Leveraging domain adaptation for efficient seismic denoising
Author:
Affiliation:
1. King Abdullah University of Science and Technology (KAUST)
Publisher
Energy in Data
Link
https://library.seg.org/doi/pdf/10.7462/eid2022-04.1
Reference10 articles.
1. Understanding acquisition and processing challenges in the desert environment through SEAM Arid and Barrett models
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3. The potential of self-supervised networks for random noise suppression in seismic data
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1. Deep Generative Model Conditioned by Phase Picks for Synthesizing Labeled Seismic Waveforms With Limited Data;IEEE Transactions on Geoscience and Remote Sensing;2024
2. Integrating self-supervised denoising in inversion-based seismic deblending;GEOPHYSICS;2023-10-11
3. Integrating self-supervised denoising in inversion-based seismic deblending;GEOPHYSICS;2023-10-11
4. Seismic Data Denoising Using a Self-Supervised Deep Learning Network;Mathematical Geosciences;2023-08-16
5. Improving the Generalization of Deep Neural Networks in Seismic Resolution Enhancement;IEEE Geoscience and Remote Sensing Letters;2023
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