# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 5 Discussion and Conclusions  
In this paper, we adopted a one-shot self-supervised learning approach, inspired by the N2S technique, for noise removal in seismic data using only the original image for training. This methodology was evaluated in scenarios with synthetic noise designed to mimic random variations found in real conditions and in data contaminated by real swell noise. For a more robust comparative analysis, we employed an adaptation of the N2N method in the context of synthetic noise and a supervised method for real noise, allowing more accurate evaluation of our proposed model. The results obtained for the elimination of Gaussian noise demonstrate a high level of efficiency, highlighting the ability of the model to operate effectively using only one image for training and without using labels. In contexts characterized by real noise, the implementation of this same method with different network architectures revealed significant variations in the results. The SRGAN generator network exhibited a high signal removal rate compared to noise, which was not satisfactory. On the other hand, the more simplified architecture, FCN3, not only showed superior denoising performance but also better preserved the original signal. This aspect is crucial, as it prevents the excessive elimination of vital information contained in the data, thus ensuring the quality and fidelity of the original information. Nonetheless, note that both methods produced results inferior to those of the supervised method. However, the unsupervised nature of this approach, using only a single image for training, highlights its potential utility in situations where labeled data are scarce or unavailable. Therefore, this study reinforces the potential of self-supervised learning and one-shot learning techniques in mitigating noise in seismic data. These findings indicate the capabilities of the proposed method, particularly in the context of simpler architectures, to remove noise and preserve the integrity of the information contained in the data. However, there remains room for improvement, particularly in optimizing the mask design, which can also be seen as a potential limitation of the method. Furthermore, considering the potential use of foundation models for initialization presents an avenue for further investigation to enhance the performance of the proposed method. In conclusion, this work advances signal processing by presenting an innovative approach to tackling the challenges of noise contamination. Unlike state-of-the-art methods, our one-shot solution does not require labels and can be applied to various types of noise, not just random noise, in both synthetic and real-world scenarios. This method offers a new perspective on noise mitigation, standing out for its simplicity and effectiveness. Conceptualization, C.d.N.P.P. ; Methodology, C.d.N.P.P. ; Software, C.d.N.P.P., R.d.L.S. and B.V.C. ; Validation, C.d.N.P.P., A.G.E. and B.V.C. ; Formal analysis, C.d.N.P.P., P.M.B., A.B. and A.G.E. ; Investigation, C.d.N.P.P., B.V.C. and A.G.E. ; Resources, C.d.N.P.P., P.M.B., A.B. and A.G.E. ; Data curation, C.d.N.P.P. and R.d.L.S. ; Writing--original draft, C.d.N.P.P. ; Writing--review & editing, C.d.N.P.P., P.M.B., A.B. and A.G.E. ; Supervision, A.G.E. ; Project administration, A.B. and A.G.E. All authors have read and agreed to the published version of the manuscript. Funding: The authors are grateful to Petrobras for financial support, for providing the real swell noise data used in this work, and for providing the high-performance computing environment for running the experiments. The authors are also grateful to Brazilian Research Council--CNPq and Rio de Janeiro State Research Agency--FAPERJ for the financial support for this research. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.