Systematic review of fiber-optic distributed acoustic sensing: advancements, applications, and challenges

Abstract

Distributed Acoustic Sensing (DAS) is an advanced optical fiber technique that uses Rayleigh backscattering to offer real-time monitoring and data collection across a wide range of applications. Despite tremendous progress, no comprehensive review has summarized recent advancements, applications, and challenges with DAS systems across multiple fields. This study aims to bridge this gap by systematically reviewing the most recent research and addressing four essential questions: significant applications, advancements in technology, critical challenges, and the growth of signal processing approaches in DAS. This review uses a systematic approach of identification, screening, eligibility and data abstraction to group findings into six important themes: civil engineering and structural health monitoring, environmental and oceanographic applications, oil and gas industry applications, seismic and geophysical monitoring, telecommunications and infrastructure security, and signal processing and advanced sensing techniques. Our findings indicate that DAS has notably enhanced applications including structural anomaly detection, environmental monitoring, pipeline surveillance, seismic analysis, and telecommunications security. The precision, scalability, and versatility of DAS have been further enhanced by developments in machine learning, noise reduction methods, and hybrid sensing models. This analysis provides practical insights and future research objectives by tackling issues like noise interference and deployment complexity, establishing DAS as a game-changing instrument for transdisciplinary monitoring and real-time data collecting across a range of industries.