Marine seismic data is often plagued by low-frequency swell noise with prominent amplitudes, compromising the usability of the data for downstream applications, such as inversion, imaging. Traditional frequency-based denoising methods, while widely used to suppress this noise, face the significant challenge of balancing effective denoising with the preservation of valuable seismic signals. In recent years, deep learning (DL)-based seismic denoising methods have developed rapidly, and the landscape of DL-based seismic denoising algorithms is largely dominated by convolutional neural networks (CNNs). However, due to the limited range of receptive fields in CNNs, they struggle to capture enough context from seismic data for effective denoising. Recently, another network architecture, Transformer, has shown better potential in the computer vision domain and seismology e.g. Earthquake transformer–an attentive deep-learning model for simultaneous earthquake detection and phase picking, where its multi-headed self-attention and long-range dependencies can expand the range of receptive fields. Therefore, this paper proposes a new denoising algorithm with the Transformer network as a backbone architecture, called Modal-Enhanced Transformer Denoising Network (ME-TransNet). Specifically, we use the Variational Mode Decomposition (VMD) method as a sparse sub-layer, aiming to reduce the learning difficulty by mapping the original data with strong amplitude noise to the modal domain with stable data features, and the SwinIR algorithm as a denoising sub-layer to attenuate the swell noise in different sub-modal data. The denoising performance of the proposed algorithm is verified by processing synthetic and field seismic data, and the signal-to-noise ratio is improved by about 33 dB.

The seismic data reconstruction techniques are primarily used to address data missing or damaged due to human or environmental factors under restricted acquisition conditions and thus enhance the accuracy of obtained stratigraphic information. Hence, seismic data reconstruction stands as a crucial preprocessing step and is necessary for the effective exploration of subsurface resources. The existing reconstruction methods often fall short of fully utilizing the information existed in seismic data, thereby impacting reconstruction accuracy of effective signals. To overcome the aforementioned limitation, we propose a hierarchical guided multicascade feedback network (HGMFN), which facilitates comprehensive interaction of seismic data across different resolutions by learning intricate features from clean and complete seismic data at various scales. The proposed network achieves progressive integration of features along with layer-by-layer guidance and multilevel feedback mechanisms, accomplishing the reconstruction task and improving the processing precision. Experiments conduct with both synthetic and field data have confirmed the accuracy and performance of HGMFN in complex seismic data reconstruction.

Subbottom profilers (SBPs) using shipboard sonar can acquire massive amounts of data during exploration missions. Some geological intrusions and artificially buried objects show hyperbola-shaped signatures in the SBP images. These signatures are valuable information, but their detection is time-consuming. In addition, noise and geometric spread further increase the difficulty of detection. This article proposes an automated detection method of hyperbola-shaped signatures in SBP images by utilizing morphological processing. The proposed method can be summarized into four steps: preprocessing, segmentation, morphological processing, and fitting. The morphological processing is the critical technology in the proposed method, including opening, dilation, and skeletonization. Trend curves of signatures can be outlined without a priori knowledge by exploiting morphological processing. The fitting algorithm can refine the curves further into an analytical curve. We validate the feasibility and effectiveness of the proposed method in field data acquired from the Marianas region. Meanwhile, we demonstrate that the proposed method better detects ill-shaped and large curvature hyperbola-shaped signatures. Compared with the template matching and the column-connection clustering (C3) methods, the proposed method can provide better precision and recall using an optimized threshold. In addition, the proposed method is a general detection methodology that can be applied to any SBP images with proper parameters. In conclusion, the morphological processing presented in this article can be employed as a generic hyperbola-shaped signature detection module in SBP image processing.

Distributed optical fiber acoustic sensing (DAS) is a rapidly developed seismic acquisition technology with the advantages of low cost, high resolution, high sensitivity, small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by the DAS system due to some factors, including optical fiber damage and inferior coupling between cable and well. Recently, some deep-learning (DL) seismic interpolation methods based on convolutional neural networks (CNN) have shown impressive performance in regular and random missing cases but still remain the consecutively missing case a challenging task. The main reason is that the weight sharing makes it difficult for CNN to capture enough comprehensive features. In this article, we propose a transformer-based interpolation method, called seismic interpolation transformer (SIT), to deal with the consecutively missing case. This proposed SIT is an encoder-decoder structure connected by some U-shaped swin-transformer (UST) blocks. In the encoder and decoder part, the multihead self-attention (MSA) mechanism is used to capture global features which is essential for the reconstruction of consecutively missing traces. The UST blocks are utilized to perform feature extraction operations on feature maps with different resolutions. Moreover, we introduce the SSIM loss to optimize the training process of SIT. In experiments, this proposed SIT outperforms A-Net and swin-transformer (ST). Moreover, ablation studies also demonstrate the advantages of new network architecture and loss function.

