effectiveness of the proposed method, which is insensitive towards low-frequency components, and may achieve reasonable inversion results for field data application. [ABSTRACT FROM AUTHOR]

Blended acquisition can improve the acquisition efficiency, and thereby reduce the acquisition costs. It becomes an important acquisition method in seismic exploration. However, the blending noise imposes challenges for subsequent traditional seismic data processing procedures, and thus deblending algorithms are necessary to obtain deblended seismic data. Based on the assumption that the signal is coherent and the blending noise is randomized in a specific domain, traditional deblending methods using the $L_{2}$ -norm measuring the data misfit can obtain separated gathers. However, the $L_{2}$ -norm is improper when the appearing seismic noise bias the normal distribution, that is, abnormal noise appears. To attenuate the abnormal noise effects during iterative deblending, we proposed an accurate deblending algorithm based on a robust sparse Radon transform (RSRT). For the RSRT, the alternating split Bregman (ASB) algorithm is used for robust 2-D Radon model updating with an $L_{1}$ -norm to measure data misfit in the mixed time–frequency domain and the sparsity constraint to the time-domain Radon model. Using the RSRT iteratively, the Radon model and the corresponding deblended data can be estimated robustly, accurately, and efficiently. Blended synthetic data with different levels of abnormal noise and with different trace intervals demonstrate the validity and flexibility of the proposed robust deblending method quantitatively with a high recovered SNR. Numerically blended field data further prove the effectiveness of the proposed method in attenuating the abnormal and blending noise. [ABSTRACT FROM AUTHOR]

Prestack amplitude variation with offset (AVO) inversion can provide abundant reservoir information underground, which is always implemented trace-by-trace. However, it cannot guarantee the lateral accuracy of the inversion results. To utilize the lateral difference of the angle gathers and improve the lateral resolution, the difference angle gathers are introduced. Based on the Bayes inversion framework, the objective function considering the difference angle gathers is first constructed. Then, the effect of difference angle gathers on inversion results is analyzed, which is essential to improve the accuracy of the inversion results. To further figure out the applicable conditions of difference angle gathers, different forward operators are analyzed including a nonlinear operator and a linear operator. The used nonlinear operator is the exact Zoeppritz’s equation. The linear operator is a linear perturbation equation based on the elastic inverse-scattering theory. Due to the difference of angle gathers in adjacent traces, the linear forward operator may cause a deviation of the updated parameters. By comparison, the exact Zoeppritz’s equation as the nonlinear forward operator has better applicability and precision. Based on the proposed method, the elastic parameters are obtained from seismic data. Numerical examples show that the inverted elastic parameters of the proposed method have a higher horizontal resolution, and the details in the inversion profile can be better highlighted. [ABSTRACT FROM AUTHOR]

The blended acquisition allows multiple sources to be simulated simultaneously in a narrow time interval, which can improve the acquisition efficiency and reduce the acquisition cost tremendously. However, the overlapped information from multiple sources poses challenges for traditional seismic data migration or inversion algorithms. Thus, accurate and efficient deblending should be implemented as a pre-requisite. Traditional inversion-based deblending algorithms can provide deblended data with a high computational burden, especially for a large volume of seismic data. As a deep learning strategy can match seismic data accurately in a nonlinear way through supervised learning, we propose a U-net-based accurate deblending algorithm, which incorporates transfer learning and an iterative strategy. A set of labeled synthetic data with a blending fold of 2 are classified into the training and validation data for U-net training and validation. Field data are regarded as the test data to assess the performance of the trained U-net. To guarantee the deblending performance of the field data to some extent, parts of field data with labels are used to fine-tune the trained U-net based on transfer learning. The fine-tuning procedure is relatively fast within several minutes. To further improve the deblending performance, we incorporate an iterative strategy with the fine-tuned U-net. The deblending performance is promising in the quality and computational efficiency compared with the curvelet-thresholding-based deblending method, which demonstrates the validity of the proposed intelligent deblending method. [ABSTRACT FROM AUTHOR]

