Seismic data denoising by combining self-supervised and supervised learning

Author:

Sun Yen1,Williamson Paul2

Affiliation:

1. TotalEnergies E&P Research & Technology USA

2. TotalEnergies OneTech R&D France

Publisher

Society of Exploration Geophysicists and American Association of Petroleum Geologists

Reference14 articles.

1. Attribute-assisted footprint suppression using a 2D continuous wavelet transform

2. Structure-oriented singular value decomposition for random noise attenuation of seismic data

3. Signal leakage in f-x deconvolution algorithms

4. Krull, A., T. Buchholz, and F. Jug, 2019, Noise2Void — Learning denoising from single noisy images: arXiv preprint, doi: 10.48550/arXiv.1811.10980.

5. Lapins, S., A. Butcher, J.M. Kendall, T. S. Hudson, A. L. Stork, M. J. Werner, J. Gunning, and A. M. Brisbourne, 2023, DAS-N2N: Machine learning distributed acoustic sensing (DAS) signal denoising without clean data: arXiv preprint, doi: 10.48550/arXiv.2304.08120.

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