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Distributed fiber acoustic sensing (DAS) technology can continuously spatially detect disturbances along the sensing fiber over long distance in real time. It has many unique advantages, including, large coverage, high time-and-space resolution, convenient implementation, strong environment adaptability, etc. Nowadays, DAS becomes a versatile technology in many fields, such as, intrusion detection, railway transportation, seismology, structure health monitoring, etc. In this paper, the sensing principle and some common performance indexes are introduced, and a brief overview of recent DAS researches in Shanghai Institute of Optics and Fine Mechanics (SIOM) is presented. Some representative research advances are explained, including, quantitative demodulation, interference fading suppression, frequency response boost, high spatial resolution, and distributed multi-dimension localization. The engineering applications of DAS, carried out by SIOM and other groups, are summarized and reviewed. Finally, possible future directions are discussed and concluded. It is believed that, DAS has great development potential and application prospect.

Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100–800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between “infested” and “healthy” signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system’s ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.

The optical fiber distributed acoustic sensing system can continuously detect acoustic signals along the optical fiber, with the characteristics of wide response frequency band, large capacity, anti-electromagnetic interference and so on. An ultra-sensitive distributed optical fiber acoustic sensing system (uDAS) was produced with a high response frequency up to 100 kHz and thousands of sensing points. The arrayed ultrasonic detection can be realized by the distributed characteristics of the system. For verifying its ultrasonic detection ability, it is applied to ultrasonic nondestructive testing of structural defects. 20 optical fiber ultrasonic detection units were constructed. Each one is a fiber ring with a diameter of about 1.5 cm and a length of 2 meters. Two cubic cement structures with a side length of 30 cm were manufactured for experiment, one of which had an artificial internal defect. The optical fiber array was attached to the surface of the cement structure, and the ultrasonic signal with a frequency of 40 kHz was excited at a single point on the opposite side. The ultrasonic propagation speed changes on account of the defects such as holes and loose texture in the propagation path. The defects in the structure are identified and located by analyzing the difference of arrival time of waves. It is expected that such sensing system could found important applications in structure defect detection.

Location error and false alarm are noticeable problems in fiber distributed acoustic sensing systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). A novel method based on signal kurtosis is proposed to locate and discriminate perturbations in Φ-OTDR systems. The spatial kurtosis (SK) along the fiber is firstly obtained by calculating the kurtosis of acoustic signals at each position of the fiber in a short time period. After the moving average on the spatial dimension, the spatial average kurtosis (SAK) is then obtained, whose peak can accurately locate the center of the vibration segment. By comparing the SAK value with a certain threshold, we may to some degree discriminate the instantaneous destructive perturbations from the system noise and certain ambient environmental interferences. The experimental results show that, comparing with the average of the previous localization methods, the SAK method improves the pencil-break and digging locating signal-to-noise ratio (SNR) by 16.6 dB and 17.3 dB, respectively; and decreases the location standard deviation by 7.3 m and 9.1 m, respectively. For the instantaneous destructive perturbation (pencil-break and digging) detection, the false alarm rate can be as low as 1.02%, while the detection probability is maintained as high as 95.57%. In addition, the time consumption of the SAK method is adequate for a real-time Φ-OTDR system.

Deep learning anomaly detection is important in distributed optical fiber acoustic sensing (DAS). However, anomaly detection is more challenging than traditional learning tasks, due to the scarcity of true-positive data and the vast imbalance and irregularity within datasets. Furthermore, it is impossible to catalog all types of anomalies, therefore, the direct application of supervised learning is deficient. To overcome these problems, an unsupervised deep learning method that only learns the normal data features from ordinary events is proposed. First, a convolutional autoencoder is used to extract DAS signal features. A clustering algorithm then locates the feature center of the normal data, and the distance to the new signal is used to determine whether it is an anomaly. The efficacy of the proposed method was evaluated in a real high-speed rail intrusion scenario, and considered all behaviors that may threaten the normal operation of high-speed trains as abnormal. The results show that the threat detection rate of this method reaches 91.5%, which is 5.9% higher than that of the state-of-the-art supervised network and, at 7.2%, the false alarm rate is 0.8% lower than the supervised network. Moreover, using a shallow autoencoder reduces the parameters to 1.34 K, which is significantly lower than the 79.55 K of the state-of-the-art supervised network.

