Multi-Source Separation Under Two Blind Conditions for Fiber-Optic Distributed Acoustic Sensor

Significant progress has been made in single source recognition for fiber-optical distributed acoustic sensor (DAS). However, it is still challenging to detect and identify more than one unpredictable vibration sources when they are superimposed at the same fiber receiving point. Thus, in this paper it is proposed a blind multi-source separation method based on fast independent component analysis (FastICA), which utilizes the independency and non-Gaussianity of different sources. Firstly, two multi-source mixing mechanisms and separability of different sources received by DAS based on Phi-OTDR are discussed; to solve the two blind problems that the source number and the mixing mode are both unknown, a linear simultaneous mixing mode is assumed, and the source number is estimated by singular value decomposition to the observation matrix; then preprocessing of denoising and anti-mixing, and separation with FastICA by maximizing negative entropy are carried out to make the non-Gaussianity of the estimated signal achieve its maximum; finally, feasibility of the separation method is evaluated through several mixing cases including simulations with two to four field collected signals and a real field test with two sources superimposed on the buried fiber. Signal waves and the spectra, and three separation indicators, such as the Performance Index (PI), the signal correlation coefficients, and the signal mean square error (SMSE), are used to evaluate the performance of the method. As far as we know, it is the first time to realize the separation of an unknown number of the superimposed sources detected by DAS.

Automated summary

In this paper, a blind multi-source separation method based on fast independent component analysis (FastICA) is proposed for fiber-optical distributed acoustic sensor (DAS). The method utilizes the independency and non-Gaussianity of different sources to solve the challenge of detecting and identifying unpredictable vibration sources when they are superimposed at the same fiber receiving point. The method includes discussions on multi-source mixing mechanisms and separability, linear simultaneous mixing mode assumption, estimation of source number, preprocessing of denoising and anti-mixing, separation with FastICA by maximizing negative entropy, and evaluation of the method's feasibility through simulations and real field tests.