Compressed domain vibration detection and classification for distributed acoustic sensing

TL;DR

This paper introduces a compressed-domain vibration detection and classification framework for distributed acoustic sensing that significantly reduces data transmission needs while maintaining high accuracy and detection performance.

Contribution

The novel approach maps feature extraction to the compressed domain, enabling direct vibration detection and classification without full data reconstruction.

Findings

Reduced data transmission by 70%

Achieved 99.4% true positive rate and 0.04% false positive rate

Attained 95.05% classification accuracy on a 5-class task

Abstract

Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors. However, it faces the challenges of transmitting, storing, and processing massive streams of data which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by DAS and modern computing system with limited reading/writing speed and storage capacity imposes restrictions on many applications. Compressive sensing (CS) is a revolutionary signal acquisition method that allows a signal to be acquired and reconstructed with significantly fewer samples than that required by Nyquist-Shannon theorem. Though the data size is greatly reduced in the sampling stage, the reconstruction of the compressed data is however time and computation consuming. To address this challenge, we propose to map the feature extractor from Nyquist-domain to compressed-domain and therefore vibration detection and classification can be directly implemented in compressed-domain. The measured results show that our framework can be used to reduce the transmitted data size by 70% while achieves 99.4% true positive rate (TPR) and 0.04% false positive rate (TPR) along 5 km sensing fibre and 95.05% classification accuracy on a 5-class classification task.