Editorial Feature

Advancing Urban Safety with Distributed Acoustic Sensing

Distributed acoustic sensing (DAS) was developed in the 1980s with the introduction of an optical time domain reflectometer designed to sense optical fiber link losses. Today, DAS systems provide high spatial resolution, wide sensing bandwidth, real-time monitoring, and long detection ranges.1

Advancing Urban Safety with Distributed Acoustic Sensing

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These systems have various potential sensing applications, including perimeter security, traffic monitoring, and pipeline leak detection. The use of DAS in such applications contributes significantly to urban safety.1,2

This article explores the role of DAS in advanced urban safety and its integration with smart city technologies.

Understanding Distributed Acoustic Sensing

DAS transmits information using light and relies on standard telecommunications-grade optical fibers for sensing. An interrogation unit within the DAS system continuously injects short-pulse lasers into the sensing cables.

As the light passes through these cables, it scatters in multiple directions due to the spatially varying refractive index of the fiber cores, generating multiple types of scattered light.

DAS systems use Rayleigh backscattered light for sensing.2 The properties of the scattered light—phase, amplitude, wavelength, intensity—alter when the optical fibers are disturbed or subjected to strain, temperature, and vibrations.1,2

DAS interrogators detect this Rayleigh backscattered light along the fibers and analyze variations in physical parameters (such as temperature and strain) to measure dynamic strain, including vibrations and acoustic waves.2

DAS offers many unique capabilities beyond those of conventional optical fiber-based sensing methods, including a detection range exceeding 100 kilometers, dynamic sensing, and high spatial detection density.2

Thus, DAS systems are promising for broad applications like perimeter security, seismic observation, oil and gas exploration, railway monitoring, and subsurface imaging, all of which are crucial for urban safety.1,2

Applications in Urban Safety

Urban infrastructure such as roads, railways, tunnels, pipelines, and power transmission lines are essential for economic and social development. However, they are prone to structural deterioration and damage from aging, geohazards, earthquakes, corrosion, and human activities. Thus, robust and proficient detection systems like DAS are necessary for their safe and reliable long-term operation.2

Perimeter security is critical to protecting urban environments and national political stability. DAS systems enhance the security of premises due to their wide monitoring range, strong environmental adaptability, high concealment, and absence of blind spots.1

The low-noise and high-resolution sensing of DAS systems also facilitates the identification of personnel, vehicles, and aircraft intrusions, ensuring high security on the premises.2

Seismic detection is another important aspect of urban safety. Conventional earthquake monitoring methods are unsuitable for urban implementation due to high data acquisition costs and limited coverage.

DAS, however, uses existing urban communication optical cables for underground structure observation and early earthquake warning. Thus, DAS techniques help avoid danger and enhance urban safety and social stability.1

Railways are crucial for intercity and intracity transportation in densely populated cities. Thus, ensuring the safe operation of railways through train positioning, speed and trajectory monitoring, and track health is essential for urban safety.2

Current railway inspection technologies lack real-time analysis and may not work in extreme weather conditions.2 In contrast, DAS can significantly enhance railway monitoring through real-time, accurate, and reliable track health monitoring and train tracking.1

Highway traffic monitoring is integral to urban society. With optical fibers buried under roads, DAS systems offer strong concealment and long-term functionality. These systems can effectively differentiate between pedestrians and motor vehicles by analyzing the recorded data from the sensing cables.2

Urban pipelines transport essential resources like oil and natural gas. Real-time monitoring using DAS systems ensures pipeline safety and enhances their functional life. DAS can also detect intrusions and leakages from abnormal vibrations and noise, using dynamic measurements to locate pipeline leakages and assess pressure variations for timely interventions.2

Tunnels enhance urban transportation facilities by reducing driving distances, saving costs, and protecting ecological environments. However, tunnel construction can be challenging due to potential water leakages, land subsidence, and flooding.

