Sample2Sample: an improved self-supervised denoising framework for random noise suppression in distributed acoustic sensing vertical seismic profile data
Yan Zhao, Yushi Li, Ning Wu, Shuqin Wang
Abstract
SUMMARY The performance of supervised deep learning-based denoising methods relies on massive amounts of high-quality training data set with labels. However, data labelling is a time-consuming and tedious process, and the lack of labelled data set has become a major bottleneck affecting the development of supervised deep learning-based denoising methods. In recent years, denoising methods that only use unlabelled noisy data set for training have received more and more attention. Although these methods get rid of the dependence on labels, they usually have some specific requirements on the training data set. For example, the paired training data are required to be multiple noisy observations for each scene or obey a specific noise distribution, etc., which are often very challenging to meet in practical applications. In this study, we propose an improved self-supervised denoising framework based on Noise2Noise that only uses noisy seismic data set for training, and we name it sample2sample. The proposed denoising framework does not require multiple repeated acquisitions of seismic data to obtain multiple independent noisy observations for each scene used for training, and has no specific requirement for the noise distribution prior. Specifically, we introduce a random sampler to generate paired subsamples from some individual noisy seismic data for training. The corresponding elements in the two paired subsamples are adjacent in the original seismic data and approximately meet the training premise of Noise2Noise, that is the paired training data have the same signal. In addition, considering that there are some subtle differences in the signals of the paired subsamples generated by sampling, we also introduce a regularization loss to compensate for this. We conducted a qualitative and quantitative analysis of the denoising performance of the proposed method through further experiments, including synthetic data experiments and field data experiments.