Reconstruction of complete seismic data is a crucial step in seismic data processing, which has seen the application of various convolutional neural networks (CNNs). These CNNs typically establish a direct mapping function between input and output data. In contrast, diffusion models which learn the feature distribution of the data, have shown promise in enhancing the accuracy and generalization capabilities of predictions by capturing the distribution of output data. However, diffusion models lack constraints based on input data. In order to use the diffusion model for seismic data interpolation, our study introduces conditional constraints to control the interpolation results of diffusion models based on input data. Furthermore, we improving the sampling process of the diffusion model to ensure higher consistency between the interpolation results and the existing data. Experimental results conducted on synthetic and field datasets demonstrate that our method outperforms existing methods in terms of achieving more accurate interpolation results, with SSIM outperforming existing methods by 4.1% and SNR by 24%. Our implementation is available at https://github.com/WAL-l/Reconstruction.

Seismic data often suffer from missing traces, and traditional reconstruction methods are cumbersome in parameterization and struggle to handle large-scale missing data. While deep learning has shown powerful reconstruction capabilities, convolutional neural networks’ point-to-point reconstruction may not fully cover the distribution of the entire dataset and may suffer performance degradation under complex missing patterns. In response to this challenge, we propose a novel diffusion model reconstruction framework tailored for 3D seismic data. To facilitate three-dimensional seismic data reconstruction using diffusion models, we introduce conditional constraints into the diffusion model, constraining the generated data of the diffusion model based on the input data to be reconstructed. We introduce a 3D neural network architecture into the diffusion model and refine the diffusion model’s generation process by incorporating existing parts of the data into the generation process, resulting in reconstructions with higher consistency. Through ablation studies determining optimal parameter values, although the sampling time is longer, our method exhibits superior reconstruction accuracy when applied to both field datasets and synthetic datasets, effectively addressing a wide range of complex missing patterns. Our implementation is available at https://github.com/WAL-l/SeisFusionhttps://github.com/WAL-l/SeisFusion.

For a long time, the 2-D and 3-D magnetotelluric (MT) forward modeling is mainly accomplished by computational methods. Traditional methods are time-consuming due to the large amounts of discrete grids and slow solution of the matrix equation. Therefore, finding a fast forward modeling algorithm remains a major concern. In recent years, deep learning has provided new ways to accomplish this goal. Most existing deep-learning-based MT forward modeling models are performed on 2-D data, and there is a lack of research on the feasibility of 3-D problems. This article constructs a large-scale 3-D MT dataset, uses a deep neural network (NN) suitable for 3-D MT data patterns, and improving the training efficiency through a transfer learning strategy for similar tasks, which can predict the apparent resistivity and phases in different polarization directions, and realizes fast and high-precision 3-D MT deep learning forward modeling. The experimental quantitative metrics show that the mean relative errors (MREs) of apparent resistivity and phase are 0.6042% and 0.2423%, respectively, and the mean absolute errors (MAEs) are 1.6726 and 0.0994, respectively. When applying the method to geoelectric models that are more complex than the training set, accurate forward modeling results validate its generalization ability. The research may provide methodological and data support for large-scale 3-D MT forward modeling in the future.

Due to limitations such as geographic, physical, or economic factors, collected seismic data often contain missing traces. Traditional reconstruction methods require extensive empirical parameter tuning and perform poorly on large contiguous sections of missing traces. Recent advances in deep learning have led to diffusion models with strong reconstruction capabilities. However, UNet-based diffusion models are computationally intensive and struggle to learn correlations between different traces. To address these challenges, we proposed SeisRDT — a latent diffusion transformer (DiT) based on representation learning for seismic data reconstruction. This uses a new architecture that we proposed, the representation diffusion transformer (RDT) architecture, which incorporates a relative positional bias that is added when calculating attention. It also uses a mask modeling scheme based on representation learning to enhance reconstructed data amplitude distribution consistency. The combination of these features enables the method to have global information awareness of the seismic data set, which is especially beneficial when there are large contiguous gaps in the data. Despite having these advantages that improve the quality of results, the proposed method has smaller computational resource requirements than most of the alternative approaches we compared it against. This is in part achieved due to its use of a pre-trained data compression model that compresses data into a latent space. Experiments on field and synthetic data sets show that our method achieves higher reconstruction accuracy than some existing methods and can handle various complex missing data scenarios. However, as the SeisRDT learns the probability distribution of the training data, it may not generalize well to data sets with significantly different distributions.

The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas. However, the acquired signals are often contaminated by complex noise, which can compromise the accuracy of subsequent inversion interpretations. Traditional denoising techniques primarily rely on parameter selection strategies, which are insufficient for processing field data in noisy environments. With the advent of deep learning, various neural networks have been employed for SATEM signal denoising. However, existing deep learning methods typically use single-mapping learning approaches that struggle to effectively separate signal from noise. These methods capture only partial information and lack interpretability. To overcome these limitations, we propose an interpretable decoupled representation learning framework, termed DREMnet, that disentangles data into content and context factors, enabling robust and interpretable denoising in complex conditions. To address the limitations of convolutional neural network (CNN) and transformer architectures, we utilize the receptance weighted key value (RWKV) architecture for data processing and introduce the contextual weighted key value (Co-WKV) mechanism, which allows unidirectional weighted key value (WKV) to perform bidirectional signal modeling. Our proposed Cover Embedding technique retains the strong local perception of convolutional networks through stacked embedding. Experimental results on test datasets demonstrate that the DREMnet method outperforms existing techniques, with processed field data that more accurately reflects the theoretical signal, offering improved identification of subsurface electrical structures.

Reconstruction of complete seismic data is a crucial step in seismic data processing, which has seen the application of various convolutional neural networks (CNNs). These CNNs typically establish a direct mapping function between input and output data. In contrast, diffusion models which learn the feature distribution of the data, have shown promise in enhancing the accuracy and generalization capabilities of predictions by capturing the distribution of output data. However, diffusion models lack constraints based on input data. In order to use the diffusion model for seismic data interpolation, our study introduces conditional constraints to control the interpolation results of diffusion models based on input data. Furthermore, we improving the sampling process of the diffusion model to ensure higher consistency between the interpolation results and the existing data. Experimental results conducted on synthetic and field datasets demonstrate that our method outperforms existing methods in terms of achieving more accurate interpolation results.