## 1 Introduction
[28] used a modified depth confidence network to identify carbonates from logging data and also used the network to process seismic data to obtain additional information. [PERSON] et al. [29] designed a big data platform that combined convolutional neural networks and different machine learning models to increase the recognition accuracy of faults. [PERSON] et al. [30] reported a sparse weighting-based matrix decomposition method to use supervised machine learning to separate active interference signals from coherent background noise sources. [PERSON] et al. [31] proposed an intelligent Monte Carlo machine learning method based on support vector regression for the intelligent interpolation processing of seismic data. [PERSON] et al. [32] designed a U-Net deep neural network model for denoising seismic data, with good denoising results. [PERSON] et al. [33] proposed a deep residual seismic denoising model, which overcomes the limitation of the number of network layers in deep learning algorithms. Compared with previous shallow neural networks, the seismic signal features are learned more adequately, and the noise suppression is better. [PERSON] et al. [34] presented a CNN with a dual residual structure, which allows arbitrary combinations between two operators of different layer pairing operations to extract richer features and helps improve denoising performance. [PERSON] [35] made improvements to a DnCNN by using a form of null convolution in its convolution kernel to increase the field of perception of seismic data, which can both efficiently denoise and protect the effective signal. In order to attenuate seismic random noise, [PERSON] et al. [36] constructed a novel unconventional algorithm based on a proximal classifier with consistency (PCC) in the transform domain. To simultaneously suppress coherent and incoherent seismic noises, this group suggested another deep neural network-based seismic denoising approach [37; 38]. The group suggested that it is feasible to draw on its emerging disciplinary field approach to address relevant problems in the geophysical domain. Moreover, [PERSON] et al. [26] proposed an unsupervised learning method that utilizes end-to-end convolutional neural networks for seismic image denoising, aiming to learn the features of effective signals and suppress unpredictable random noise. [PERSON] et al. [39] simulated synthesized images with different resolutions and noise levels as the training set and combined the L1 loss and the multi-scale structural similarity loss to improve the perceptual quality of the original data. [PERSON] et al. [40] employed Fourier domain adaptation to achieve domain transfer. First, they replaced the amplitude spectrum of synthetic seismic data with the amplitude spectrum of real seismic data for feature space mapping. Then, the network model was transferred and trained using synthetic seismic data to improve its performance on real seismic data. [PERSON] et al. [41] proposed a dual-encoder U-Net to exploit the details and edge information of data, using a main decoder for high-resolution recovery and an edge decoder for edge contour monitoring with the aim of improving the resolution of noisy seismic data and maintaining data fidelity. The DL models mentioned earlier are applied in many different scenarios for seismic data reconstruction, including synthetic and field datasets. However, most of them only consider the seismic features in the time domain when training the DL models, which ignores the frequency information of seismic data, i.e., the multi-scale features of seismic data. Additionally, the prevalent use of pooling operations within CNN-based models, intended to expand receptive fields, often inadvertently compromises the detailed seismic reflection features, adversely affecting the data reconstruction accuracy.