High Sensitivity Differential Phase OTDR for Acoustic Signals Detection

Tomboza, Wendy; Guerrier, Sterenn; Awwad, Élie; Dorize, Christian

doi:10.1109/lpt.2021.3084557

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…The joint group from different institutions (Tomboza et al) [ 176 ] recorded human speech by probing a coherent source with pulsed radiation in two polarization states, in both cases with different Golay sequences ( Figure 24 ).…”

Section: Acoustic Sensing and Das In Humanitarian Sciences And Cultur...mentioning

confidence: 99%

“…LW—linewidth, Mod—modulator, FUT—fiber under test, BPD—balanced photodetector, MIMO—multiple input multiple outputs. Adapted from [ 176 ].…”

Section: Figurementioning

confidence: 99%

“… Blue line—the original signal of the human voice, saying “Bonjour,” brown color—the detected phase. The waveform is repeated by the review authors, please find the original waveform in [ 176 ]. …”

Section: Figurementioning

confidence: 99%

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Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

Gorshkov

Yüksel

Fotiadi

et al. 2022

Sensors

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This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

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Section: Acoustic Sensing and Das In Humanitarian Sciences And Cultur...mentioning

confidence: 99%

“…LW—linewidth, Mod—modulator, FUT—fiber under test, BPD—balanced photodetector, MIMO—multiple input multiple outputs. Adapted from [ 176 ].…”

Section: Figurementioning

confidence: 99%

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Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

Gorshkov

Yüksel

Fotiadi

et al. 2022

Sensors

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show abstract

“…In the past decade, there have been many successful applications of DAS in the field of geophysical detection, such as vertical seismic profile (VSP) acquisition [ 10 , 11 , 12 ], hydraulic fracturing monitoring [ 13 , 14 , 15 ], earthquake observation [ 16 , 17 , 18 ], and structural detection and imaging [ 19 , 20 , 21 ]. In the fields of acoustics [ 22 , 23 ] and biology [ 24 , 25 ], DAS has also shown its powerfulness. With the rapid development of demodulation techniques, the applications of DAS have gradually extended from land to ocean [ 26 , 27 , 28 ], glaciers [ 29 , 30 ], and volcanoes [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Zhu

Liu

Wang

et al. 2022

Sensors

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Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

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“…Phase Sensitive Optical Time Domain Reflectometer( -OTDR) is a typical distributed optical fiber sensor system that can detect and locate external mechanical vibration for a long distance [1]. It is widely used in many fields, such as long-distance cable breakage monitoring, structural health monitoring, voice signal and human footprint detection, and city-wide behavior monitoring [2]- [5]. In the early researches, researchers have made great efforts to optimize the performance parameters of the system, such as spatial resolution, sensitivity, dynamic range, signal-to-noise ratio and sensing dimensions [6]- [11].…”

Section: Introductionmentioning

confidence: 99%

A Recognition Method for Multi-Radial-Distance Event of Φ-OTDR System Based on CNN

et al. 2021

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This paper proposes a multi-radial-distance event classification method based on deep learning. To the best of our knowledge, this is the first time that the -OTDR can tell how far the target event from the sensing fiber is through deep learning approach. The temporal-spatial data matrix collected by the system is filtered by three different band-pass filters to form RGB images as the input of the Inception_V3 network trained by ImageNet dataset. The passband of three band-pass filters is selected by searching the maximum Euclidean distance in the frequency domain. Three kinds of filters with different frequency bands enhance the effective features of data samples in advance. The simulated annealing (SA) algorithm is applied to search the maximum Euclidean distance. Field experiment includes five kinds of events with four different radial distances, where there are 17 subclasses in total, has been carried out. The classification results show that the classification accuracy reaches 86% and the method can tell both the event type and radial distance.

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High Sensitivity Differential Phase OTDR for Acoustic Signals Detection

Cited by 23 publications

References 8 publications

Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

A Recognition Method for Multi-Radial-Distance Event of Φ-OTDR System Based on CNN

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