Distributed acoustic sensor (DAS) technology leverages optical fiber cables to detect acoustic signals, providing cost-effective and dense monitoring capabilities. It offers several advantages including resistance to extreme conditions, immunity to electromagnetic interference, and accurate detection. However, DAS typically exhibits a lower signal-to-noise ratio (S/N) compared to geophones and is susceptible to various noise types, such as random noise, erratic noise, level noise, and long-period noise. This reduced S/N can negatively impact data analyses containing inversion and interpretation. While artificial intelligence has demonstrated excellent denoising capabilities, most existing methods rely on supervised learning with labeled data, which imposes stringent requirements on the quality of the labels. To address this issue, we develop a label-free unsupervised learning (UL) network model based on Context-Pyramid-UNet (CP-UNet) to suppress erratic and random noises in DAS data. The CP-UNet utilizes the Context Pyramid Module in the encoding and decoding process to extract features and reconstruct the DAS data. To enhance the connectivity between shallow and deep features, we add a Connected Module (CM) to both encoding and decoding section. Layer Normalization (LN) is utilized to replace the commonly employed Batch Normalization (BN), accelerating the convergence of the model and preventing gradient explosion during training. Huber-loss is adopted as our loss function whose parameters are experimentally determined. We apply the network to both the 2-D synthetic and filed data. Comparing to traditional denoising methods and the latest UL framework, our proposed method demonstrates superior noise reduction performance.

翻译：分布式声学传感（DAS）技术利用光纤电缆探测声学信号，提供了经济高效且密集的监测能力。它具有耐极端条件、抗电磁干扰以及探测准确等多项优势。然而，与检波器相比，DAS通常表现出较低的信噪比（S/N），并且容易受到多种噪声类型的影响，例如随机噪声、突发噪声、电平噪声和长周期噪声。这种降低的信噪比可能对包含反演和解释的数据分析产生负面影响。尽管人工智能已展现出优异的去噪能力，但现有方法大多依赖于带标签数据的监督学习，这对标签质量提出了严格要求。为解决此问题，我们开发了一种基于上下文金字塔UNet（CP-UNet）的无标签无监督学习（UL）网络模型，以抑制DAS数据中的突发噪声和随机噪声。CP-UNet在编码和解码过程中利用上下文金字塔模块来提取特征并重建DAS数据。为增强浅层特征与深层特征之间的连通性，我们在编码和解码部分均添加了连接模块（CM）。采用层归一化（LN）替代常用的批归一化（BN），以加速模型收敛并防止训练过程中的梯度爆炸。我们采用Huber损失作为损失函数，其参数通过实验确定。我们将该网络应用于二维合成数据与野外数据。与传统去噪方法及最新的无监督学习框架相比，我们提出的方法展现出更优的降噪性能。