SagnacAssisted Enhanced OTDR for Distributed Acoustic Sensing: A Standardized Benchmark and Engineering Evaluation Framework

2026-06-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

This study introduces a Sagnac-assisted enhanced phase-sensitive optical time-domain reflectometry ($φ$-OTDR) sensing architecture designed to improve large-scale distributed acoustic sensing (DAS) performance. The architecture addresses common field issues like polarization-induced fading (PIF), local signal degradation, and environmental interference by integrating a Sagnac interferometer for continuous phase response. Heterogeneous signal alignment is achieved via a cross-correlation procedure on an FPGA platform. The research also establishes a standardized benchmark framework for DAS event recognition, comparing conventional feature engineering, shallow classifiers, single-branch deep models, and dual-branch fusion models. Experiments on a 10-km sensing fiber with six acoustic event classes demonstrated that the dual-branch fusion model achieved the best trade-off, with 89.79% accuracy, 89.83% macro-F1, and a 5.00% nuisance alarm rate on a balanced test set.

Key takeaway

For Machine Learning Engineers developing distributed acoustic sensing (DAS) systems, you should consider integrating Sagnac-assisted $φ$-OTDR architectures to enhance signal reliability. Prioritize dual-branch fusion models for event recognition, as they offer superior accuracy and F1-scores compared to other methods. When evaluating system performance, move beyond accuracy alone; incorporate macro-F1, nuisance alarm rate, false negative rate, and latency for a comprehensive, deployment-oriented assessment.

Key insights

Sagnac-assisted $φ$-OTDR and a dual-branch fusion model significantly enhance distributed acoustic sensing performance and event recognition accuracy.

Principles

• Sagnac interferometers mitigate $φ$-OTDR fading.
• Multi-metric evaluation is crucial for DAS deployment.
• Dual-branch fusion models offer optimal DAS trade-offs.

Method

A Sagnac interferometer supplements $φ$-OTDR, with FPGA-based cross-correlation for signal alignment. A benchmark protocol evaluates models using consistent data partitioning, preprocessing, and multi-metric definitions for DAS event recognition.

In practice

• Implement Sagnac-assisted $φ$-OTDR for DAS.
• Utilize dual-branch fusion models for event recognition.
• Evaluate DAS systems using multiple performance metrics.

Topics

• Distributed Acoustic Sensing
• Sagnac Interferometer
• $φ$-OTDR
• Event Recognition
• Dual-Branch Fusion Models
• Machine Learning Benchmarking
• FPGA Implementation

Code references

• wawa-abc/das

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.