Shear wave velocity (VS) is a vital prerequisite for rock geophysics. However, due to historical, cost, and technical reasons, the shear wave velocity of some wells is missing. To reduce the deviation of the description of underground oil and gas distribution, it is urgent to develop a high-precision neural network prediction method. In this paper, an attention module is designed to automatically calculate the weight of each part of the input value. Then, the weighted data are fed into the long short-term memory network to predict shear wave velocities. Numerical simulations demonstrate the efficacy of the proposed method, which achieves a significantly lower MAE of 38.89 compared to the LSTM network’s 45.35 in Well B. In addition, the relationship between network input length and prediction accuracy is further analyzed.

Seismic data denoising is essential for accurate seismic-exploration data processing and interpretation. Traditional noise suppression methods often result in the loss of critical signals, affecting subsurface structure characterization. This study introduces an innovative Attention Dual-Dilated Convolutional Neural Network (ADDC-Net) to address random noise in seismic data. The network expands its model width to extract complementary features, effectively handling complex random noise. By incorporating dilated convolution, the model increases its receptive field without altering kernel size, enabling a more comprehensive analysis of global data features and extraction of effective signal characteristics. An attention mechanism further enhances noise suppression, reducing signal leakage in local similarity graphs. Experimental results show that ADDC-Net outperforms feedforward DnCNN and DudeNet, improving PSNR by 2.8905 dB and 0.6410 dB, respectively. Additionally, ADDC-Net operates faster than convolutional networks with equivalent receptive fields, demonstrating superior denoising performance while preserving signals.

Hyperspectral images (HSIs) contain abundant spectral and spatial structural information, but they are inevitably contaminated by a variety of noises during data reception and transmission, leading to image quality degradation and subsequent application hindrance. Hence, removing mixed noise from hyperspectral images is an important step in improving the performance of subsequent image processing. It is a well-established fact that the data information of hyperspectral images can be effectively represented by a global spectral low-rank subspace due to the high redundancy and correlation (RAC) in the spatial and spectral domains. Taking advantage of this property, a new algorithm based on subspace representation and nonlocal low-rank tensor decomposition is proposed to filter the mixed noise of hyperspectral images. The algorithm first obtains the subspace representation of the hyperspectral image by utilizing the spectral low-rank property and obtains the orthogonal basis and representation coefficient image (RCI). Then, the representation coefficient image is grouped and denoised using tensor decomposition and wavelet decomposition, respectively, according to the spatial nonlocal self-similarity. Afterward, the orthogonal basis and denoised representation coefficient image are optimized using the alternating direction method of multipliers (ADMM). Finally, iterative regularization is used to update the image to obtain the final denoised hyperspectral image. Experiments on both simulated and real datasets demonstrate that the algorithm proposed in this paper is superior to related mainstream methods in both quantitative metrics and intuitive vision. Because it is denoising for image subspace, the time complexity is greatly reduced and is lower than related denoising algorithms in terms of computational cost.