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Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep

Mugdim Bublin1

  • 1FH Campus Wien, University of Applied Sciences, 1100 Vienna, Austria.

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Distributed Acoustic Sensing (DAS) for pipeline monitoring can distinguish threats from interference. Deep learning shows promise, outperforming classical methods in speed and efficiency, especially when combined with other approaches.

Keywords:
Distributed Acoustic Sensingdeep neural networksmachine learningsignal processing

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Area of Science:

  • Geophysics
  • Sensor Technology
  • Machine Learning

Background:

  • Distributed Acoustic Sensing (DAS) offers advanced pipeline monitoring capabilities.
  • A key challenge is differentiating critical events (e.g., excavation) from noise (e.g., machinery).

Purpose of the Study:

  • To evaluate the effectiveness of classical machine learning algorithms for DAS event detection.
  • To compare classical machine learning with deep learning approaches for pipeline monitoring.
  • To propose guidelines for hybrid system design.

Main Methods:

  • Utilized simulations and real-world data for algorithm testing.
  • Implemented and compared classical machine learning algorithms.
  • Developed and evaluated a deep learning model.
  • Investigated hybrid approaches combining different methods.

Main Results:

  • Both classical and deep learning methods achieved acceptable detection accuracy.
  • Deep learning demonstrated significant advantages, including reduced feature engineering needs, a sixfold decrease in event detection delay, and a twelvefold reduction in execution time.
  • The best performance was achieved by integrating deep learning with knowledge-based and classical machine learning techniques.

Conclusions:

  • Deep learning is a highly promising approach for DAS-based pipeline monitoring, offering superior speed and efficiency.
  • Hybrid systems combining deep learning with classical and knowledge-based methods yield optimal results.
  • Guidelines for efficient hybrid system design are proposed.