Dynamic and comprehensive evaluation technologies like DAS are essential for monitoring tunnel health status, capable of identifying vibration events like unexpected rockfalls during construction with over 90 % accuracy.

Integration with Smart City Technologies

Smart cities worldwide are adopting intelligent security systems capable of dynamically processing vast data volumes. When integrated with artificial intelligence (AI) techniques such as pattern recognition, neural networks, and support vector machines, DAS systems become potent tools for smart city applications.1,2 Additionally, challenges like cable failures in telecommunication networks can be mitigated using DAS enhanced by AI and machine learning.1

Researchers are employing DAS to provide comprehensive monitoring and response solutions for smart cities in multiple ways. For instance, a recent study in Remote Sensing described an urban DAS testbed near a university in Spain, serving as a proof-of-concept for generating data-driven mobility models.

This DAS system provided crucial details of the sensing scenario, automatically distinguished different events (for example, traffic elements and earthquakes), effectively removed initial noise, and successfully analyzed complex frequency signals.3

Another recent study in the IEEE Internet of Things Journal introduced a smart fiber-optic DAS (sDAS) with multitask learning (MTL) for efficient ground listening applications. The two-level MTL-enhanced sDAS system could simultaneously recognize and localize ground events, showing improved learning capabilities for new events and robustness against environmental noise.4

Advancements and Prospects for Distributed Acoustic Sensing

Despite numerous benefits, DAS systems still require improved sensitivity, spatial resolution, sensing distance, signal-to-noise ratio, and frequency response range.1

A DAS system with MHz level frequency response range could non-destructively monitor engineering structures.2 Further integrating DAS with machine learning and neural networks would facilitate multi-parameter and multidimensional monitoring, enhance calibration precision, and improve noise removal capabilities.1,3

The directional sensitivity limit of DAS could be improved using novel structures of optical fibers such as spirally winding, umbrella, and checkerboard layouts, enabling multi-component measurements by capturing signals from multiple directions.2

Advanced data processing software and risk assessment systems could also help urban management departments extract more potential information and quickly assess infrastructure health.2

Overall, the notable advantages of DAS include data anonymity, immunity to weather conditions, long sensing range, and high resolution.3 Further improvements in data-sharing platforms and standardized monitoring guidelines will promote the broader adoption of DAS in cities worldwide and enhance public safety standards.2

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References and Further Reading

1. Shang, Y., Sun, M., Wang, C., Yang, J., Du, Y., Yi, J., Zhao, W., Wang, Y., Zhao, Y., Ni, J. (2022). Research Progress in Distributed Acoustic Sensing Techniques. Sensors. doi.org/10.3390/s22166060

2. Zhu, H.-H., Liu, W., Wang, T., Su, J.-W., Shi, B. (2022). Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends. Sensors. doi.org/10.3390/s22197550

3. García, L., Mota, S., Titos, M., Martínez, C., Segura, JC., Benítez, C. (2023). Fiber Optic Acoustic Sensing to Understand and Affect the Rhythm of the Cities: Proof-of-Concept to Create Data-Driven Urban Mobility Models. Remote Sensing (Basel). doi.org/10.3390/rs15133282

4. Wu, H., Wang, Y., Liu, X., Sun, Y., Yan, G., Wu, Y., Rao, Y. (2024). Smart Fiber-Optic Distributed Acoustic Sensing (sDAS) With Multitask Learning for Time-Efficient Ground Listening Applications. IEEE Internet of Things Journal. doi.org/10.1109/jiot.2023.332014

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Nidhi Dhull

Written by

Nidhi Dhull

Nidhi Dhull is a freelance scientific writer, editor, and reviewer with a PhD in Physics. Nidhi has an extensive research experience in material sciences. Her research has been mainly focused on biosensing applications of thin films. During her Ph.D., she developed a noninvasive immunosensor for cortisol hormone and a paper-based biosensor for E. coli bacteria. Her works have been published in reputed journals of publishers like Elsevier and Taylor & Francis. She has also made a significant contribution to some pending patents.  


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