Fiber Optic Sensing in Rain Detection Using Unsupervised Domain Adaptation

Traditional rain detection methods are often constrained by their limited spatial coverage and lack of adaptability in diverse environmental conditions. This gap highlights the need for more advanced, adaptable solutions for rain detection. Recent advances in distributed acoustic sensing (DAS) have shown potential for overcoming these limitations. Using DAS, we propose a CNN with unsupervised domain adaptation technique that utilizes already-laid optical fiber networks for rain intensity classification. Specifically, firstly we collected the field data from a live telecommunication network using DAS underdifferent field environment, which was recorded over eight different days spanning five months. After filtering and feature extraction, a Deep Reconstruction Classification Network (DRCN) is implemented to concurrently minimize both the classification loss and reconstruction lossduring the learning process, aiming to capture a domain adaptive feature representa- tion applicable to new environmental conditions. Experimental results show that our approach effectively identifies rain status and adapts well to rain intensity classification across newdomains, addressing the gap left by machine learning methods in the context of unlabeled field data. This adaptability is crucial fordeveloping more accurate and reliable rain detection systems that capable of functioning effectively across various domains.

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Recommended citation: Ding, Y., Shi, S., Tian, Y., Jiang, Z., Ozharar, S., Wang, T., and Moore, J.: Fiber Optic Sensing in Rain Detection Using Unsupervised Domain Adaptation, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-87, https://doi.org/10.5194/egusphere-gc12-fibreoptic-87, 2024.