Integrated principal component analysis denoising technique for phase-sensitive optical time domain reflectometry vibration detection

Abstract

This paper presents an integrated principal component analysis (IPCA) technique for denoising phase-sensitive optical time domain reflectometry ( Φ -OTDR) sensing data for vibration detection. As one of the key distributed optical fiber sensing technologies, it has attracted great attention, mainly due to its high sensitivity, fast response time, dynamic range, and vibration detection abilities. To enhance vibration detection along the sensing fiber, an appropriate denoising method must be carefully selected. Hence, the PCA that can effectively reduce noise on signals while preserving significant details of the denoised signal is identified. It was then applied on the said signal after digital down-conversion where the noise was greatly reduced. Then angle and phase unwrapping was performed and the vibration was clearly detected with a significant enhancement of the signal-to-noise ratio. As proof of concept, the theoretical analysis and an experimental demonstration of a vibration sensing range of 800 m are presented.

Real-time and post-hoc compression for data from Distributed Acoustic Sensing

“…Convolutional neural networks (CNN) (Liehr et al, 2020), principal component analysis (PCA) (Ibrahim et al, 2020), Curvelet (Qin et al, 2017), and other methods (Meng et al, 2019;Soto et al, 2016) have been applied to reduce noise in DAS datasets (Liehr et al, 2020). These de-noising methods can be regarded as lossy compression methods designed to reduce the cost or improve quality of specific analysis procedures.…”

A unified wavefield-partitioning approach for distributed acoustic sensing

“…Previous efforts to remove stochastic noise from DAS data have been varied and proposed both outside of and within seismology. Many of these efforts have successfully applied time-space analysis techniques from signal processing such as wavelet transforms (Qin et al 2012), 2-D edge detection (Zhu et al 2013), 2-D bilateral filters (He et al 2017), empirical mode decomposition (Qin et al 2017b) and principal component analysis (Ibrahim et al 2020). In particular, Qin et al (2017a) proposed an approach to remove random noise in the curvelet domain.…”