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.

Photonic integrated sensing and communication technology based on optical fibers has recently attracted great research attention. In particular, distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of geology and seismology. With the massive deployment of telecommunication submarine cables, it provides an effective and low-cost approach for studying various oceanic geological and seismic activities. Though there have been some demonstrations of using DAS with submarine cables for the oceanic and geological analysis, no systematic analysis of the method has been presented, especially the signal processing techniques employed for the study. In this work, we discuss the signal processing procedure adopted to carry out seismic analysis using data obtained with the underwater DAS in detail, including the preprocessing of raw DAS data, the seismic recognition after feature extraction and event classification, and the seismic analysis of localization and magnitude estimations. Based on these signal processing methods, we successfully detect and analyze different seismic activities with our underwater DAS testbed, which utilizes the telecommunication submarine cable between two islands in the Pearl River estuary area of South China Sea.

Instantaneous frequency measurement (IFM) of microwave signals using photonic methods provides a novel and efficient approach for fast and broadband radio frequency (RF) signal analysis. Here, we propose and experimentally demonstrate a photonic-assisted IFM method utilizing a few-mode fiber-based microwave photonic technique. By offset splicing the few-mode fiber (FMF) with a single mode fiber, both LP01 and LP11 modes can be excited, which is used to develop a microwave photonic filter (MPF). A detailed analysis of the FMF as the true time delay line is presented. An amplitude comparison function (ACF) that is the ratio of frequency response traces of an MPF pair is established, which is used to determine the unknown microwave frequency instantaneously. The proof-of-concept experiment demonstrates a frequency measurement range of 0.5 GHz to 17.5 GHz and a measurement accuracy of ±0.2 GHz in most of the frequency points. The proposed system has the merits of simplicity, cost-effectiveness, compactness and robustness.

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.