# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 1 Introduction  
To explore and monitor underground natural resources, advanced techniques for acquiring subsurface images must be used. One of the most effective tools in this field is seismic imaging. The crucial first step in this technique is to acquire seismic data, which are subsequently processed to generate a seismic image of the subsurface. During seismic data acquisition, seismic waves are transmitted through the Earth, and the echoes reflected from various geological layers are captured. During this step, external factors, such as environmental elements and interference from the devices used in this process, impact the acquired signals. This interference leads to the production of various types of noise, which are typically classified as either random or coherent noise. In the first group, the noise is characterized mainly by strong randomness. On the other hand, coherent noise exhibits a certain regularity and is characterized by relatively low speed, high amplitude, low frequency, and strong energy [1]. To increase the quality of the acquired data and hence their interpretation, the data are processed, thus preventing the masking of pertinent reflections. In this step, a sequence of sophisticated techniques specifically designed to process seismic data is applied. A crucial component of this procedure is noise attenuation, which is aimed primarily at improving the signal-to-noise ratio of the obtained seismic data. Different techniques are applied depending on the characteristics of the noise. The methods for random noise generally rely on filtering techniques in the time domain,predictive filtering, median filtering, methods based on sparse transformations, and low-rank matrix decomposition [2; 3; 4]. Coherent noise, on the other hand, is addressed with techniques for suppressing unwanted parts in the spectral domain, such as filters in the frequency domain, low-pass and high-pass filters, and the ground-roll attenuation method [5; 6]. A challenge encountered with this type of technique is the undesired removal of signals. With the increasing use of artificial intelligence in various fields, methodologies employing machine learning have begun to emerge in seismic applications [7; 8]. Deep learning techniques, due to their ability to solve high-complexity end-to-end problems, have extensively progressed recently and are now used in applications ranging from the exploration phase to processing, inversion, and seismic interpretation [9; 10; 11; 12]. In seismic data denoising, the deep learning methods employed typically use a supervised approach, as shown below. In this approach, a set of noisy data and a set of corresponding precleaned data are used as the input and target of the model, respectively. These supervised approaches have been the foundation for numerous studies aimed at noise attenuation in seismic data processing. [PERSON] et al. [13] applied a CNN architecture to attenuate random and linear noise generated synthetically. [PERSON] et al. [14] proposed an architecture named FD-Net composed of convolutional layers with batch normalization and a rectified linear unit (ReLU) activation function for random synthetic seismic noise removal. For the same type of noise, [PERSON] et al. [15] and [PERSON] et al. [16] presented variations of an autoencoder architecture as models. [PERSON] et al. [17], on the other hand, proposed not only noise reduction but also improvements in the resolution of the generated seismic images through a generative adversarial network (GAN). Employing the same architecture, [PERSON] et al. [18] proposed the addition of a lightweight convolutional attention module in the generator to better extract signal features. Moreover, [PERSON] et al. [19] developed an autoencoder architecture over residual layers for a synthetic dataset with additive Gaussian noise. [PERSON] et al. [20] introduced residual and building blocks into a UNet architecture. The residual blocks enable the acquisition of multiscale features.