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Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data | IEEE Conference Publication | IEEE Xplore

Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data


Abstract:

We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fi...Show More

Abstract:

We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.
Date of Conference: 03-06 July 2022
Date Added to IEEE Xplore: 17 August 2022
ISBN Information:
Conference Location: Toyama, Japan
References is not available for this document.

I. Introduction

Distributed acoustic sensor (DAS) is an interrogator that detects the phase change of Rayleigh backscattered light in the gauge length traveling in an optical fiber, based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) technology [1]. Due to a large number of measuring points, DAS is mainly applied for large-scale monitoring, such as pipeline safety monitoring [2], seismic monitoring [3], and traffic monitoring [4]. In addition to these vibration detections, the detection technology with the highly sensitive optical-fiber mandrels for acoustics in the air is also being developed, such as the acoustic-source localization based on the array-signal processing [5]. Thus, DAS has the potential to turn existing global telecom fibers into huge acoustic sensor networks.

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1.
Z. Wang, “Recent Progress in Distributed Fiber Acoustic Sensing with Φ-OTDR,” Sensors, 20, 6594 ( 2020 ).
2.
H. Wu, “A Dynamic Time Sequence Recognition and Knowledge Mining Method Based on the Hidden Markov Models (HMMs) for Pipeline Safety Monitoring With Φ. OTDR,” J. Lightwave Technol, 37, 4991–5000 ( 2019 ).
3.
T. Daley, “Field testing of fibre-optic distributed acoustic sensing (DAS) for sub-surface seismic monitoring,” Leading Edge 36, 936.942 ( 2013 ).
4.
M. Huang, “First Field Trial of Distributed Fiber Optical Sensing and High-Speed Communication Over an Operational Telecom Network,” J. Lightwave Technol, 38 ( 1 ), 75. 81 ( 2020 ).
5.
J. Liang, “Distributed acoustic sensing for 2D and 3D acoustic source localization,” Opt. Lett, 44, 1690–1693 ( 2019 ).
6.
SEAFOM. DAS parameter defnitions and tests. SEAFOM MSP-02. ( 2018 ).

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References

References is not available for this document.