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

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…

• NN
  National Natural Science Foundation of China

  Awards: 42174153, 42230805
• NS
  Natural Science Foundation of Nantong University

  Award: 2024CX093