Self-Supervised Seismic Random Noise Suppression With Higher-Quality Training Data Based on Similarity Differences

Suppressing random noise and improving the signal-to-noise ratio of seismic data holds immense significance for subsequent high-precision processing. As one of the most widely used denoising methods, self-learning-based algorithms typically partition the large zone into several smaller zones for ind...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.93889-93898
Hauptverfasser:	Gao, Jian, Li, Zhenchun, Zhang, Min, Gao, Wanyue, Gao, Yixuan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Correlation Costs Deep learning Machine learning Noise Noise reduction Random noise Seismic data Self-supervised learning Signal quality Signal to noise ratio Similarity Training Training data
Online-Zugang:	Volltext
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