# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 1 Introduction
The building blocks, on the other hand, can incorporate these features by distinguishing signals and random noise, generating results superior to those of a standard UNet architecture. Considering multi-scale feature extraction, [PERSON] et al. [21] proposed a new architecture called MSD-Net, designed for mutual-guided scale-aggregation noise reduction. Spatial aggregation attention was used in this context to enhance relevant features and consequently improve noise reduction. For traffic noise issues, [PERSON] et al. [22] use a UNet with asymmetric convolutional blocks (ABC-Unet) that yields results superior to those of the base model. [PERSON] et al. [23] proposed a new architecture for seismic denoising named D-CNN that uses dilated convolutional layers to increase the receptive field without increasing the number of network parameters. In addition, the authors introduced a new augmentation strategy using generative adversarial networks (GANs), which addresses the issue of a lack of real seismic data. [PERSON] et al. [24] introduced the MAW-Net architecture, based on the U-Net network, for noise suppression and microseismic event localization. This architecture employed a two-channel decoder and integrated an attention module to enhance feature extraction. [PERSON] and [PERSON] [25] adapted a denoising convolutional neural network (DnCNN) algorithm originally proposed by [PERSON] [26] for image noise removal to seismic noise removal. In this approach, the noise is used as the target instead of the clean data. The prediction is then subtracted from the original noise to obtain the clean data. Specifically for coherent noise, [PERSON] et al. [27] proposed a method using a U-Net architecture for removing noise generated through the sum of four types of noise: high-level Gaussian noise, repetitive hyperbolic noise at fixed locations, random Gaussian noise, and real noise from field data. [PERSON] et al. [28] presented a new densely connected residual convolutional network (DCRCD) architecture for the removal of complex noise from distributed acoustic sensing (DAS) data. [PERSON] et al. [29] introduced the Only2 Noise model, which aims to extract swell noise of varying intensities and shot characteristics. The model, which is embedded in layers of residual convolutional networks, features the swell noise as the target, whereas the clean data are obtained by removing the prediction from the original data. In this scenario, the model can be utilized not only for noise attenuation but also for capturing the patterns of the swell noise. Some approaches combine traditional methods with neural network models. [PERSON] et al. [30] applied a combination of the singular value decomposition (SVD) method and deep learning, where the right singular vector (RSV) resulting from SVD is used as the target of the neural network model, producing better results than the traditional method alone. [PERSON] et al. [31] proposed the decomposition of the data input and output of a residual network via 1D wavelets, which are ranked in low- and high-frequency coefficients. In general, the progress achieved with these methodologies is considerable. Nevertheless, supervised techniques continue to be limited by their dependence on the volume of labeled data. In response, self-supervised methodologies have been proposed as potential solutions. These methodologies do not require clean data for training. In self-supervised methodologies, context is used to facilitate automatic supervision; thus, human intervention is not required. This is cleverly accomplished by treating the input data themselves as an extensive source of supervisory signals. Rather than relying on external annotations, these approaches extract features from the structure of the data through innovative methods. For example, an image can be artificially masked or distorted, and the model is then trained to predict the original unaltered content, thereby acquiring a deep understanding of the visual semantics without explicit labeling.