Changpeng Wang

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.

Given the rapidly growing scale and complexity of scientific datasets, existing dimensionality reduction methods suffer from the “crowding problem” and the inability to embed new samples. A data dimensionality reduction method based on variational autoencoder uniform manifold approximation and projection (VAE-UMAP) has been proposed. First, to reduce the coupling between the high-dimensional data, the data is compressed into latent variables using a variational autoencoder (VAE). Then, the uniform manifold approximation and projection (UMAP) is used to further reduce the dimensionality of the latent variables, so that the low-dimensional embedding better maintains the similarity relationship within the original data. Finally, the proposed method is fitted with a training set and embedded in an out-of-sample test set to evaluate the generalization ability to the new data. Experimental results show that on the MNIST and Fashion-MNIST datasets, compared to four prominent dimensionality reduction methods UMAP, DensMAP, VAE and AE, the proposed method achieved trustworthiness scores of 0.994 4 and 0.993 9, surpassing the best current method UMAP by 0.031 6 and 0.014 1, respectively. Additionally, there were significant improvements in visualization, Kendall rank correlation coefficient, and classification accuracy metrics.

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.