Deploying Self-Supervised Learning for Real Seismic Data Denoising
Self-supervised learning (SSL) has emerged as a promising approach to seismic data denoising as it does not require clean reference data. In this work, the deployment of the Noisy-as-Clean (NaC) method was evaluated for real seismic data denoising under controlled conditions. Two independent seismic acquisitions, each comprising noisy and filtered data, were organized into four real datasets. The NaC SSL method was adapted to add real noise to the noisy input, controlled by a parameter. An experimental protocol with ten experiments was designed to compare different strategies for deploying the NaC SSL method with the supervised learning baseline, using identical network topology and hyper...
| Authors | Giovanny A. M. Arboleda, Claudio D. T. de Souza, Carlos E. M. dos Anjos et al. |
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| Primary category | physics.geo-ph |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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