Seismic exploration is a crucial method for studying underground geological structures and oil/gas resources. However, the presence of various noise sources during seismic wave propagation hinders accurate interpretation and imaging. To address this challenge, effective denoising methods are essential. In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in seismic data processing. Nevertheless, CNNs have limitations in capturing long-range dependencies and global coherence. As an alternative, we propose a Transformer-based model called Seismic Data Denoising Transformer (SDT) for seismic signal processing. By leveraging self-attention mechanisms, the SDT model overcomes the limitations of CNNs and effectively captures long-range features for seismic signal reconstruction. We also introduce a novel self-supervised pretraining strategy using a large-scale dataset to further enhance performance. Experimental results demonstrate the advantages of SDT in complex seismic noise attenuation and preserving weak signal amplitudes. The proposed method exhibits promising potential for real-world seismic data applications.

Velocity and reflectivity models are fundamental outcomes obtained from seismic exploration, enabling the identification of subsurface structures and physical properties. Two primary methods for obtaining these high-resolution results are two-way wave equation-based full waveform inversion (FWI) and least-squares reverse time migration (LSRTM). Despite sharing a similar least-squares inversion framework, FWI and LSRTM present challenges in effectively combining them to generate both velocity and reflectivity models simultaneously due to their different modeling engines and dependencies on data components. In this study, we propose a novel joint migration inversion (JMI) approach by incorporating a two-way full-wavefield modeling engine parameterized by the subsurface velocity and reflectivity. By providing adjoint velocity and reflectivity sensitive kernels, this JMI can produce high-quality velocity and reflectivity models concurrently. The method developed in this study has the potential to directly process seismic data containing full-wavefield information, reducing data preprocessing complexity and avoiding potential damage to valid signals during the processing. Through a synthetic data test and a benchmark test, we demonstrate the effectiveness of our JMI approach. Moreover, when compared with conventional approaches for velocity building and imaging, our method yields velocity and reflectivity models with higher resolution and improved consistency.

In marine exploration, conventional migration algorithms based on primary reflections often have poor angular illumination, especially in shallow areas and complex salt boundaries. Source density is the primary controller of angular illumination. It is well-known that source density is relatively sparse in a typical marine seismic acquisition, such as in a towed streamer survey. In contrast, imaging of multiples treats each receiver as a virtual source, mimicking a high-density source survey. Imaging of multiples can provide additional angular illumination and help to improve subsurface imaging. The extra illumination provided by multiple reflections enhances angle-domain common-image gathers (ADCIGs), a component of velocity model building and amplitude versus angle (AVA) analysis. Our main objective is to illustrate the advantages of angular illumination from imaging of multiples in the angular domain. To achieve this purpose, we propose a workflow for calculating high-quality ADCIGs for imaging of multiples. The workflow mainly contains up-going and down-going wavefield decomposition and the stabilized Poynting vectors for calculating more accurate imaging angles. To illustrate the accuracy of the proposed workflow, a simple model is used to calculate angle gathers for imaging of multiples. Then, the Sigsbee2b model and a field dataset from the Gulf of Mexico are used to compute angle gathers by imaging primaries and multiples to demonstrate the improved angular illumination achieved when multiples are also used for imaging. The comparison results show that the angle gathers obtained by imaging of multiple has better angular illumination, especially in shallow areas and complex salt boundaries.

