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  1. Home
  3. Deep Learning-based Phase Demodulation For Distributed Acoustic Sensor.
  1. Home
  3. Deep Learning-based Phase Demodulation For Distributed Acoustic Sensor.

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Deep learning-based phase demodulation for distributed acoustic sensor.

Yiming Tang1,2,3, Kewei Liu1,2, Chen Liu1,2,3

  • 1College of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China.

Scientific Reports
|August 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning algorithm accelerates phase demodulation for distributed acoustic sensing (DAS) data. This method significantly speeds up processing while maintaining accuracy, improving fiber optic sensing performance.

Keywords:
Deep learningDistributed acoustic sensor (DAS)Phase demodulationSignal processing

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Area of Science:

  • Optical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Deep learning is increasingly applied to fiber optic sensing, yet raw data demodulation remains a challenge.
  • Accurate demodulation is vital for understanding physical processes and optimizing fiber optic sensing systems.
  • Traditional demodulation methods, like the Hilbert transform, are computationally intensive.

Purpose of the Study:

  • To develop a deep learning-based phase demodulation algorithm for distributed acoustic sensor (DAS) data.
  • To replace computationally expensive traditional demodulation techniques with a faster, deep learning approach.
  • To enable intuitive, real-time graphical display of demodulated results.

Main Methods:

  • A novel phase demodulation algorithm was designed utilizing a deep learning framework.
  • The algorithm processes raw data from distributed acoustic sensing (DAS) systems.
  • The approach was tested on a dataset of 2000 rows and 4500 columns at a 20 kHz pulse frequency over 1.8 km.

    • Main Results:

    • The deep learning method significantly reduced processing time from 2.62 seconds to 0.1 seconds for the test dataset.
    • The enhanced processing speed was achieved while maintaining accuracy comparable to traditional algorithms.
    • The algorithm provides faster acquisition of detailed demodulated data and real-time visualization.

    Conclusions:

    • The proposed deep learning algorithm offers a substantial improvement in processing speed for DAS data demodulation.
    • This advancement facilitates more efficient and real-time analysis of fiber optic sensing data.
    • The method holds potential for enhancing measurement accuracy and system performance in various fiber optic sensing applications.