Fiber Optic Sensing in Rain Detection Using Unsupervised Domain Adaptation

Abstract

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. DAS transforms existing optical fiber cables into distributed sensornetworks, which detects external perturbations, such as vibrations, and acoustic pulses, along the fiber by utilizing the coherent Rayleighbackscattering. Each fiber segment with a length of spatial resolution can be considered a point sensor that enables continuous, real-time measurements across the entire route. Prior studies have demonstrated the potential of DAS in detecting and classifying rain intensity under controlled laboratory conditions. Building upon this research, further work expanded the application of DAS to real-world environments, employing pre-trained supervised Convolutional Neural Networks (CNNs) for field trials in rain detection and classification. However,existing research primarily focus on environments with consistent, labeled datasets and often struggle to adapt to variable environmental conditions. 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.