Unsupervised Seismic Random Noise Suppression Based on Local Similarity and Replacement Strategy
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DOI: 10.1109/access.2023.3272905
Abstract: Improving the signal-to-noise ratio and suppressing random noise in seismic data is critical for high-precision processing. Although deep learning-based algorithms have gained popularity as denoising methods, they suffer from poor generalization ability, resulting in high… read more here.
Keywords: local similarity; noise; random noise; similarity replacement ... See more keywords