Distributed Acoustic Sensing (DAS) is a novel technology that uses fiber optics to sense and monitor vibrations. It has demonstrated immense potential for various applications, including seismology research, traffic vibration detection, structural health inspection, and lifeline engineering. DAS technology transforms long sections of fiber optic cables into a high-density array of vibration sensors, providing exceptional spatial and temporal resolution for real-time monitoring of vibrations. Obtaining high-quality vibration data using DAS requires a robust coupling between the fiber optic cable and the ground layer. The study utilized the DAS system to detect vibration signals generated by vehicles operating on the campus road of Beijing Jiaotong University. Three distinct deployment methods were employed: the uncoupled fiber on the road, the underground communication fiber optic cable ducts, and the cement-bonded fixed fiber optic cable on the road shoulder, and compared for their outcomes. Vehicle vibration signals under the three deployment methods were analyzed using an improved wavelet threshold algorithm, which was verified to be effective. The results indicate that for practical applications, the most effective deployment method is the cement-bonded fixed fiber optic cable on the road shoulder, followed by the uncoupled fiber on the road, and the underground communication fiber optic cable ducts are the least effective. This has important implications for the future development of DAS as a tool for various fields.

Aircraft detection plays a vital role in aviation management and safe operation in the aviation system. Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR) technology is a prevailing sensing method in geophysics research, structure inspection, transportation detection, etc. Compared with existing video- or radio-based detection methods, Φ-OTDR is cost-effective, suitable for long-distance detection, and resistant to severe weather conditions. We present a detection system using Φ-OTDR technology and analyze the character of the acoustic signal of aircraft. Instead of runway monitoring in the airport or noise detection in the air, this study focuses on the detection of seismic vibration signal excited by the sound of aircraft. The Chebyshev filter is adopted to eliminate the impact of background noise and random noise from the original vibration signal; the short-time Fourier transform is used for time-frequency analysis. The experimental results showed that the seismic vibration signal excited by the aircraft sound is mainly low-frequency, which is under 5 Hz. Time delay of aircraft vibration signal in different locations of the optic fiber is recorded by the sensing system. The Doppler effect is also revealed by the time-domain analysis: the frequency increases when the aircraft is approaching and decreases when the aircraft moves away.

High-speed railways (HSRs) generate repeatable and spatially extended seismic wavefields, providing useful signals for distributed acoustic sensing (DAS)-based vibration analysis. This study develops an integrated measurement framework for two characteristic frequency eigenquantities in HSR-induced seismic wavefields: train frequency and bridge frequency. Building on established spectral-line, cepstral, and Doppler descriptions of HSR seismic wavefields, we systematize the relevant theoretical expressions, compare frequency-domain correlation and cepstral strategies for train-frequency estimation, and derive a velocity-independent bridge-frequency estimator from paired Doppler-shifted components. DAS data collected along viaduct sections of the Beijing–Guangzhou HSR are used to evaluate the framework across single trains, dense observation traces, and multiple train events. The results show that bridge frequency is more stable than train frequency, with lower measurement variance. The frequency-derived train speeds and carriage lengths fall within typical operating ranges of Chinese HSR trains, and the observed spatial periodicity in frequency measurements is consistent with bridge pier spacing. These findings support accurate frequency measurement and preliminary estimation of train speed, carriage length, and wave velocity from DAS records. Together, they clarify measurable frequency parameters of HSR seismic sources and establish a quantitative source-characterization basis for DAS-based railway vibration analysis and future multi-source monitoring studies.