This article presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e., pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully-denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a dataset from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g., Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 seconds of data recorded at a sampling frequency of 1000 Hz over 985 channels (approx. 1 km of fiber) in $<$1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring.

翻译：本文提出了一种弱监督机器学习方法，称为DAS-N2N，用于抑制分布式声学传感（DAS）记录中的强随机噪声。DAS-N2N无需人工生成标签（即预先确定的清洁事件信号或噪声片段示例）进行训练，旨在将随机噪声过程映射到选定的汇总统计量（如分布均值、中位数或众数），同时保留真实底层信号。该方法通过拼接（连接）单根光缆内包含的两根光纤，记录同一底层信号的两个含噪副本，这两个副本受不同独立随机观测噪声实例的干扰。随后，仅需利用这两个含噪数据副本即可训练深度学习模型，生成近乎完全去噪的副本。模型训练完成后，仅需单根光纤的含噪数据即可工作。利用部署在南极Rutford冰流表面的DAS阵列数据集，我们证明DAS-N2N能够显著抑制非相干噪声，并提升自然微震冰震事件的信噪比。我们进一步表明，该方法本质上比标准带通/带阻滤波和白噪声（如维纳滤波）例程以及基于掩码单个DAS通道的可比自监督学习方法更高效、更有效。针对此任务，我们首选的模型轻量级，可在不到1秒内处理采样频率为1000 Hz、覆盖985个通道（约1公里光纤）的30秒数据。由于DAS记录中的高噪声水平，DAS-N2N等高效数据驱动去噪方法对于时间关键的DAS地震检测（尤其是微震监测）至关重要。