Seismic inversion is crucial in estimating subsurface properties in the oil and gas industry. However, the limited well-log data obtained in production is primarily one-dimensional (1D) data, leading to the dominant of 1D trace-to-trace inversion methods. Inherently, these algorithms do not take seismic data spatial correlation into consideration, resulting in unstable results with poor lateral continuity. Obtaining high-precision multi-parameter inversion results simultaneously for prestack inversion remains a challenge. To address these issues, this work proposes a 2D Multi-task Full Attention U-Net for prestack three-parameter inversion. The proposed network captures geological structural features through shared information and allows each task to learn task-specific attention weights in separate branches. The full attention mechanism fuses shared and internal features of each task and pays attention to both channels and feature maps. A joint hybrid loss function based on mean squared error and structural similarity is used to enhance the continuity and resolution of inversion results. To obtain sufficient training samples, we generate small sample patches by sliding windows both laterally and vertically around the wells. We also incorporate initial models and feed them into the network together with seismic data as input to achieve stable inversion results. Experiments on field data demonstrate that the proposed approach can simultaneously obtain high-resolution and transversely continuous three-parameter inversion results.

Noise suppression is crucial for seismic data processing and interpretation. Both the supervised and self-supervised methods need to construct training data pairs, which is challenging for practical implementations. In this research, we propose an unsupervised denoising framework based on improved blind spot strategy, which operates directly on single noisy seismic data. Firstly, a global masker is introduced to mask the noisy data to obtain two masked data and input them into the denoising network, and then utilize the mask mapper to integrate all blind spots onto the same channel. Secondly, original noisy data is incorporated into the network training process to avoid information loss. Finally, a hybrid loss function with orthogonal convolution regularization constraint is adopted to further improve the performance of the network. Synthetic and field seismic data experiments show that the proposed method can make good balance between noise suppression and valid signals preservation.

Due to various reasons, seismic data are often inevitably affected by noise. Therefore, random noise suppression of seismic data is a key step for seismic data processing workflow. Recently, deep learning method has performed well in seismic data denoising. In this study, we propose a self-supervised deep learning seismic data noise attenuation method. We introduced an effective Blind2Unblind (B2U) denoising framework, which can complete denoising using only a single noisy seismic data. Use a mask mapper with global awareness, which can sample all pixels at the blind spots on noisy data and map them to a same channel. At the same time, a re-visible loss function is used to train the network, which can optimize all blind spots, mitigating the information loss and retaining more details of geological structure. The denoising experiments on synthetic and field data show that our method has achieved superior results compared with previous work.

Seismic elastic parameter inversion enables the transformation of seismic data into subsurface structures and physical parameters of formations. However, due to the intricate geological structure, deep learning methods have been discovered to produce more precise inversion results than traditional methods. Nevertheless, inverting multiple elastic parameters individually is both time-consuming and prone to causing significant errors for the ignorance of interconnections among the parameters. Therefore, multi-task learning is employed in this work. To further improve the inversion accuracy, a hybrid network leveraging the advantages of Fully Convolutional Residual Network (FCRN) and Gated Recurrent Unit Network (GRU) is proposed for the simultaneous inversion of the velocity of P-wave and density, named Multi-task FCRN and GRU (MFG). FCRN is responsible for the efficient extraction of local information from the seismic data, while GRU captures the global dependencies in the data along time. To be mentioned, an auxiliary task of seismic data reconstruction has been added as a regularization technique to enhance the stability of network training. The experimental results obtained using both synthetic model and field data indicate that MFG significantly enhances the inversion accuracy, lateral continuity, and vertical resolution.

Deep learning has been widely used in seismic inversion. Since the label data obtained in production is actually a small amount of one-dimensional (1D) well-log data, most deep learning based seismic inversion is 1D trace-to-trace method based on poststack seismic data. However, the 1D algorithm does not take seismic data spatial correlation into consideration and the results lack of stability with good lateral continuity. Meanwhile, it is still a challenge to obtain high-precision multi-parameter inversion results simultaneously for prestack inversion. To mitigate the above issues, we propose a 2D Multi-task Full Attention U-Net for prestack three-parameter inversion. The proposed network can capture geological structural features in shared information and allow each task to automatically learn task-specific attention weights in a separate branch. The full attention mechanism fuses the shared features and the internal features of each task in stages, and pays attention in both channels and feature maps. Moreover, a joint hybrid loss function based on mean squared error and structural similarity is used to further enhance the continuity and resolution of inversion results. To obtain sufficient training samples, we generate a large number of small sample patches by sliding window both laterally and vertically with equidistant around the wells. In order to obtain stable inversion results, we incorporate the initial models and feed them into the network together with seismic data as input. Experiments on synthetic and field data show that our proposed method can simultaneously obtain three-parameter inversion results with high vertical resolution and transverse continuity.

Missing traces interpolation is a basic step in the seismic data processing workflow. Recently, many seismic data interpolation methods based on different neural networks have been proposed. The existing research shows that when the seismic data are consecutively missing, the larger gap for missing traces, the more difficult task of interpolation, due to convolution operation in the neural network can only capture local relations. In this letter, we incorporate the coordinate attention block to the Unet for 2-D successive missing traces interpolation. The hybrid loss function combined with structural similarity (SSIM) and$\text {L}_{ {1}}$norm is used as the loss function to further improve the interpolation performance of the designed network. Comparison experiments on 2-D synthetic and field seismic data show that the interpolation results obtained by the proposed method are more accurate and reasonable compared with Unet and Unets equipped state-of-the-art similar modules.