In seismic exploration, erratic noise is a type of intense and complicated interference with large-amplitude and non-Gaussian distributions. The presence of erratic noise has been demonstrated to corrupt reflection events, adversely affecting the identification of effective signals. Nonetheless, conventional and time-frequency thresholding denoising methods based on the least-squares scheme usually assume that the seismic random noise has a Gaussian distribution, which is not the case for erratic noise. Therefore, the attenuation for erratic noise is challenging, owing to the deviation from the assumptions of the conventional methods. The recent application of convolutional neural networks (CNNs) to seismic data processing has yielded promising results. However, these CNN-based frameworks always have limited feature-interaction capability, resulting in the degeneration in denoising performance when coping with intense erratic noise. To address this issue, a novel joint-guided denoising network (JGD-Net) is proposed in this study. Unlike conventional CNN frameworks, JGD-Net uses a joint-guided scheme and attention mechanism to enhance the denoising capability. We generate synthetic records using published geological models such as Marmousi and salt dome to compose our training dataset. Furthermore, a novel loss function based on L1 norm and hyperbolic tangent function is designed to further ensure the optimization process of the training procedure and ease the influence of abnormal energy of erratic noise. Both synthetic and field data are processed sfor the evaluation of denoising performance. Compared with other popular methods, JDG-Net shows advantages in attenuating intense erratic noise, particularly under extremely low signal-to-noise ratio (SNR) conditions.

Distributed optical fiber acoustic sensing (DAS) is an emerging acquisition technology in seismic exploration. However, DAS records are always affected by the complex background noise, resulting in a low signal-to-noise ratio (SNR). In addition, the DAS background noise has different properties from the noise existing in conventional seismic data. Thus, conventional denoising methods may degrade the record when dealing with complex DAS data. To improve the denoising capability, a novel denoising network, called residual modular cascaded heterogeneous network (RMCHN), is proposed. In general, the network is based on the idea of heterogeneous convolution and modular convolutional neural networks. Specifically, different modules are designed to extract the discriminatory features of the DAS data through effective information integration. On this basis, heterogeneous convolution combined with long and short path feature learning strategy is employed to fuse the captured features, thereby improving the feature expression capability and avoiding the information loss. Both synthetic and field denoising results indicate that RMCHN can suppress the DAS background noise with excellent performance in signal restoration, even for the weak signals form deep strata.

Seismic data reconstruction is always an essential step in the field of seismic data processing. Effective reconstruction methods can obtain high-density information at low-cost and also recover missing seismic data. Due to the strong feature extraction ability, convolutional neural network (CNN) has shown remarkable performance in numerous fields of data processing and been gradually applied to seismic data reconstruction. However, most of CNN-based methods applied to seismic data reconstruction only consider features in single scale or just utilize simple interactions between different scales, which is likely to result in performance degradation when facing complex and extremely incomplete seismic data. To further promote the performance of CNN-based methods in seismic data reconstruction, a novel multiscale enhanced attention network (MSEA-Net) is proposed based on the self-enhanced scheme. In general, MSEA-Net has a multiscale architecture which can significantly improve the processing accuracy by fusing the potential features in different-resolution seismic data. From the basis, a parallel sparse residual block is designed and applied in MSEA-Net to enhance processing efficiency and avoid overfitting issues. In addition, a dense spatial attention block is also introduced to the network to further reinforce the effective features, thereby strengthening the reconstruction performance. Experimental results demonstrate that our proposed network can effectively reconstruct incomplete seismic data including regular missing data, irregular missing data, and even consecutively missing data with big gap, which is superior than exist interpolation methods including commonly used U-Net.

Recently, a number of deep learning (DL) methods are developed to attenuate the noise in seismic data. Most of them show good performance under a common precondition: the training and testing data are drawn from the same distribution. However, it is challenging to acquire sufficient noise training data whose distribution is same as the noise presented in the testing seismic data; we call such noise data with same distribution as target-domain noise. To address this issue, we propose a promising DL paradigm for seismic data denoising based on transfer learning and seismic noise modeling. We firstly utilize a Green-function-based modeling method for seismic noise to generate a massive amount of synthetic noise which is similar to real seismic noise. Secondly, the high-authenticity synthetic noise is used as the pre-training data in source domain. Finally, we utilize limited real target-domain noise data to fine-tune the partial trainable parameters of pre-trained model and thus transferring it into target domain. This proposed DL paradigm gets rid of the need for enough target-domain noise data, so as to extend the application scope of DL-based method in seismic data denoising. Moreover, we design a novel network architecture based on multi-cascade structure and attention mechanism. This DL paradigm shows extremely similar denoising performance to that of using a large amount of target-domain noise data in both synthetic and real examples, demonstrating its potential in mitigating the dependence of DL-based seismic denoising methods on target-domain noise data.