The time–frequency analysis is very useful for attenuation compensation, quality factor Q estimation, anomaly detection, and so on. Among plenty of time–frequency analysis methods, the S transform (ST) and its extensions are widely used because of their self-adjustable flexibility, compared with the short-time Fourier transform and Gabor transform. However, the traditional ST has a poor amplitude-preserving property near the boundary while being implemented in the time domain, because the partition of unity cannot be guaranteed. Besides, the frequency distribution biases the actual Fourier spectrum because of the linear-frequency-dependent term in the analytical window, which can decrease the accuracy of attenuation estimation. To preserve the amplitude and frequency, a new analytical window is designed, and the corresponding comprehensive window is derived in the time domain. The frequency-domain formulae are derived in detail for an efficient implementation, in which the time-domain convolution is achieved through multiplication. Numerical examples on the synthetic layered model and pseudorandom time series demonstrate the validity of the proposed method in amplitude- and frequency-preserving quantitatively. Examples at a well location of field data further demonstrate its frequency-preserving property qualitatively. Furthermore, the proposed method can have wide applications in exploration geophysics, seismology, or signal analysis fields, combining with the synchrosqueezing transform. [ABSTRACT FROM AUTHOR]

External source interference noise (ESIN) is a common kind of noise in marine seismic data acquisition. According to the noise-to-signal ratio (NSR), a shot gather can be divided into a low NSR part and a high NSR part. The existing ESIN attenuation methods work well in high NSR parts of shot gathers. However, because the signals in low NSR parts are much stronger than ESINs, these methods cannot suppress the ESINs in low NSR parts, and they usually damage the signals. In this letter, we propose a deep-learning method to suppress the ESINs in low NSR parts based on a convolutional neural network (CNN). The end-to-end fully convolutional network needs labeled training samples; however, the real data are unlabeled, i.e., the ESINs in low NSR parts are unknown. To obtain the labeled training data, we propose a sample generation method based on real data. The ESINs in high NSR parts extracted by the traditional methods and the signals of the clean shot gathers are added together to synthesize training samples. We then use the synthesized data and its ESINs to train the network. The experiments prove that the proposed method can suppress the ESINs in low NSR parts properly and protect the signals as well. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) is a powerful tool to understand the real complicated earth model. As FWI is a highly nonlinear problem and depends strongly on the initial model, how to effectively retrieve the large-scale background model is critical for the success of FWI. For elastic FWI (EFWI), the inversion challenge increases because the P-wave and S-wave are coupled together if no mode separation technologies are applied. In this article, we develop a new EFWI strategy, where we simultaneously implement the angle decomposition and mode separation for the wavefield. Based on the analysis of radiation patterns of different parameters and the fact that small scattering angles correspond to large-scale model perturbations, we can retrieve the large-scale background model of the P-wave velocity with pure small scattering angle P-P mode wavefield. On the other hand, the pure small scattering angle S-S, S-P, and P-S mode wavefields are used to estimate the large-scale background model of the S-wave velocity. The correctly retrieved large-scale background models further guarantee the success of subsequent fine structure retrieving for the P- and S-wave velocity models by using different wave modes. The proposed method is able to reduce the cycle-skipping problem and the multiparameter crosstalk problem simultaneously. Numerical examples show that the proposed method provides much improved inversion results than the conventional EFWI, which demonstrates the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The trace interval in the common shot and receiver gathers is always inconsistent. The inconsistency affects the final performance of seismic data processing, and the reconstruction methods can enhance the consistency. Unfortunately, most interpolation algorithms are suitable in randomly missing cases, and the difficulty increases sharply in regularly missing cases, especially with big gaps. As deep learning (DL) has a strong self-learning ability in nonlinear characterizations to avoid linear events, sparsity, and low rank assumptions, we introduce DL into missing shots’ reconstruction. The spatial reciprocity of Green’s function is used to provide reasonable training data sets. First, the residual learning networks (ResNets) and the interpolation issue are briefly illustrated. Then, the spatial reciprocity is reviewed and illustrated qualitatively using the common shot and receiver gathers. The similar features in the common shot and receiver gathers guarantee the reasonability to regard the common shot gathers as the training sets and to regard the common receiver gathers as the test sets. The common shot gathers are divided into the training sets to train ResNets and the validation sets to verify the performance of the trained ResNets. Finally, the trained ResNets are used to reconstruct missing shots intelligently in the common receiver gather. Three different data sets are used to prove the validity of the proposed strategy. After reconstruction, the events are more continuous with less serrations and serious frequency wavenumber (FK) aliasing is attenuated effectively. The reconstructed data with a better consistency can improve the accuracy of migration and the final reservoir characterization. [ABSTRACT FROM AUTHOR]

