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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.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

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 Sensing deep neural networks machine learning signal 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.