Semi-Supervised DAS VSP Data Denoising Using Signal and Noise Distribution Difference

Abstract

Distributed acoustic sensing (DAS), an emerging technology for signal acquisition, has been progressively applied to collect vertical seismic profile (VSP) data. Unfortunately, the obtained DAS VSP data are usually contaminated by various complex noise, which poses a major obstacle to subsequent processing; therefore, suppressing the noise in the DAS VSP data is a critical step. With the development of neural networks, deep learning is widely used for seismic data denoising. Supervised learning-based denoising methods, however, require massive amounts of training datasets with labels. The lack of labeled datasets limit the performance of supervised learning methods. The recently proposed unsupervised learning-based denoising methods reduce the reliance on labeled data, but they are not suitable for processing seismic data containing multiple types of complex noise. In this study, we propose a semi-supervised denoising network (SSDN) that contains both supervised and unsupervised paths. The supervised path is trained using a synthetic dataset to extract rich signal features. Unsupervised path exploits the distribution difference between signal and noise, using field dataset to extract realistic and accurate signal features. The backbone network consists of a three-layer pyramid structure and incorporates a multiscale fusion strategy to improve network performance. The idea of semi-supervised learning reduces the reliance on labeled data, takes full advantage of the distribution characteristic of the field data, and benefits the generalization ability of the denoising model. Experimental results on one synthetic data and four field DAS VSP data demonstrate that the proposed method obtains competitive performance in intense noise suppression and effective signal recovery.