Self-supervised deep learning for multi-profile seismic data denoising

Abstract

Self-supervised deep learning has been widely exploited in seismic data denoising. However, most of the current methods take a single seismic profile to reconstruct two new similar profiles by sampling. Then, the two new similar profiles are conducted as input and label respectively to train the model. In fact, there obviously exists similarity and more structural information among adjacent profiles. Therefore, we want to use adjacent multiple profiles to denoise seismic data. But this ideal requires that the neural network model can process multiple profiles. In order to solve this problem, we introduce a supervised model which can input five continuous profiles and denoise the middle profile. In addition, a self-supervised training strategy is proposed for the supervised model to train with no clean profiles. The experimental results on synthetic noise show that our method can achieve a higher Signal-to-Noise Ratio (SNR). According to the experiment on real noise, the proposed algorithm also obtains cleaner and smoother denoising profiles.