# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 1 Introduction
utilized DAS systems to achieve meter-level spatial resolution for pinpointing gas pipeline leak points. Their study also explored how leak hole size and leak direction impact the spectral characteristics of leak signals [11]. However, none of the aforementioned works in the literature delved into the discussion of leak event recognition accuracy. In practice, while achieving leak localization is feasible, false positives and false negatives remain significant challenges for DAS systems. Initially, researchers relied on threshold settings for accurate vibration event identification. In 2014, [PERSON] et al. set a level threshold to determine the event category [12]. In 2018, [PERSON] et al. used the amplitude of the vibration signal as the basis for judging whether there was a vibration event by setting a threshold and achieved a recognition accuracy of 95.57% for a specific \"instantaneous destructive disturbance\" in a laboratory environment [13]. However, pattern recognition is only performed through thresholds, and its recognition accuracy and stability are poor in practical application scenarios. It is difficult to ensure the stability of the signal-to-noise ratio of the received signal due to the attenuation of the signal in the process of long-distance transmission, coupled with the influence of coherent fading and polarization fading, which leads to the inaccuracy of the frequency characteristics demodulated. The disturbance signals on the optical fiber are intricately intertwined, and even the vibration signals that need to be detected are dynamically changing, which further leads to a reduction in the recognition accuracy of the system. Pattern recognition through machine learning is a popular solution. By analyzing existing data and selecting appropriate algorithms, a model can be built and continuously optimized, and finally, various events can be identified quickly and accurately. The concept of machine learning was first proposed by the American computer scientist [PERSON] in 1959 as a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to enable AI to mimic human learning methods and gradually improve its recognition accuracy [14]. In 1998, [PERSON] et al. proposed a model architecture for convolutional neural networks (CNNs) and applied them to the task of handwriting number recognition [15]. The LeNet-5 model proposed by [PERSON] achieved excellent performance on the MNIST dataset, marking the birth of deep learning and achieving more complex feature extraction. In 2015, [PERSON] et al. proposed the ResNet18 neural network, which solves the gradient vanishing problem that may occur when the number of layers in traditional CNN networks increases [16]. The emergence of these models provides a strong guarantee for the accurate realization of pattern recognition. In this paper, a coherent \(\varphi\)-OTDR system is employed to capture vibration signal phase information and a pre-trained neural network model is responsible for processing this information to accurately identify leakage events and locations. The proposed method is evaluated in a simulated water pipelines setup. Phase information from both leaking and non-leaking pipe segments is collected to create separate datasets for training different models: CNN and ResNet18. During practical testing, the test accuracy values of the CNN model and ResNet18 model achieve 99.7% and 99.5%, respectively. What is more, in the multi-leakage point detection experiment, the ResNet18 model performs better, which indicates that Resnet18 has better generalization ability compared to the CNN model.