In recent years, pattern recognition technologies for distributed optical fiber vibration sensing have attracted more and more attention, aiming to intelligently recognize vibration events along with the optical fiber. Firstly, distributed optical fiber sensors for vibration detection are introduced. Secondly, this paper presents the state of the art of pattern recognition models used in distributed optical fiber vibration sensing systems. The feature extraction method, the model structure, and the processing performance are reported. As the results of the comparison, the support vector machine is a small sample learning method with a solid theoretical foundation and it has excellent generalization ability. The artificial neural network is suitable for massive data learning and multi-classification problems. Also, deep learning can learn more features information by a deep nonlinear network structure in an automated way, and thus has better accuracy and robustness. Furthermore, different applications of pattern recognition for distributed optical fiber vibration sensing are provided, including perimeter security, pipeline monitoring, and railway safety monitoring. Finally, the prospects of pattern recognition for distributed optical fiber vibration sensing are discussed.

中文翻译：

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分布式光纤振动传感的模式识别：综述

近年来，用于分布式光纤振动感测的模式识别技术越来越受到关注，其目的是与光纤一起智能地识别振动事件。首先，介绍了用于振动检测的分布式光纤传感器。其次，本文介绍了分布式光纤振动传感系统中使用的模式识别模型的最新技术。报告了特征提取方法，模型结构和处理性能。作为比较的结果，支持向量机是一种小样本学习方法，具有扎实的理论基础，并且具有出色的泛化能力。人工神经网络适用于海量数据学习和多分类问题。还，深度学习可以通过深度的非线性网络结构以自动化的方式学习更多的特征信息，因此具有更好的准确性和鲁棒性。此外，还提供了模式识别在分布式光纤振动感测中的不同应用，包括周界安全性，管道监控和铁路安全监控。最后，讨论了分布式光纤振动传感模式识别的前景。