Distributed Acoustic Sensing Data Pattern Recognition with a Short-Time Fourier Transform Downsampling Module
Affiliation:

1.西交利物浦大学;2.Nanjing Poineer Awareness Information Technology CO..LTD

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    Abstract:

    Distributed acoustic sensing technology is widely used in seismic monitoring, intrusion detection, and other fields due to its advantages of wide monitoring range and low cost. However, the problems of complex signal processing and large data volume limit its application. This paper proposes a downsampling method based on Short-Time Fourier Transform, which reduces the length of the time series while retaining high-frequency information. Experiments show that this method improves the efficiency and classification performance of the model, with an F1 value of 0.9147 on a four-class private dataset and an accuracy of 0.9944 on a two-class public dataset.

    Reference
    [1] PAPP A, WIESMEYR C, LITZENBERGER M, et al. Train detection and tracking in optical time domain reflectometry (OTDR) signals[C]//Pattern Recognition: 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings 38. Springer, 2016: 320-331.
    [2] KAYAN C E, YUKSEL ALDOGAN K, GUMUS A. Intensity and phase stacked analysis of a Φ-OTDR system using deep transfer learning and recurrent neural networks[J]. Applied Optics, 2023, 62(7): 1753-1764.
    [3] ZHANG Y, YU M, CHANG T, et al. Phase-sensitive Optical Time-domain Reflectometric System Pattern Recognition Method Based on Wavenet[J]. Acta Photonica Sinica, 2021, 50(3): 0306003.
    [4] JIANG W, YAN C. High-accuracy classification method of vibration sensing events in φ-OTDR system based on Vision Transformer[C]//2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2024: 1704-1709.
    [5] DOSOVITSKIY A. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020.
    [6] WU H, ZHOU B, ZHU K, et al. Pattern recognition in distributed fiber-optic acoustic sensor using an intensity and phase stacked convolutional neural network with data augmentation[J]. Optics express, 2021, 29(3): 3269-3283.
    [7] SHI Y, CHEN J, KANG X, et al. An Φ-OTDR event recognition method based on Transformer[C]//2023 21st International Conference on Optical Communications and Networks (ICOCN). IEEE, 2023: 1-3.
    [8] WU H, WANG Y, LIU X, et al. Smart Fiber-Optic Distributed Acoustic Sensing (sDAS) With Multi-Task Learning for Time-Efficient Ground Listening Applications[J]. IEEE Internet of Things Journal, 2023.
    [9] SHI Y, LI Y, ZHANG Y, et al. An easy access method for event recognition of Φ-OTDR sensing system based on transfer learning[J]. Journal of Lightwave Technology, 2021, 39(13): 4548-4555.
    [10] SUN Z, LIU K, XU T, et al. Intelligent sensing analysis using mel-time-frequency-imaging and deep learning for distributed fiber-optic vibration detection[J]. IEEE Sensors Journal, 2022, 22(22): 21933-21941.
    [11] LIU M, WANG X, LIANG S, et al. Single and composite disturbance event recognition based on the DBN-GRU network in ?-OTDR[J/OL]. Appl. Opt., 2023, 62(1): 133-141. https://opg.optica.org/ao/abstract.cfm?URI=ao-62-1-133.
    [12] GE Z, WU H, ZHAO C, et al. High-accuracy event classification of distributed optical fiber vibration sensing based on time–space analysis[J]. Sensors, 2022, 22(5): 2053.
    [13] XU G, LIAO W, ZHANG X, et al. Haar wavelet downsampling: A simple but effective downsampling module for semantic segmentation[J]. Pattern Recognition, 2023, 143: 109819.
    [14] SANDLER M, HOWARD A, ZHU M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.
    [15] WADA K. Labelme: Image Polygonal Annotation with Python[EB/OL]. https://github.com/wkentaro/labelme.
    [16] Shi Y, Li Y H, Zhang Y C, et al. An easy access method for, event recognition of Φ-OTDR sensing system based on transfer, learning[J]. Journal of Lightwave Technology, 2021, 39(13):, et al. Algoritmy strojového u?ení pro zpracování událostí z fázově citlivého OTDR[D]. Masaryk University, 2024.
    [17] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.
    [18] ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2921-2929.
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History
  • Received:December 16,2024
  • Revised:February 24,2025
  • Adopted:March 11,2025
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