Application of machine learning for signal recognition in distributed fibre optic acoustic sensing technology

Abstract

Coherent Rayleigh scattering-based distributed fibre optic sensing technology enables real-time acquisition of vibration and acoustic information along the optical fibres. However, the complexity of monitoring environments often leads to false alarms and missed detections during the process of information source identification with distributed acoustic sensing (DAS). Therefore, it becomes crucial to effectively extract meaningful signal features and perform accurate pattern recognition in the presence of external noise disturbance. The authors provide a comprehensive review of signal feature extraction and pattern recognition techniques applied in DAS technology. After introducing the fundamentals of DAS, specific applications are considered, and the following techniques have been analysed and compared: feature extraction algorithms based on wavelet decomposition, feature extraction schemes utilising other decomposition models, traditional recognition classifiers, and neural network-based recognition classifiers using deep learning. The advantages and limitations of each scheme are discussed, along with their potential applications in various scenarios. The aim is to provide insights into the latest technologies in signal processing and pattern recognition for DAS, fostering further advancements in this field.