Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million ${\mathrm{m}}^3$ on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over $95\%$. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

中文翻译：

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使用分布式声学传感和半监督学习自动监测岩坡破坏


有效利用分布式声学传感 (DAS) 提供的大量信息来监测群众运动仍然是一个挑战。我们提出了一种半监督神经网络，专门用于筛选与一系列岩石崩塌相关的 DAS 数据，这些崩塌导致了大约 120 万次重大故障${\mathrm{米}}^3$ 2023 年 6 月 15 日在瑞士东部布里恩茨举行。除了 DAS 之外，2023 年 5 月 16 日至 6 月 30 日的数据集还包括用于部分地面实况标记的多普勒雷达数据。该算法能够区分岩石边坡破坏和背景噪声（包括道路和火车交通），检测精度超过$95\%$ 。它识别了数百个先兆故障，并显示了在重大崩溃之前和期间持续检测的几个小时。事件大小和信噪比 (SNR) 是关键的性能依赖性。由于我们算法的关键部分是在无人监督的情况下运行的，因此我们建议它适用于自然灾害的一般监测。