The blended acquisition can help improve the seismic data quality or enhance the acquisition efficiency. However, the blended seismic data should first be separated for subsequent traditional seismic data processing steps. The signal is coherent in the common receiver domain, and the blending noise shows randomness when the blending operator is constructed using a random time delay series. The seismic data can be characterized sparsely by the curvelet transform which can be used for deblending. However, it has a high computational cost, especially for large-volume seismic data. The spectrum of the seismic data is band-limited with the conjugate symmetry property, and thus the principal frequency components can characterize the signal accurately. The size of the principal frequency components is at least halved. Thus, we propose to implement the curvelet transform on the principal frequency wavenumber (PFK) domain data instead of the time-space (TX) domain data. The size of the PFK domain data is at least halved compared with the TX domain data, which can improve the deblending efficiency reasonably. The related formulae are fully derived and the efficiency enhancement analysis is provided in detail. One synthetic and two field artificially blended data are provided to demonstrate the validity and flexibility of the proposed method in the efficiency improvement and the deblending performance. The separated gathers can be beneficial for subsequent traditional seismic data processing procedures. [ABSTRACT FROM AUTHOR]

Fluid discrimination is an extremely important part of seismic data interpretation. It plays an important role in the refined description of hydrocarbon-bearing reservoirs. The conventional AVO inversion based on Zoeppritz's equation shows potential in lithology prediction and fluid discrimination; however, the dispersion and attenuation induced by pore fluid are not fully considered. The relationship between dispersion terms in different frequency-dependent AVO equations has not yet been discussed. Following the arguments of Chapman, the influence of pore fluid on elastic parameters is analyzed in detail. We find that the dispersion and attenuation of Russell fluid factor, Lamé parameter, and bulk modulus are more pronounced than those of P-wave modulus. The Russell fluid factor is most prominent among them. Based on frequency-dependent AVO inversion, the uniform expression of different dispersion terms of these parameters is derived. Then, incorporating the P-wave difference with the dispersion terms, we obtain new P-wave difference dispersion factors which can identify the gas-bearing reservoir location better compared with the dispersion terms. Field data application also shows that the dispersion term of Russell fluid factor is optimal in identifying fluid. However, the dispersion term of Russell fluid factor could be unsatisfactory, if the value of the weighting parameter associated with dry rock is improper. Then, this parameter is studied to propose a reasonable setting range. The results given by this paper are helpful for the fluid discrimination in hydrocarbon-bearing rocks. [ABSTRACT FROM AUTHOR]

