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.

Due to inherent limitations in data acquisition, seismic data reconstruction is an important procedure to recover missing data or improve observation density. Many conventional methods exist to solve the reconstruction task. Reconstruction is challenging, especially in the case of complex seismic data. Recently, convolutional neural networks (CNN) have been applied in seismic data processing. In most cases, the architectures of these CNN-based methods are relatively simple, without sufficient feature interaction, limiting their performance. To improve reconstruction results, a multicascade self-guided network (MSG-Net) is presented. In general, MSG-Net is inspired by the self-guided scheme, and a multicascade architecture is designed to extract informative features within the analyzed seismic data at different resolutions. Following this, a parallel spatial attention module is used to further refine and enhance the primary features, thereby improving reconstruction accuracy. To test and verify the new approach, a training data set is generated, based on the synthetic records obtained by forward modeling methods. Experimental results demonstrate that MSG-Net is a promising approach for performing seismic data interpolation.

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.

The super-resolution reconstruction for seismic images obtained by multi-step processing of field data is essential due to the noise contamination, sparse geometry and low dominant frequency of events, which impairs the subsequent seismic interpretation. Deep-learning-based methods show strong potential in super resolution through supervised learning. Generative adversarial networks (GANs) have shown capability in super-resolution of different kind of images, however it is limited in enhancing the detailed geological structures of seismic images which are fatal for interpretation. To address this issue, we propose a Super-resolution Hybrid Bilateral-attention GAN (SHBGAN) to improve the recovery of weak signals and the reconstruction of geological structures. Specifically, the generator employs hybrid and bilateral attention modules to enhance the capture ability of global and local features. Meanwhile, we use dilated convolutional layers instead of batch normalization (BN) layers in the residual block to improve generalization ability of the trained model. Meanwhile, the discriminator employs global average pooling and convolutional layers to score the authenticity of seismic images rather than the probability to enhance the stability of training. In addition, we add the mean structural similarity (MSSIM) term to the loss function of generator to improve perception quality of predictions. Numerical tests on both synthetic and field data show that SHBGAN is more effective than competing methods in recovering weak signals and reconstructing subtle faults.

Full waveform inversion (FWI) is currently the most accurate technique for obtaining the properties of subsurface media. The absence of low frequencies in the observed data caused cycle-skipping phenomenon and poor initial model which affect the convergence of FWI. We propose a global-feature-fusion and multi-scale network (GM-Net) in a way of supervised learning to compensate for the absent low frequency components in the observed data trace by trace. The difficulty of extrapolating frequency is to achieve smoothness and continuity when changing from high frequency signals to low frequency signals, which is visually shown in the reduction and movement of the sidelobes in high-frequency signals and the overall oscillation of the signals is slowed down. For achieving better extrapolation, the encoder-decoder architecture with multi-scale feature extraction is designed as the backbone of the network. For avoiding the loss of information, we propose to perform 1/2 down-sampling on the original input signal separately based on the odd and even time samples, and then concatenate them along the channel dimension. Since 1-dimensional (1D) seismic data is a type of time-series signal and the wavelengths of low frequencies are long, we pay more attention to the relevance of contextual information. Thus, dilated convolution layers, gridding convolution blocks and non-local attention blocks are used to enlarger the receptive field both in time and channel dimensions to extract and fuse global features. Numerical tests both on synthetic data and different types of field marine data demonstrate the feasibility and generalization of our method.

Recorded seismic data often are characterized by a low signal-to-noise ratio (SNR) that can hinder subsequent imaging and interpretation tasks. Thus, it is necessary to explore a method to recover weak signals from strong background noise. While numerous studies have demonstrated the effectiveness of deep-learning methods in seismic noise attenuation, enhancing their capability to recover weak signals under low SNR conditions remains an area for further exploration. To address this issue, we propose an edge-feature-guided wavelet U-Net (EFGW-UNet). In this novel architecture, we utilize the discrete wavelet transform (DWT) to replace the pooling operation deployed in the conventional U-Net, thereby maintaining more detailed information of effective signals. Meanwhile, we also design a dual decoder for edge detection to obtain the shape and edge information on the effective signals. Finally, to fuse multilevel image features and edge features, an attention feature fusion module is deployed. In the experimental part, we use synthetic and real data to illustrate the effectiveness of EFGW-UNet. Our results suggest better denoising performance than competitive methods, especially for weak signal recovery submerged in heavy noise.