Coral reefs are essential marine ecosystems that support a vast array of biodiversity and provide numerous benefits, including fisheries, tourism, and coastal protection. However, these ecosystems are increasingly threatened by various factors, including anthropogenic noise from activities such as shipping and coastal development. Traditional acoustic methods of monitoring reef health, such as hydrophones, are limited by their point-based sensing, reliance on batteries, and need for manual data retrieval, which can be labor-intensive and costly. In this study, we explore the application of fiber-optic distributed acoustic sensing (DAS) for real-time marine reef monitoring, a new application compared to its previous use in deep-sea soundscape monitoring. We deployed a fiber-optic DAS system in a reef area on the coast of the Central Red Sea, alongside a conventional hydrophone for comparison. The experiment was conducted in a degraded inshore reef near the KAUST shoreline, characterized by sand, macroalgae, scattered boulders, and encrusting sponges. This site was selected as a proxy for coral reef monitoring due to its biological activity, including snapping shrimp and the presence of reef-related fish species. Our observations revealed significant acoustic activity within the 1.5 to 5 kHz range, with snapping shrimp sounds increasing after the onshore lights were switched off, consistent with known behavioral patterns of increased acoustic activity during low-light conditions. Additionally, we detected various fish vocalizations, including drums and impulses, within the 100 to 1000 Hz range. The DAS system also successfully tracked the timing and trajectory of scuba diver movements along the reef. These findings demonstrate the potential of fiber-optic DAS technology to provide high-resolution spatial mapping of reef soundscapes, offering a comprehensive and cost-effective solution for continuous reef monitoring, thereby demonstrating the feasibility of DAS for real-time acoustic monitoring in reef environments.

Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space–time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.

Accurate measurement of pipeline flow is of great significance for industrial and environmental monitoring. Traditional intrusive methods have the disadvantages of high cost and damage to pipeline structure, while non-intrusive techniques can circumvent such issues. Although Taylor’s frozen hypothesis has a theoretical advantage in non-intrusive velocity detection, current research focuses on planar flow fields, and its applicability in turbulent circular pipes remains controversial. Moreover, there is no precedent for combining it with distributed acoustic sensing (DAS) technology. This paper constructs a circular pipe turbulence model through large eddy simulation (LES), revealing the spatiotemporal distribution characteristics of turbulent kinetic energy and the energy propagation rules of FK spectra. It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. An experimental circulating pipeline DAS measurement system was set up to complete signal denoising and compare two principles of flow velocity verification. The results show that the vortex structure of turbulent flow in circular pipes remains stable in the convection direction, conforming to theoretical premises; the relative error of average flow velocity by this method is ≤3%, with significant improvements in accuracy and stability in high-flow zones. This study provides innovative methods and experimental basis for non-intrusive flow detection using DAS.

The emerging distributed fiber-optic acoustic sensing (DAS) technology has broad prospects for application in vertical seismic profiles (VSP). However, the acquired DAS-VSP data often suffers from coupled noise that seriously affects data quality. Traditional methods for suppressing coupled noise are usually time-consuming and not suitable for the large-scale denoising of DAS-VSP data. To address this, a coupled noise suppression method based on the U-Net network is proposed, and a self-attention (SA) block is introduced to enhance the denoising ability of the network. Transfer learning is employed to achieve coupled noise suppression from synthetic data to field data. Denoising results demonstrate that the network can effectively suppress coupled noise in DAS-VSP data while preserving signal energy to a certain extent, exhibiting strong generalization capability. Upon completion of network training, denoising results can be obtained within seconds, making it more convenient and efficient compared to traditional methods.

With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology.