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

Title: 噪声是您所需的一切：通过扩散模型重新思考噪声在地震去噪中的价值

Abstract: We introduce SeisDiff-denoNIA, a novel diffusion-based seismic denoising framework that trains directly on field noise, eliminating the reliance on synthetic datasets. Unlike conventional denoising methods that require clean signal labels, our approach leverages field noise extracted prior to first arrivals as training targets, allowing the diffusion model to explicitly learn the true noise distribution. The model demonstrates robust performance on field DAS-VSP data contaminated by different noise types, and significantly outperforms traditional signal-based diffusion models under low SNR conditions in synthetic tests. The results suggest that explicitly modeling noise is not only viable but advantageous for seismic denoising tasks. Show Chinese abstract

Abstract: 我们引入了SeisDiff-denoNIA，一种基于扩散的新型地震去噪框架，该框架直接在野外噪声上进行训练，消除了对合成数据集的依赖。 与需要干净信号标签的传统去噪方法不同，我们的方法利用首次到达前提取的野外噪声作为训练目标，使扩散模型能够显式学习真实的噪声分布。 该模型在受不同噪声类型污染的野外DAS-VSP数据上表现出稳健的性能，在合成测试中，在低信噪比条件下显著优于传统的基于信号的扩散模型。 结果表明，显式建模噪声不仅可行，而且对地震去噪任务具有优势。