Continuous, real-time monitoring of surface seismic activity around the globe is of great interest for acquiring new insight into global tomography analyses and for recognition of seismic patterns leading to potentially hazardous situations. The already-existing telecommunication fiber optic network arises as an ideal solution for this application, owing to its ubiquity and the capacity of optical fibers to perform distributed, highly sensitive monitoring of vibrations at relatively low cost (ultra-high density of point sensors available with minimal deployment of new equipment). This perspective article discusses early approaches on the application of fiber-optic distributed acoustic sensors (DASs) for seismic activity monitoring. The benefits and potential impact of DAS technology in these kinds of applications are here illustrated with new experimental results on teleseism monitoring based on a specific approach: the so-called chirped-pulse DAS. This technology offers promising prospects for the field of seismic tomography due to its appealing properties in terms of simplicity, consistent sensitivity across sensing channels, and robustness. Furthermore, we also report on several signal processing techniques readily applicable to chirped-pulse DAS recordings for extracting relevant seismic information from ambient acoustic noise. The outcome presented here may serve as a foundation for a novel conception for ubiquitous seismic monitoring with minimal investment.

There is an increasing interest in researchers and companies on the combination of Distributed Acoustic Sensing (DAS) and a Pattern Recognition System (PRS) to detect and classify potentially dangerous events that occur in areas above fiber optic cables deployed along active pipelines, aiming to construct pipeline surveillance systems. This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other environment in which DAS+PRS strategies are to be used). To do so, we describe the fundamentals of the machine learning approaches when applied to DAS systems, and also do a detailed literature review of the main contributions on this topic. Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations in what respect to the design of such systems. The literature review concludes that a real field deployment of a PRS based on DAS technology is still a challenging area of research, far from being fully solved.

Distributed optical fiber Brillouin sensors provide innovative solutions for the monitoring of temperature and strain in large structures. The effective range of these sensors is typically of the order of 20-30 km, which limits their use in certain applications in which the distance to monitor is larger. In this work, we have developed a new technique to significantly extend the measurement distance of a distributed Brillouin Optical Time-Domain Analysis (BOTDA) sensor. Distributed Raman Amplification in the sensing fiber provides the means to enhance the operating range of the setup. Three Raman pumping configurations are theoretically and experimentally investigated: co-propagating, counter-propagating and bidirectional propagation with respect to the Brillouin pump pulse. We show that some of the amplification schemes tested can extend the measurement range and improve the measurement quality over long distances.

In this work, we present field tests concerning the application of fiber Bragg grating (FBG) sensors for the monitoring of railway traffic. The test campaigns are performed on the Spanish high-speed line Madrid-Barcelona, with different types of trains (S-102 TALGO-BOMBARDIER, S-103 SIEMENS-VELARO, and S-120 CAF). We located the FBG sensors in the rail track at 70 km from Madrid in the country side, where the trains primarily are tested during commercial operation with maximum speeds between 250-300 km/h. The FBG sensor interrogation system used allows the simultaneous monitoring of four FBG sensors at 8000 samples/s. The different position of the FBG sensors in relation with the rail can be used for different purposes such as train identification, axle counting, speed and acceleration detection, wheel imperfections monitoring, and dynamic load calculation.

Distributed acoustic sensing (DAS) delivers real-time observation ofphysical perturbations such as vibrations or strain variations in conventional optical fibers with high sensitivity. The high density of sensing points and large network footprint provided by a single DAS system, along with the availability of a vast optical fiber network already deployed both in land and in oceanic regions, contrast with the high deployment and maintenance cost of conventional instrumentation networks for seismology. This situation has triggered a rapid growth of DAS deployments for seismic monitoring in recent years. Photonic engineers and geophysicists have joined efforts to prove the value of optical fibers as distributed seismometers, which has resulted in a wide panoply of tests demonstrating diverse applicability across the geosciences. For example, DAS has been successfully applied recording local to teleseismic earthquakes, monitoring glacial icequakes, and observing oceanographic phenomena at the sea floor. Most of the realized tests have been performed using commercially available optical fiber interrogators based on phase-sensitive optical time-domain reflectometry. Among them, DAS based on chirped pulse distributed acoustic sensing have provided optimized performance in terms of both range and sensitivity, particularly at low frequencies. In this communication, we provide a comprehensive review of the current situation of DAS for seismology applications, focusing on near surface monitoring, where already deployed optical fibers can be repurposed as sensor networks.

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.