The formulation of conventional least-squares reverse time migration (LSRTM) starts with the forward modeling process; as a result, the migration operator of it presents as a migration process with a cross correlation imaging condition (CCIC). Since the imaging results produced by CCIC usually contain undesirable components (e.g., strong backscattering noise), it can be assumed that the primary target of the conventional LSRTM is to fit input data rather than produce high-quality imaging results; therefore, conventional LSRTM can be considered as a modeling-driven algorithm. To mitigate the desirable component in the imaging results, additional efforts should be spent in the process of modeling-driven LSRTM. To improve the performance of the LSRTM, we develop a migration-driven LSRTM by formulating the migration process using the inverse scattering imaging condition (ISIC) first. To guarantee the convergence of the algorithm, an adjoint modeling operator and a data precondition operator are incorporated in this migration-driven LSRTM. Since the ISIC can effectively eliminate the backscattering noise, this migration-driven LSRTM can produce high-quality images without the influence of that. After two synthetic data tests, this approach is applied to a 2-D streamer field dataset from the Gulf of Mexico. These tests indicate that, compared to the modeling-driven LSRTM, the migration-driven LSRTM approach can solve the inversion problem more robustly and efficiently.

Conventional least-squares reverse time migration (LSRTM) generally involves a de-migration operator based on the first-order scattering approximation (Born modeling), which can only simulate the seismograms containing the primary reflected wave. When the input observed seismograms contain “redundant information” (especially multiples), crosstalk may occur in the imaging results. Therefore, we develop a least-squares full-wavefield reverse time migration (LSFWM), which is implemented based on a two-way modeling engine with vector reflectivity and the corresponding adjoint sensitive kernel. This modeling engine is modified from the variable density acoustic wave equation and can simulate the subsurface wavefield containing the primaries and multiples only by giving the accurate or estimated subsurface reflectivity and velocity. Theoretically, this LSFWM approach can eliminate the influence of “redundant information” on imaging and provide higher-quality imaging results compared to conventional LSRTM. In addition, since the modeling engine is based on vector reflectivity, the imaging results produced by the LSFWM are also vectorized, which can give more information about the subsurface structures, especially steep structures. And the imaging results produced by the LSFWM can accurately depict the subsurface reflectivity. These are helpful to obtain the information on subsurface structure and physical properties more clearly.

As complementary to the imaging of primaries, multiples can be used for imaging and produce additional angular illumination. These additional illumination angles can improve imaging resolution. There are several methods to calculate the angle gathers from imaging of primaries, among which the directional vector methods are matured and have been implemented in practice. For conventional directional vector algorithms using common-shot reverse time migration, usually, only one angle with the main contribution to each imaging point is collected, which is referred to as a one-shot, one-position, and one-angle mapping strategy. This strategy is appropriate for imaging of primaries; however, the principle fails in the imaging of multiples, where more than one angle exists at an imaging point even using a shot gather. Therefore, conventional directional vector approaches cannot separate different imaging angles produced by imaging of multiples. In this article, we introduce the Poynting vector method to calculate angle gathers for imaging of multiples. Based on the one-shot, one-position, and one-angle mapping strategy, we search for the peak of modulus of Poynting vectors in a time window to differentiate imaging angles at an imaging point. The ray path diagrams and angle-gather imaging conditions are used to demonstrate the improved angular illumination from imaging of multiples. Then, the arriva -time equation is derived mathematically to demonstrate wavefield arrival time differences for different imaging angles from imaging of multiples. Based on the arrival time differences, a Poynting vector algorithm is proposed to compute more than one angle at one subsurface location for imaging of multiples. Data examples are used to validate our approach.

Distributed acoustic sensing (DAS) has been considered as a breakthrough technique in seismic data collection owing to its advantages in acquisition cost and accuracy. However, the existence of complex background noise combined with a tough exploration environment always results in incomplete data with a low signal-to-noise ratio, posing a big challenge for the subsequent processing of DAS data. To improve the quality of DAS data, convolutional neural networks (CNN) have gradually been utilized to deal with the denoising and reconstruction tasks. Meanwhile, some successful applications have verified that CNN-based methods can significantly alleviate the impacts of DAS background noise and missing trace records, compared with conventional approaches. Nonetheless, in most researches, the denoising and reconstruction tasks are accomplished independently, severely affecting the processing efficiency. In this study, a multi-scale encoder-decoder network (MEDN) is proposed to simultaneously achieve the DAS background noise suppression and weak signal recovery through a unified model. Generally, MEDN can extract the different-scale features through both a multi-scale network architecture and a multi-scale residual (MSR) block. The captured different-scale features are then fused to enhance the effective feature. In addition, the encoder-decoder scheme is also utilized in the design of the network architecture to further enhance the reconstruction performance. Moreover, depthwise separable convolution (DSC) blocks are also utilized to ease the computational burden and improve the processing efficiency. Theoretical and field data processing results show that MEDN can provide better denoising and reconstruction performance than conventional methods and popular CNN-based frameworks.

