October 3, 2025Open Access

Noise is All You Need: rethinking the value of noise on seismic denoising via diffusion models

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The novel SeisDiff-denoNIA framework shows strong performance in seismic denoising, particularly under low SNR conditions.
Results indicate that leveraging field noise as training data significantly enhances noise distribution learning.
The diffusion model outperformed traditional methods when tested on field DAS-VSP data contaminated by various noise types.
This approach highlights the viability of modeling noise directly, presenting a shift from reliance on synthetic datasets.

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Diffusion models improve seismic denoising performance in field data, suggesting noise modeling advantages.

Zhu et al. (2025) studied this question.

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