A Self-Supervised Method Using Noise2Noise Strategy for Denoising CRP Gathers

Abstract

With the improvement of computing power and the rapid development of deep learning, deep-learning-based methods are widely used in the field of seismic data noise suppression. Supervised learning has proven to be effective but its performance largely relies on noise-free data labeling, which is often unavailable or an expensive process. Therefore, as a form of unsupervised learning, self-supervised learning emerged to overcome this difficulty, with its labels coming from the training dataset itself. In this letter, we propose a self-supervised learning method that requires only raw seismic data to train the model by using the Noise2Noise strategy, which takes advantage of the unpredictability of noises to regress from noisy data to clean data. Our method aims at improving the noise suppression effect for common-reflection-point (CRP) gathers. By comparing with conventional methods, both synthetic and field data show that the proposed framework is not only effective in suppressing random noise, but also remains effective for coherent noise.