The Radon transform (RT) has been widely used as a powerful tool, especially in exploration geophysics fields, such as multiple removal, interpolation, and velocity analysis. However, the existing strong outlier effects can seriously decrease the accuracy of the traditional RT. Therefore, a robust time-invariant RT (TIRT) is proposed in the mixed time–frequency domain to attenuate the outlier effects by using double L1-norm sparse constraints performed on the data misfit and the Radon model in the time domain. For the TIRT, the forward RT and its adjoint can be implemented in the frequency domain efficiently. Only one matrix inversion for each frequency component is involved in all iterations to speed up the iterations. Then, the 1-D alternating split Bregman (ASB) algorithm is introduced and improved for 2-D Radon model updating efficiently. It involves matrix-vector multiplication operators and two proximity operators. These two proximity operators can guarantee the robustness and sparseness of the proposed method. Numerical examples of synthetic and field data demonstrate the effectiveness and validity of the proposed method. The proposed method is also used for interpolation to decrease the trace interval. After interpolation, seismic data are more continuous with less serrations along the spatial direction and the frequency-wavenumber spectrum is more focused. The interpolated data have wider potential applications in improving the accuracy of the following seismic processing. It should be noted that the proposed robust and efficient RT can also be used in remote sensing and computerized tomography fields instead of the traditional RT. [ABSTRACT FROM AUTHOR]

The external source interference noise (ESIN) in modern marine seismic data acquisition can seriously contaminate the reflection signal, which affects accurate reservoir characterization and description. An automatic ESINdetection and attenuation algorithm is proposed, which includes automatic ESIN detection, trajectory recognition and extraction. The ESIN detection aims to identify whether the shot gather is contaminated or not during the dynamic marine acquisition by using the average filtering and logistic operators. After obtaining the ESIN apex estimation and the optimal distance automatically, its trajectory can be recognized based on the derived time-invariant hyperbolic travel-time equation. For the recognized trajectories, a sparse time-invariant hyperbolic Radon transform (TIHRT) is designed in the mixed time-frequency domain to extract the ESIN by using the classification technique in the TIHRT domain. For implementing the TIHRT accurately and efficiently, an iterative 2D model shrinkage algorithm is introduced for the accurate and robust ESIN extraction and attenuation. Synthetic and field marine data examples are provided to demonstrate the promising performance of the proposed automatic ESIN detection and attenuation method. The proposed method can attenuate the detected ESIN effectively and preserve the reflection signals. [ABSTRACT FROM AUTHOR]

Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seismic interpretation results, we take advantage of the information of unlabeled data by unsupervised feature extraction and semi-supervised classification methods. The sparse autoencoder (SAE) is an unsupervised learning method that can extract the features of data without labels, and the transductive support vector machine (TSVM) is a semi-supervised method that trains a classifier according to both labeled and unlabeled data. In this paper, we propose a semi-supervised learning framework that combines SAE and TSVM to detect gas chimneys. In this framework, SAE is used to extract features from data and TSVM is used to classify the labeled and unlabeled features. Therefore, the unlabeled data is taken advantage of in both unsupervised feature extraction and semi-supervised classification to improve accuracy. In order to improve the precision of detection, the attributes of neighborhood regions are also utilized. Due to the information learned from plenty of unlabeled data, the proposed framework performs well. Numerical experiments are carried out on sample sets and field data. The proposed framework has higher testing accuracy than the traditional MLP method, especially when the labeled training set is small. In field data experiments, the proposed framework also gets good prediction results for gas chimney locations. [ABSTRACT FROM AUTHOR]

Because of the environment limitations, irregularity appears in the observed seismic data. In addition, the observed seismic data contains random noise from the acquisition equipment and surrounding environment, which affects the performances of multi-channel techniques, such as surface related multiple elimination (SRME) and amplitude variation with offset (AVO) analysis. The projection onto convex sets (POCS) method, known as an efficient interpolation method, is suitable for high signal-to-noise ratio (SNR) situations; however, the existing random noise may affect its final performance. In our previously published paper, the POCS formula was deduced in the view of iterative hard thresholding (IHT) method using a projection operator. In this paper, more physical illustrations about its detailed deduction are provided to show the differences between IHT and POCS in noise-free and noisy situations with easy understanding for readers. Then, performances of the POCS and IHT methods are compared in both noise-free and noisy situations, in terms of seismograms, frequency wavenumber (FK) spectra and single traces. For noise-free data, both the POCS and IHT methods can achieve good interpolation results. For noisy data, the POCS method is unsuitable because of the observed noisy data insertion, while the IHT performance is satisfactory because it uses a thresholding operator to eliminate random noise. Numerical examples on noise-free datasets demonstrate the validities of the POCS and IHT methods for interpolation. Tests on noisy data contaminated with additive white Gaussian noise prove the ability and superiority of the IHT method with anti-noise property compared with the POCS method. The projection onto convex sets (POCS) method is deduced using the iterative hard thresholding (IHT) algorithm and a projection operator with more detailed physical illustrations. The interpolation performances on noise-free and noisy data are explained in detail and the reasons behind these performances are fully discussed, which provide clues to further improve interpolation accuracy. [ABSTRACT FROM AUTHOR]

