Wave equation-based traveltime inversion is a method that uses traveltime information to obtain the low-wavenumber components of subsurface velocity models. This helps create a reliable initial model for full waveform inversion (FWI). However, this method usually requires identifying the first arrival waves or specific seismic events to calculate the traveltime differences between the observed and synthetic data. When working with multiple seismic events, such as those from elastic wavefields or simultaneous sources-based seismic data, it becomes difficult to obtain the low-wavenumber components of velocity models by traveltime inversion. In this letter, we propose a simultaneous source-based elastic wave local-scale traveltime inversion (SS-ELTI) method. This method utilizes both P-wave and S-wave data, along with local-scale traveltimes from various seismic events generated by simultaneous sources. This approach enables the simultaneous inversion of the low-wavenumber components of both P-wave and S-wave velocity parameters. Numerical tests demonstrate that the proposed SS-ELTI method can effectively reduce the computational costs and mitigate the cycle-skipping problem of elastic full waveform inversion (EFWI).

Distributed acoustic sensing (DAS) has been progressively used in acquiring vertical seismic profiles. However, DAS signals are susceptible to be contaminated by diverse noise, causing many difficulties in the interpretation of DAS VSP. Most of the existing methods for DAS noise suppression rely on either global or local information for the extraction of signal features. They neglected that both local detail and long-distance relevant features are required for denoising. To address this issue, we propose a U-shaped network with a combination of convolutional neural networks (CNNs) and refining transformers, named Urefiner. We employ CNN as a preprocessing step for the transformer model. The transformer model relies on the attention map to extract global features, while the CNN module will aggregate similar features within the attention map to facilitate local information processing. To facilitate the fusion of local information and global information, the distributing-local-attention (DLA) module is induced to calculate the weighted aggregation in the attention map between CNN and transformer, which can improve the effective receptive field of the network in local areas. Additionally, to improve the network’s attention to seismic signals for signal protection, we introduce a learnable linear matrix to expand attention map. It can aggregate the information acquired from different attention heads into a learnable weight matrix for the attention calculation. This linear weighting scheme can promote the interconnections among diverse attention heads, thereby facilitating the fusion of extracted information. Based on the above designs, the proposed Urefiner can augment the effective receptive field to amplify the significance of signal features in the attention map. Experimental results show that the network can effectively suppress various noises in DAS VSP and accurately protecting weak seismic signals.

Suppressing various noises while achieving precise signal reconstruction in Distributed Acoustic Sensing Vertical Seismic Profiling (DAS VSP) remains a challenge. Existing denoising methods are insufficient due to factors such as the unknown noise-disturbing mechanism, low SNR, and limited training data. Therefore, this study proposes the Mean-Stochastic Differential Equation (SDE) diffusion model as an advanced solution. Built upon the standard diffusion model, which incorporates forward and backward diffusion processes, our model introduced three modifications to enhance performance. 1. Improving the forward diffusion process: Transforming the final state into a combination of the noisy DAS VSP and Gaussian noise. This adjustment allows precise representations of multi-type noise generation and facilitates backward sampling. 2. Enhancing noise prediction between successive steps in the backward process: A Nonlinear Activation Free Network (NAFnet) with a time Multi-Layer Perceptron (MLP) was employed to provide accurate noise predictions at different states. 3. Addressing training instability inherent in standard diffusion: The objective function is modified to seek the optimal trajectory of the best quality of signal reconstruction rather than directly evaluating the noise prediction. The forward diffusion is a dynamic evolution of adding noise to the pure signal, while the backward processing aims to remove the noise step by step. Comprehensive experiments demonstrate the superiority of our method in diverse noise suppression, signal resolution enhancement, and amplitude preservation. Moreover, grounded in physics-based equations, our method exhibits less dependency on training data compared to conventional deep learning methods.

