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[1] arXiv:2511.15638 (替换) [中文pdf, pdf, html, 其他]

标题： 从不完美标签中学习：一种物理感知的神经算子及其在DAS数据去噪中的应用 显示英文标题

标题： Learning from Imperfect Labels: A Physics-Aware Neural Operator with Application to DAS Data Denoising

Yang Cui, Denis Anikiev, Umair Bin Waheed, Yangkang Chen

主题： 地球物理 (physics.geo-ph)

监督深度学习方法通常需要大型数据集和高质量的标签才能实现可靠的预测。 然而，当在不完美的标签上进行训练时，它们的性能往往会下降。 为了解决这个挑战，我们提出了一种物理感知损失函数，作为惩罚项，在训练过程中减轻标签不完美的影响。 此外，我们引入了一种改进的U-Net增强傅里叶神经算子（UFNO），在利用函数空间中算子学习优势的同时，实现了高保真特征表示。 通过结合这两个组件，我们开发了一个物理感知UFNO（PAUFNO）框架，能够有效地从不完美的标签中学习。 为了评估所提出的框架，我们将它应用于犹他FORGE场地分布式声学传感（DAS）数据的去噪。 标签数据是使用集成滤波方法生成的，但在井周通道中仍包含残留的耦合噪声。 去噪工作流程包括一种基于补丁的数据增强策略，包括提升步骤、空域卷积操作、谱卷积和一个投影层，以将数据恢复到所需的形状。 大量的数值实验表明，所提出的框架实现了优越的去噪性能，有效增强了DAS记录并以高精度恢复隐藏信号。 显示英文摘要

Supervised deep learning methods typically require large datasets and high-quality labels to achieve reliable predictions. However, their performance often degrades when trained on imperfect labels. To address this challenge, we propose a physics-aware loss function that serves as a penalty term to mitigate label imperfections during training. In addition, we introduce a modified U-Net-Enhanced Fourier Neural Operator (UFNO) that achieves high-fidelity feature representation while leveraging the advantages of operator learning in function space. By combining these two components, we develop a physics-aware UFNO (PAUFNO) framework that effectively learns from imperfect labels. To evaluate the proposed framework, we apply it to the denoising of distributed acoustic sensing (DAS) data from the Utah FORGE site. The label data were generated using an integrated filtering-based method, but still contain residual coupling noise in the near-wellbore channels. The denoising workflow incorporates a patching-based data augmentation strategy, including an uplifting step, spatial-domain convolutional operations, spectral convolution, and a projection layer to restore data to the desired shape. Extensive numerical experiments demonstrate that the proposed framework achieves superior denoising performance, effectively enhancing DAS records and recovering hidden signals with high accuracy.