# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 1 Introduction
In addition to general applications involving images, this methodology is gaining prominence in seismic applications, with an increasing number of methods being developed. For example, to remove random seismic noise, [PERSON] et al. [32] employed a robust autoencoder neural network, along with maximum and minimum normalization and sampling of patches from the raw data for training in an unsupervised manner, using cross-entropy as the loss function. [PERSON] et al. [33] built upon the idea that the noise in each sample is statistically independent and adapted the noise2 void method [34] for random Gaussian and Laplace synthetic noise. The results were compared with those of traditional methods of FX deconvolution and sparsity-promoting inversion via curvelet transform. [PERSON] et al. [35] introduced an input data generation approach that involves the addition of noise via a combination of Gaussian-Poisson distributions while simultaneously employing the original training data as labels. The objective function was accordingly adapted to accommodate this approach. On the other hand, [PERSON] et al. [36] proposed modifications to the Noise2 Noise (N2N) method [37], which remains one of the initial self-supervised denoising methodologies presenting results significantly comparable to those of the present day. They adapted this method to suit a seismic context where the training pairs were generated through a random noise sampler, operating under the assumption that the signal remains unchanged. Also based on the N2N method, [PERSON] et al. [38] proposed the use of the Monte Carlo method to select training samples that include more valid signals. In related work, [PERSON] [39] leveraged the self-supervised Neighbor2 Neighbor method [40] for noise attenuation in seismology. This was carried out by employing a subsampling technique among neighboring pixels. Notably, for clarity, the term \"pixel\" is used here out of conventional convenience. A seismogram is not, strictly speaking, an image where each point represents a specific color or level of light. Instead, each point in a seismogram represents a measurement of seismic waves at a specific point in time and space, with the amplitude of those waves corresponding to the value associated with that \"pixel\". We utilize the term \"pixel\" only because of its common usage and familiarity in digital image processing, which shares many computational similarities with our methodology. Understanding this adapted use of \"pixel\" is foundational, as it directly informs our methodological approach detailed below. In this study, we modify the Noise2 Self (N2S) method [41], originally proposed by [PERSON] et al., for seismic noise reduction. This method stands out for its approach to the blind noise problem, where the statistical independence of noise is exploited during the image reconstruction stage. First, a certain section of an image is masked; then, a CNN-based architecture is trained to identify the masked area. Once the training is complete, any inconsistencies among the pixels are excluded, yielding a noise-free image as a final product. The contributions of this work include several adaptations specifically tailored to seismic denoising. Initially, we proposed various neural network structures. Then, we modified the training protocol, which typically encompasses distinct phases of training, validation, and testing, to a one-shot learning approach, employing multiple masks derived from the same image and controlling their initiation through the number of iterations. Each seismic shot contains a significantly larger volume of data than an average standard image does, and each shot exhibits characteristics that are intrinsically similar. This proximity suggests that a single image might be sufficient to enable effective learning, making the one-shot training method the most appropriate for this context.