Distributed acoustic sensing (DAS), deployed on dark telecom fiber, is well‐positioned to play a significant role in seismic monitoring networks because of the combination of a large aperture, fine spatial resolution, broadband sensitivity, and the ubiquitous presence of unused telecommunication fibers in many areas of the world. In this study, we explore the feasibility of dark‐fiber array deployed in a noisy environment for detecting small explosions. We test the effectiveness of template matching for the detection of low‐frequency blasts generated by mining activities in the Imperial Valley, California. We first evaluate dark‐fiber detection performance by analyzing the relationship between detection threshold (DT) and the number of DAS channels used. We find that although, as expected, increasing the number of channels yields higher detection significance and lowers DT, the gain in performance is far from linear, with local anomalies across the DAS cable associated with zones of higher noise. We focus on investigating the types of noise affecting template matching and practical approaches mitigating anthropogenic noise that lower detection performance. Using median absolute deviation, we identify two types of noise sources affecting detection performance. Next, we design a voting scheme that selects DAS channels contributing to lowering of the DT and ensures improvement in detection when adding sequential channels. Finally, we compare dark‐fiber detection performance with nearby conventional seismometers and find that a single station can outperform up to ∼10 DAS channels. However, using the full aperture of our dark‐fiber transect allows to obtain ∼10% lower DT and yields fewer false‐positive detections than an array of four seismometers. Methodological solutions for noise assessment and channel selection allow us to fully benefit from the large aperture and dense sampling offered by dark fiber. The findings of this study are a step toward incorporating existing telecom fibers into novel explosion‐monitoring workflows.

中文翻译：

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在噪声环境中使用大 N 暗光纤进行低频爆炸检测：模板匹配和最佳通道选择

部署在暗电信光纤上的分布式声学传感 (DAS) 凭借大孔径、精细空间分辨率、宽带灵敏度以及无处不在的未使用电信光纤的结合，在地震监测网络中发挥着重要作用。在世界许多地区。在这项研究中，我们探讨了在嘈杂环境中部署暗光纤阵列来检测小型爆炸的可行性。我们测试了模板匹配用于检测加利福尼亚州帝王谷采矿活动产生的低频爆炸的有效性。我们首先通过分析检测阈值（DT）和所使用的 DAS 通道数量之间的关系来评估暗光纤检测性能。我们发现，尽管正如预期的那样，增加通道数量会产生更高的检测意义并降低 DT，但性能增益远非线性，DAS 电缆上的局部异常与较高噪声区域相关。我们专注于研究影响模板匹配的噪声类型以及减轻降低检测性能的人为噪声的实用方法。使用中值绝对偏差，我们识别出影响检测性能的两种类型的噪声源。接下来，我们设计了一种投票方案，选择有助于降低 DT 的 DAS 通道，并确保在添加顺序通道时改进检测。最后，我们将暗光纤探测性能与附近的传统地震仪进行了比较，发现单个站的性能可以优于多达 ∼10 个 DAS 通道。然而，与四个地震仪阵列相比，使用我们的暗光纤横断面的全孔径可以获得约 10% 的低 DT，并且产生更少的误报检测。噪声评估和通道选择的方法解决方案使我们能够充分受益于暗光纤提供的大孔径和密集采样。这项研究的结果是将现有电信光纤纳入新型爆炸监测工作流程的一步。