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Review
. 2025 Aug 14;25(16):5052.
doi: 10.3390/s25165052.

Research Progress of Event Intelligent Perception Based on DAS

Di Wu  1   2 , Qing-Quan Liang  1   2 , Bing-Xuan Hu  3 , Ze-Ting Zhang  1   2 , Xue-Feng Wang  1   2 , Jia-Jun Jiang  1   2 , Gao-Wei Yi  1   2 , Hong-Yao Zeng  1   2 , Jin-Yuan Hu  2 , Yang Yu  2 , Zhen-Rong Zhang  1
Affiliations
Review

Research Progress of Event Intelligent Perception Based on DAS

Di Wu et al. Sensors (Basel). .

Abstract

This review systematically examines intelligent event perception in distributed acoustic sensing (DAS) systems. Beginning with the elucidation of the DAS principles, system architectures, and core performance metrics, it establishes a comprehensive theoretical framework for evaluation. This study subsequently delineates methodological innovations in both traditional machine learning and deep learning approaches for event perception, accompanied by performance optimization strategies. Particular emphasis was placed on advances in hybrid architectures and intelligent sensing strategies that achieve an optimal balance between computational efficiency and detection accuracy. Representative applications spanning traffic monitoring, perimeter security, infrastructure inspection, and seismic early warning systems demonstrate the cross-domain adaptability of the technology. Finally, this review addresses critical challenges, including data scarcity and environmental noise interference, while outlining future research directions. This work provides a systematic reference for advancing both the theoretical and applied aspects of DAS technology, while highlighting its transformative potential in the development of smart cities.

Keywords: deep learning; distributed acoustic sensing (DAS); event intelligent perception; machine learning.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
A schematic diagram of target types for event perception in DAS.
Figure 2
Figure 2
Distributed acoustic sensing system.
Figure 3
Figure 3
Drawing of a linear optimal hyperplane generated by the SVM model.
Figure 4
Figure 4
Hidden Markov model sequence.
Figure 5
Figure 5
Schematic diagram of the random forest algorithm.
Figure 6
Figure 6
1D-CNN architecture for DAS signal classification (Ref. [67], Figure 5). Conv: Convolutional layers. BNorm: Batch normalization. Pooling: Pooling layers. Flatten: Flatten layer. FC: Fully connected layers.
Figure 7
Figure 7
Structure of LSTM-CNN (Ref. [92], Figure 8).
Figure 8
Figure 8
A general model of the attention mechanism. The system transforms input features into query (Q), key (K), and value (V) matrices through learned linear projections.
Figure 9
Figure 9
Schematic diagram of DAS-based traffic monitoring system (Ref. [115], Figure 1).
Figure 10
Figure 10
Fiber optic and hardware-linked perimeter security system (Ref. [122], Figure 1). (1) Sensing fiber deployed along fences with optimized bending radius; (2) 3 × 3 coupler interferometer for phase demodulation; (3) FPGA-accelerated edge computing unit; and (4) multi-tier alarm classification. Pink arrows indicate signal flow direction.
Figure 11
Figure 11
Current and potential application scenarios of DAS for monitoring infrastructure and geological hazards (Ref. [132], Figure 1).
Figure 12
Figure 12
Diagram of the principle of epicenter location (Ref. [139], Figure 5).
Figure 13
Figure 13
OptoSeisTM subsea permanent reservoir monitoring system deployment and operation diagram (Ref. [143], Figure 15).
Figure 14
Figure 14
Some typical autonomous mobile robots.
See this image and copyright information in PMC

References

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