Deghosting is a critical step to improve the resolution of marine seismic data. For the frequency wavenumber (f-k) spectra of the original seismic data with ghosts, there always exist some low-SNR frequency components around the centers of the frequency notch bands, which are caused by the ghosts. In general, the traditional inverse filtering-based deghosting methods lack the ability to recover these low-SNR frequency components without amplifying the additive noise. In this letter, we propose a postprocessing method to enhance the deghosted seismic data by reconstructing these low-SNR frequency components. The proposed method includes two key steps. First, the low-SNR frequency components of the deghosted seismic data in the f-k domain are located adaptively by using both the original seismic data and its deghosted result. Second, the projection onto convex sets algorithm is introduced to reconstruct these low-SNR frequency components located in the previous step. To illustrate this, we use the proposed method to improve the deghosted results obtained by the non-Gaussianity maximization based f-k deghosting method. Applications on synthetic and real field marine data sets demonstrate the validity of the proposed method which can enhance the deghosted results significantly. [ABSTRACT FROM AUTHOR]

Random noise in seismic data can affect the performance of reservoir characterization and interpretation, which makes denoising become an essential procedure. This letter focuses on suppressing random noise in poststack seismic data while preserving the edges of desired signals. Due to the lateral continuity of seismic data, polynomial fitting (PF) method can be a good alternative in attenuating random noise. However, discontinuities exist widely in poststack seismic data, which might be damaged by the PF filter. By contrast, principle component analysis (PCA)-based filters have better performance in edge preserving, but there appear artifacts in the denoised results using the PCA-based filters. Thus, we propose an edge-preserving polynomial PCA filter which combines advantages of the PF and PCA methods by optimizing a PCA problem with a weighted polynomial constraint. The weight coefficient is determined adaptively according to the signal-to-noise ratio estimation and the energy proportion in the selected analysis window, which can help distinguish the horizontal continuous events and the edges effectively. To deal with the complicated slopes which make the local linear hypothesis invalid, we introduce a robust local slope estimation method and apply the slope estimation-based event tracing strategy to horizontally align the data set. Synthetic and field data examples show that the proposed method has a better performance in noise attenuation and edge preserving, compared with the edge-preserving PF method. In addition, the denoised results are free from artifacts. [ABSTRACT FROM PUBLISHER]

Sampling irregularity in observed seismic data may cause a significant complexity increase in subsequent processing. Seismic data interpolation helps in removing this sampling irregularity, for which purpose complex-valued curvelet transform is used, but it is time-consuming because of the huge size of observed data. In order to improve efficiency as well as keep interpolation accuracy, I first extract principal frequency components using forward Fourier transform. The size of the principal frequency-space domain data is at least halved compared with that of the original time-space domain data because the complex-valued components of the representation of a real-valued signal (i.e., a complex-valued signal with zero as its imaginary component) exhibit conjugate symmetry in the frequency domain. Then, the projection onto convex projection (POCS) method is used to interpolate frequency-space data based on complex-valued curvelet transform. Finally, interpolated seismic data in the time-space domain can be obtained using inverse Fourier transform. Synthetic data and field data examples show that the efficiency can be improved more than two times and the performance is slightly better in the frequency-space domain compared with the POCS method directly performed in the time-space domain, which demonstrates the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