Deep learning (DL) exhibits excellent performance in seismic noise suppression, and DL successes are attributed to its ability to learn rich representations from a large amount of data. However, obtaining numerous high-quality labeled data is challenging owing to confidentiality, regional sensitivity, and manual labeling, which limits the capability of DL. To reduce data dependency and improve network generalization, this study proposes a novel denoising architecture based on small-sample transfer learning (TL). The proposed architecture uses a fully pretrained model on the source data as a feature extractor, and then copies and transfers the rich features from the extractor to the denoiser for fine-tuning on the target data. Moreover, to reduce the discrepancy between two different data and better reuse the transferred features, a noise attention block (NAB) is proposed to regularize the representations. The results of multiregion experiment indicate that the proposed network leads to a significant improvement in denoising performance, essentially outperforming existing denoising methods; additionally, it exhibits strong generalization for different types and regions of seismic noise. Moreover, the proposed method can effectively address the data dependency issue, thus, providing great potential for real-time processing or small device applications.

In recent years, the denoising of low-frequency desert noise has been the significant and difficult point in processing seismic data. Traditional random noise suppression methods could not get a good result in processing seismic data in desert areas. Moreover, convolutional neural network (CNN) has made notable achievements in many fields recently. In order to denoise seismic data in desert areas and improve the signal-to-noise ratio (SNR), CNN is introduced to process seismic data. According to the characteristics of desert seismic data, we designed a new network suitable for desert seismic data training and denoising, which is named DnResNeXt. Then, to form a mapping from the noisy data to the pure desert noise, we build a mass of training sets to train the denoising network. Thus, the network can predict the noise, then by subtracting the predicted noise from the noisy data, the denoised data are obtained. Consequently, compared with the traditional methods in suppressing random noise, DnResNeXt network has obvious advantages in both simulation and actual experiments.

Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious advantages over geophones, some weaknesses may limit its application. The main challenge is that DAS is polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. To suppress these novel noises, we developed a new denoising neural network based on singular spectrum analysis—multichannel singular value decomposition denoising convolutional neural network (SVDDCNN). The network can simultaneously extract data features from singular spectrum instead of the time domain, which can represent geophysical features more accurately and help separate signals from noises. Second, a multichannel input layer is designed, and the input is decomposed into three subspaces by singular spectrum analysis, which provides records of different signal-to-noise ratios (SNRs) for training and improves generalization ability of the network. Third, to enhance the quality of the data set, we added the noise subspace records removed by SVD into the training set to provide various forms of noise with different singular spectra. Both synthetic and field examples show that our network has achieved impressive denoising of DAS VSP and demonstrated competitive performance compared with other methods. Furthermore, the structure similarity (SSIM) map is introduced to evaluate the signal leakage by calculating the similarity between the denoised record and the removed noise record. The lowest SSIM index of the proposed network indicated superior signal preservation ability.

In seismic exploration, random noise is an obstacle to the extraction of the effective signals, so the investigation aimed at random noise is the basis of signal processing. It is of great significance to analyze the noise properties and establish accurate noise models. Since the complex changes of the actual medium seriously affect propagation characteristics, it is necessary to establish a noise model in a more realistic medium. In this letter, we suppose a weakly heterogeneous medium whose properties vary with the position. And the link between the Lam constants of the medium and noise properties is established. Therefore, a wave equation is deduced in that medium to describe the propagation law of desert seismic exploration random noise. Based on the Greens function, the random noise field is obtained by superimposing all wave fields excited by each pointlike source. Afterward, quantitative comparisons between the actual random noise and the proposed random noise model are given. The results manifest that there are significant similarities in mathematical characteristics between them. Moreover, compared with the noise model in the homogeneous medium, the proposed noise model is more reliable. In order to prove the application value of the random noise model, it is first applied to construct a complete training set for denoising convolutional neural networks, which is valuable for attenuating the desert seismic exploration random noise. This is an effective way to extend noise data. Consequently, this feasible application will strongly promote the application of neural networks in seismic exploration.