Seismic data reconstruction is a crucial step in seismic data processing. Traditional methods and deep learning approaches have both been widely used in this field. However, they ignored the interactive learning of inter-channel information, especially in the case of high missing rate where feature extraction became more difficult. To address this issue, we propose an adaptive feature fusion network for the reconstruction of both random and consecutive missing seismic data. The information interaction block is designed into this model to improve the efficiency and adaptability of feature selection. It adaptively emphasizes important feature channels and enables inter-channel information exchange learning. To enhance the ability to capture global and local details, a cross-dimensional feature fusion module is designed at the bottleneck, integrating information from both the channel and spatial dimensions. Additionally, the strategy loss is designed to enable the network to learn the correlations among missing parts of the seismic traces, thereby boosting the reconstruction performance of our model. Compared with other state-of-the-art seismic data reconstruction methods, the proposed algorithm achieves improvements in both qualitative and quantitative evaluations: the reconstruction quality has improved by 20% on both synthetic and field datasets with random missing data. The reconstruction quality has improved by 30% on both synthetic and field datasets with consecutive missing data. At the end of the paper, we conducted ablation experiments, hyperparameter analysis and discussion.

Seismic data reconstruction is a crucial step in seismic data processing. Most existing methods reconstruct seismic data in the spatial domain, often ignoring some important frequency components in the frequency domain, such as high-frequency texture features. Therefore, we propose a frequency-domain self-attention network (FDSANet) to effectively reconstruct seismic data with high-missing-rate. The wavelet transform is employed in this model to better restore weak signals and provide more information at different resolutions. The fast Fourier transform in the frequency-domain self-attention module (FDSAM) enhances global frequency awareness, especially for high-frequency energy. Different frequency components are element-wise multiplied by dynamic weights, effectively suppressing energy leakage and aliasing. Moreover, the nearest-neighbor similarity loss on adjacent shot gathers is incorporated into the loss function to learn information from neighboring shot gathers, further enhancing the reconstruction performance of our model. Experiments on both synthetic and field datasets demonstrate that FDSANet achieves significant improvement over several state-of-the-art methods.

Missing traces reconstruction is a key step for seismic data processing. In recent years, researchers have proposed various interpolation methods for seismic trace reconstruction. However, their models are hard to recover the weak signals in the consecutively missing case. Moreover, convolution operation used in these models is not sensitive to long-term dependencies and global information, which affects the reconstruction of the middle part of the missing area. To solve these problems, we propose a wavelet-based swin residual network (WSRN) for seismic data reconstruction. The swin residual block is designed into the U-net framework to improve the local and non-local modeling ability. Furthermore, by replacing the normal sampling layer, the multi-level wavelet transform is introduced to enhance the recovery ability of weak signals, and data augmentation strategy and a hybrid loss function are used to improve the reconstruction performance of WSRN. Experimental results on synthetic and field datasets illustrate that WSRN achieves significant improvement over some representative deep learning methods.

Random noise attenuation is essential in seismic data processing. In this paper, we propose an unsupervised method called “shot2shot with re-de-noising regularization” to remove random noise. Shot2Shot (S2S) is a new way to train a denoising neural network. S2S takes a shot-gather and its multiple neighboring shot-gathers as input and labels of the neural network, respectively. The principle that S2S can eliminate noise is the correlation of seismic waves and the independence of random noise between neighboring shot-gathers. Because neural networks are more likely to learn correlated information between inputs and labels rather than independent information. Although S2S is effective in denoising, this mode of training may lead to relatively coarse results. Therefore, we propose re-de-noising regularization to make the results of S2S more refined. The re-de-noising regularization consists of two penalty terms that balance each other, the re-de-noising term and the stability term. The stability term is responsible for introducing more fine content from the observations, such as weak waves, but this can introduce new noise. Thus the re-de-noising term is used to avoid the interference of this new noise. Experimentally, our method outperforms other state-of-the-art methods in terms of quantitative results. Visually, our method not only removes the noise but also reconstructs the noisy data more completely. In addition, we explain the role of S2S and re-de-noising regularization more intuitively through ablation experiments. Finally, the robustness of the key hyperparameters is discussed.

Seismic data interpolation is an indispensable part of seismic data processing. In recent years, deep-learning-based interpolation algorithms for seismic data have become popular due to their high accuracy. However, a considerable amount of work has focused on the migration of concepts and algorithms in deep-learning-based methods while ignoring the implicit properties of seismic data itself. In this article, we propose the regeneration prior, which is an implicit property of seismic data with respect to the interpolation function, and are used for self-supervised seismic data interpolation tasks. In mathematical form, the regeneration prior can be considered as a regular term describing the structure of the seismic data. Theoretically, the regeneration prior is a necessary condition to obtain an optimal interpolation function. Experimentally, the proposed method achieves significant improvement in accuracy and intuitive visualization in comparison with advanced unsupervised or self-supervised methods. In addition, we provide an intuitive interpretation of the regeneration prior, and our study shows that the regeneration prior plays an anti-overfitting structuring role in the parameter learning process of the interpolation function. Finally, we analyze the robustness of the regeneration prior. The experimental results show that the performance of the regeneration prior is stable despite the fact that the hyperparameters associated with the regeneration prior are perturbed in a considerable range.

Seismic data reconstruction is an important technology in seismic data processing. Existing reconstruction methods have achieved promising performance for regularly/randomly missing cases. However, recovering consecutive missing data remains challenging due to the loss of large amounts of information in local regions. In this paper, we devise a novel network called RRMFI-Net, which is mainly constructed by a Recurrent Residual Multiscale Feature Inference (RRMFI) module and a Recurrence Adjustment Attention (RAA) module. The RRMFI module infers and fills the missing regions multiple times, and uses the result as a clue for the next inference, which makes the result more elegant. To ensure that there is no ambiguity between the results of multiple inferences, we devise an RRA module, which is fused into the RRMFI module to obtain padding information from a long distance. Experimentally, we compare RRMFI-Net with supervised state-of-the-art methods, demonstrating that RRMFI-Net is more effective on multiple indicators. Furthermore, we conduct ablation studies discussing the impact of key network hyperparameters.

The spatial downscaling of soil moisture (SM) provides a technical tool to solve the problem of coarse resolution of passive microwave products. However, conventional methods are developed based on the kilometer scale grid pixels of remote sensing images. The regular rough grids will lead to the mixing and uncertainty of SM information. In this paper, we formulate a novel land geoparcel-based spatial downscaling technique for the Soil Moisture Active Passive (SMAP) satellite products. It is developed by combining XGBoost (eXtreme Gradient Boosting) machine learning algorithm with the support of geoparcel vector data and a variety of auxiliary raster data. The downscaling effect is evaluated by using SMAP 9km products and site measured data in Tongnan District of Chongqing, China. The experiments show that the geoparcel-based downscaling method maintains the dynamic range of the original SM product, and conserves energy before and after downscaling. It is proved that our method effectively increases the spatial details of the original SM product with complete spatial coverage. The comparison and analysis with the ground verification data demonstrate that the formalized procedure with geoparcel-based spatial downscaling allows better results than those of using km-scale regular grids.