The S-transform (ST), as a time–frequency analysis tool, has been widely used, but the amplitude preserving property is a little poor near the boundary of the selected discrete signal. The reason lies that the summation of the product between the analytical window and the comprehensive window over the sliding step deviates from unity near the boundary in the discrete cases. In order to hold the amplitude preserving property for the discrete signal recovery analysis, an amplitude preserving S-transform (APST) is proposed based on a novel analytical window selection. First, lots of numerical tests are used to analyze the shortcomings of the ST near the boundary for the selected discrete signal and demonstrate the effectiveness and the validity of the proposed APST using the novel analytical window. After that, the proposed APST is used for seismic data attenuation compensation, during which the attenuation function is estimated based on the minimum phase assumption using a statistical variable-step hyperbolic smoothing method. Numerical examples on synthetic and field data demonstrate the validity of the proposed method using the seismogram and time–frequency spectrum comparisons. Besides, the proposed APST can be easily extended into a generalized ST which is more flexible compared with the ST, and it can also be used in seismology, remote sensing, and other related discrete signal analysis fields. [ABSTRACT FROM PUBLISHER]

Due to the environment effects, economy restrictions, and acquisition equipment limitations, observed seismic data always have several traces missing and contain some random noise, affecting the performance of surface-related multiple elimination (SRME), wave-equation-based imaging, and inversion. Projection onto convex sets (POCS) is an effective interpolation algorithm, while the performance is unsatisfactory in noisy situations. Weighted POCS (WPOCS) method can weaken the random noise effects to some extent, but the performance is still unsatisfactory. Thus, an improved WPOCS (IWPOCS) method is proposed in this paper, for seismic data interpolation and denoising simultaneously based on Curvelet transform. First, the POCS formula is derived from the iterative hard threshold (IHT) view. Then, its shortcoming is analyzed because there is an implicit assumption that the observed seismic data should have a high signal-to-noise ratio (SNR). Finally, a novel method named IWPOCS is proposed based on WPOCS method, which can achieve simultaneous interpolation and denoising. Among the above three methods, the IWPOCS method is the most effective to interpolate and denoise seismic data in terms of recovered SNR and visual view. Numerical experiments on the synthetic data and the real seismic data from the marine acquisition with towed streamers confirm the validity of the proposed IWPOCS method. [ABSTRACT FROM PUBLISHER]

The process of angiogenesis is essential for tumor development and metastasis. Vascular endothelial growth factor (VEGF), which is overexpressed in most human cancers, has been demonstrated to be a major modulator of angiogenesis. Thus, inhibition of VEGF signaling has the potential for tumor anti-angiogenic therapy. Signal transducer and activator of transcription-3 (STAT3) is a key regulator for angiogenesis by directly binding to the VEGF promoter to upregulate its transcription. Several factors can enhance STAT3 activity to affect angiogenesis. Here, we found that overexpression of nuclear transcription factor-Y alpha (NF-YA) gene could promote cell invasion and angiogenesis accompanying the increase of STAT3 signaling in human melanoma cells. Moreover, the expression and secretion of VEGF was also found to be upregulated by the overexpression of NF-YA gene in melanoma cells. The STAT3 inhibitor was able to attenuate the upregulation of VEGF induced by NF-YA overexpression. Enhancer of zeste homolog 2 (EZH2), the catalytic subunit of the Polycomb repressive complex 2, enhances STAT3 activity by mediating its lysine methylation. We also showed that NF-YA upregulated the expression of EZH2 and NF-YA‑induced angiogenesis could be inhibited by EZH2 knockdown. Taken together, these findings indicate that overexpression of NF-YA contributes to tumor angiogenesis through EZH2-STAT3 signaling in human melanoma cells, highlighting NF-YA as a potential therapeutic target in human melanoma.

