Noisy2Noisy: Denoise Pre-Stack Seismic Data Without Paired Training Data With Labels

Abstract

In recent years, supervised deep learning-based denoising methods have been popularized and developed rapidly in the field of seismic data processing. Supervised training, however, is limited by the quality and quantity of the paired training data (noisy clean or noisy noise). Data labeling is a time-consuming and expensive work. Compared with raw seismic data, only a small amount of seismic data have been correctly labeled, which, to some extent, limits the long-term development of supervised deep learning-based methods in the field of seismic data denoising. In this letter, we propose an improved denoising framework based on an unsupervised deep learning-based denoising method Noise2Noise, which only needs unprocessed raw seismic data to train the denoising model. Moreover, unlike Noise2Noise, the proposed method does not need to repeatedly collect seismic data to obtain a training pair with similar signal, which is more convenient and effective. Specifically, we propose a block random sampler that can generate training pairs using raw seismic data, which satisfies the training assumption of Noise2Noise that the training pair has a similar signal. In addition, our method has no requirements for the network structure and noise distribution prior and is flexible. Both synthetic seismic data and field seismic data denoising results show that our method can effectively suppress the random noise, and the denoising performance is equivalent to that of supervised deep learning-based denoising methods. In addition, our method may provide a certain reference for geophysical-related research.