Aoqi Song

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 paper, 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 the 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 letter, we devise a novel network called recurrent residual multiscale feature inference network (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.