Partial or total flap necrosis after flap transplantation is sometimes clinically encountered in reconstructive surgery, often as a result of a period of hypoxia that exceeds the tolerance of the flap tissue. In this study, we determine whether tanshinone IIA (TSA) pretreatment can protect flap tissue against hypoxic injury and improve its viability. Primary epithelial cells isolated from the dorsal skin of mice were pretreated with TSA for two weeks. Cell counting kit-8 and Trypan Blue assays were carried out to examine the proliferation of TSA-pretreated cells after exposure to cobalt chloride. Then, Polymerase chain reaction and Western blot analysis were used to determine the expression of β-catenin, GSK-3β, SOX2, and OCT4 in TSA-treated cells. In vivo, after mice were pretreated with TSA for two weeks, a reproducible ischemic flap model was implemented, and the area of surviving tissue in the transplanted flaps was measured. Immunohistochemistry was also conducted to examine the related biomarkers mentioned above. Results show that epidermal cells, pretreated with TSA, showed enhanced resistance to hypoxia. Activation of the Wnt signaling pathway in TSA-pretreated cells was characterized by the upregulation of β-catenin and the downregulation of GSK-3β. The expression of SOX2 and OCT4 controlled by Wnt signaling were also found higher in TSA pretreated epithelial cells. In the reproducible ischaemic flap model, pretreatment with TSA enhanced resistance to hypoxia and increased the area of surviving tissue in transplanted flaps. The expression of Wnt signaling pathway components, stem-cell related biomarkers, and CD34, which are involved in the regeneration of blood vessels, was also upregulated in TSA-pretreated flap tissue. The results show that TSA pretreatment protects free flaps against hypoxic injury and increases the area of surviving tissue by activating Wnt signaling and upregulating stem cell-related biomarkers.

Background: Partial or total flap necrosis after flap transplantation is sometimes encountered in reconstructive surgery, often as a result of a period of hypoxia that exceeds the tolerance of the flap tissue. The purpose of this study was to determine whether Tanshinone IIA (TSA) pretreatment can protect flap tissue against hypoxic injury and improve its viability., Methods: Primary epithelial cells isolated from the dorsal skin of mice were pretreated with TSA for 2 weeks. Cell Counting Kit-8 and Trypan Blue assays were carried out to examine the proliferation of TSA-pretreated cells after exposure to cobalt chloride. Polymerase chain reaction and western blot analysis were used to assess the expression of β-catenin, vascular endothelial growth factor (VEGF), sex determining region Y-box 2 (SOX2), OCT4 (also known as POU domain class 5 transcription factor 1), Nanog, and glycogen synthase kinase-3 beta (GSK-3β) in TSA-treated cells. In other experiments, after mice were pretreated with TSA for 2 weeks, a reproducible ischemic flap model was implemented, and the area of surviving tissue in the transplanted flaps was measured. Immunohistochemistry was conducted to examine Wnt signaling as well as stem cell- and angiogenesis-related biomarkers in epithelial tissue in vivo., Results: Epidermal cells, pretreated with TSA, showed enhanced resistance to hypoxia. Activation of the Wnt signaling pathway in TSA-pretreated cells was characterized by the upregulation of β-catenin and the downregulation of GSK-3β. The expression of SOX2, Nanog, and OCT4 were also higher in TSA-pretreated epithelial cells than in control cells. In the reproducible ischemic flap model, pretreatment with TSA enhanced resistance to hypoxia and increased the area of surviving tissue in transplanted flaps. The expression of Wnt signaling pathway components, stem-cell related biomarkers, and VEGF and CD34, which are involved in the regeneration of blood vessels, was also upregulated in TSA-pretreated flap tissue., Conclusions: TSA pretreatment protects free flaps against hypoxic injury and increases the area of surviving tissue by activating Wnt signaling and upregulating stem cell-related biomarkers.