We propose and experimentally demonstrate a novel interference fading suppression method for phase-sensitive optical time domain reflectometry (φ-OTDR) using space-division multiplexed (SDM) pulse probes in a few-mode fiber. The SDM probes consist of multiple different modes, and three spatial modes (LP01, LP11a, and LP11b) are used in this work for the proof of concept. Firstly, the Rayleigh backscattering light of different modes is experimentally characterized, and it turns out that the waveforms of the φ-OTDR traces for distinct modes are all different and independent. Thanks to the spatial difference of the fading positions for distinct modes, multiple probes from spatially multiplexed modes can be used to suppress the interference fading in φ-OTDR. Then, the performances of the φ-OTDR systems using a single probe and multiple probes are evaluated and compared. Specifically, the statistical analysis shows that the fading probabilities over both the fiber length and the time scale are reduced significantly by using multiple SDM probes, which verifies the significant performance improvement on fading suppression. By introducing the concept of SDM to φ-OTDR, the proposed novel interference fading suppression method avoids the complicated frequency or phase modulation, which has the advantages of simplicity, good effectiveness and high reliability.

Distributed acoustic sensors (DASs) have the capability of registering faint vibrations with high spatial resolution along the sensing fiber. Advanced algorithms are important for DAS in many applications since they can help extract and classify the unique signatures of different types of vibration events. Deep convolutional neural networks (CNNs), which have powerful spectro-temporal feature learning capability, are well suited for event classification in DAS. Generally, these data-driven methods are highly dependent on the availability of large quantities of training data for learning a mapping from input to output. In this work, to fully utilize the collected information and maximize the power of CNNs, we propose a method to enlarge the useful dataset for CNNs from two aspects. First, we propose an intensity and phase stacked CNN (IP-CNN) to utilize both the intensity and phase information from a DAS with coherent detection. Second, we propose to use data augmentation to further increase the training dataset size. The influence of different data augmentation methods on the performance of the proposed CNN architecture is thoroughly investigated. The experimental results show that the proposed IP-CNN with data augmentation produces a classification accuracy of 88.2% on our DAS dataset with 1km sensing length. This indicates that the usage of both intensity and phase information together with the enlarged training dataset after data augmentation can greatly improve the classification accuracy, which is useful for DAS pattern recognition in real applications.

Water distribution networks (WDNs) are essential infrastructure for providing fresh water to communities, but detecting leaks for WDNs is challenging and costly. In this article, we propose a novel solution that combines an optical network and WDN for distributed water pipe leak detection. Our approach involves using a standard outdoor fiber-optic cable for distributed vibration measurement along a 40-meter water pipe. To accurately identify and locate leaks, we introduce a leak identification algorithm based on 3Dconvolutional neural networks (3D-CNNs) that consider the temporal, spectral, and spatial information. Additionally, we propose a leak quantification method that can help prioritize repairs based on the severity of the leak. We evaluate our scheme for different conditions and find that it can detect leak flow rates as low as 0.027 L/s with a location accuracy of within 3 meters and a quantification accuracy of over 85%. Our proposed method offers a cost-effective and valueadded solution for designing optical networks and WDNs in new development areas.

Distributed acoustic sensing (DAS) is an attractive technology that can turn existing fibre optic networks to real-time distributed vibration sensors. However, it encounters great challenges in handling the transmission, storage, and processing of voluminous data streams which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by φ-OTDR and modern computing system with limited reading/writing speed and storage capacity leads to 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. The data size is greatly reduced in the sampling stage, but the reconstruction of the compressed data is still 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 method can be used to reduce the transmitted data size by 70% while achieves a true positive rate (TPR) of 99.4% and a false positive rate (FPR) of 0.04% of positioning along 5-km-long sensing fibre and a classification accuracy of 95.05% on a 5-class classification task.

Rayleigh scattering-based distributed optical fiber sensors with long sensing distance and large dynamic range are highly desired for application scenarios such as vehicle tracking, structure health monitoring, and geological survey. To enlarge the dynamic range, we propose a coherent optical time domain reflectometry (COTDR) based on double-sideband linear frequency modulation (LFM) pulse. By utilizing I/Q demodulation, both the positive and negative frequency band of the Rayleigh backscattering (RBS) signal can be properly demodulated. Consequently, the dynamic range is doubled without increasing the bandwidth of signal generator, photodetector (PD), and oscilloscope. In the experiment, the chirped pulse with 10 μs pulse width and 498 MHz frequency sweeping range is launched into the sensing fiber. Single-shot strain measurement is achieved over 5 km single-mode fiber with a spatial resolution of 2.5 m and a strain sensitivity of 7.5 pε/Hz. A vibration signal with 3.09 με peak-to-peak amplitude (corresponding to 461 MHz frequency shift) is successfully measured with the double-sideband spectrum, which cannot be properly recovered with the single-sideband spectrum.