Recently, distributed optical fiber acoustic sensing (DAS) is regarded as a transformative technology in seismic exploration. However, both various complex background noise and weak desired signals significantly limit its practical application. To explore an effective denoising method for the vertical seismic profile (VSP) record received by DAS, we propose an improved residual encoder–decoder deep neural network (RED-Net) enhanced by deep iterative memory block (DMB) and channel aggregation block (CAB), called residual channel aggregation encoder–decoder network (RCEN). Here, DMB uses the weight accumulation theory to improve the feature extraction ability and achieve accurate noise elimination. Meanwhile, CAB, using the multi-channel analysis architecture, enhances the weak signal retention performance. In addition, we leverage both the synthetic data obtained by forward modeling and real DAS noise data to construct a sufficient training dataset with high authenticity, thereby meeting the requirement of network training. Both the synthetic and field DAS-VSP data processing results demonstrate the advantage of RCEN compared with competing algorithms, including singular value decomposition (SVD), conventional RED-Net, and feed-forward denoising convolutional neural network (DnCNN).

Seismic background noise often damages the desired signals, thereby resulting in some artifacts in the seismic imaging that follows. Since about 2016, some supervised-deep-learning methods have shown impressive performance in seismic data denoising, but they usually only consider single-scale features and neglect the multi-scale strategy. To further reinforce their denoising performance, a novel multi-scale convolutional neural network (CNN) combined with spatial attention mechanism, called multi-scale spatial attention denoising network (MSSA-Net), is proposed to tell weak reflected signals apart from strong seismic background noise. Unlike conventional single-scale CNNs, this proposed MSSA-Net can achieve the extraction of multi-scale features which is beneficial for the suppression of strong noise and the recovery of weak reflected signals. Specifically, MSSA-Net contains a principal denoising network and two auxiliary networks. The former utilizes the widen convolution composed of multiple parallel convolution layers with different kernel sizes to capture the informative multi-scale features; the latter two leverage up and down sampling to extract local fine and global coarse features, respectively. Furthermore, a spatial attention block is adopted to fuse these multi-scale features, thereby distinguishing weak reflected signals from strong seismic background noise. Multiple experiments of synthetic and real seismic records demonstrate the effectiveness of MSSA-Net. In addition, compared with two classical single-scale CNNs, MSSA-Net performs better in signal recovery, indicating the positive effect of multi-scale strategy.

Seismic background noise affects the recognition of reflection signals, thereby impeding the subsequent seismic data processing, such as seismic imaging and inversion. In addition, seismic background noise has relatively complex properties, such as non-stationarity and spectral aliasing, which can be further hampered with the deterioration of the exploration environment. Deep-learning methods have been successfully applied to effectively attenuate complex seismic noise and have shown significant improvements over conventional denoising methods. However, most denoising networks only utilize single-scale features, resulting in poor performance when confronted with seismic data in a low signal-to-noise ratio. To further enhance the denoising capability, a novel multiscale residual pyramid network (MRP-Net) was proposed to separate the desired signals and complex seismic noise. Compared with single-scale networks, MRP-Net can take advantage of the multiscale features, thereby improving noise attenuation capability. In general, the pyramid-like framework in MRP-Net can extract the potential features at different scales through down-sampling and up-sampling operations, and skip connections were applied to fuse the global-coarse and local-fine features. On this basis, a double-path spatial attention module was designed to enhance the desired features, further improving the processing performance of separating the desired signals from the intense seismic background noise. Comprehensive experiments on synthetic and field seismic data demonstrate that MRP-Net is effective for complex seismic noise attenuation, both for conventional geophone-acquired data and DAS records.

Automated guided vehicles (AGVs) have been largely used in manufacturing and supply chain management. With the development of Auto-ID technologies like radio frequency identification (RFID), AGVs' positioning could be enhanced. This paper demonstrates using magnetic field lines in the AGVs for precise coverage locating based on the errors' suppression positioning method. Dolph-Chebyshev antenna array is used to enable AGVs with more precise location implementation. It is observed that the far-field active RFID system positioning accuracy is higher, the movement is more stable, and the fluctuating rate is smaller.