Explore the vast range of titles available.

This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single μg were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.

Distributed acoustic sensing techniques based on Rayleigh scattering have been widely used in many applications due to their unique advantages, such as long-distance detection, high spatial resolution, and wide sensing bandwidth. In this paper, we provide a review of the recent advancements in distributed acoustic sensing techniques. The research progress and operation principles are systematically reviewed. The pivotal technologies and solutions applied to distributed acoustic sensing are introduced in terms of polarization fading, coherent fading, spatial resolution, frequency response, signal-to-noise ratio, and sensing distance. The applications of the distributed acoustic sensing are covered, including perimeter security, earthquake monitoring, energy exploration, underwater positioning, and railway monitoring. The potential developments of the distributed acoustic sensing techniques are also discussed.

In 2016, a novel interrogation technique for phase-sensitive (Φ)OTDR was mathematically formalized and experimentally demonstrated, based on the use of a chirped-pulse as a probe, in an otherwise direct-detection-based standard setup: chirped-pulse (CP-)ΦOTDR. Despite its short lifetime, this methodology has now become a reference for distributed acoustic sensing (DAS) due to its valuable advantages with respect to conventional (i.e., coherent-detection or frequency sweeping-based) interrogation strategies. Presenting intrinsic immunity to fading points and using direct detection, CP-ΦOTDR presents reliable high sensitivity measurements while keeping the cost and complexity of the setup bounded. Numerous technique analyses and contributions to study/improve its performance have been recently published, leading to a solid, highly competitive and extraordinarily simple method for distributed fibre sensing. The interesting sensing features achieved in these last years CP-ΦOTDR have motivated the use of this technology in diverse applications, such as seismology or civil engineering (monitoring of pipelines, train rails, etc.). Besides, new areas of application of this distributed sensor have been explored, based on distributed chemical (refractive index) and temperature-based transducer sensors. In this review, the principle of operation of CP-ΦOTDR is revisited, highlighting the particular performance characteristics of the technique and offering a comparison with alternative distributed sensing methods (with focus on coherent-detection-based ΦOTDR). The sensor is also characterized for operation in up to 100 km with a low cost-setup, showing performances close to the attainable limits for a given set of signal parameters [≈tens-hundreds of pe/sqrt(Hz)]. The areas of application of this sensing technology employed so far are briefly outlined in order to frame the technology.

Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.

Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars. The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods. The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h. The analysis methods presented here will help to establish routines for near real-time train tracking and train integrity analysis.

Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning. Plain Language Summary Distributed Acoustic Sensing (DAS) is an emerging technique that can convert an optical fiber cable into a dense array to record seismic waves from earthquakes. The recorded seismic signals contain essential information about earthquakes. For example, DAS can record high‐amplitude signals from earthquakes with large magnitudes. However, the exact setting of the optical cables (i.e., installation conditions and coupling with the surrounding medium) is often unknown, thus preventing quantitative estimations of earthquake magnitudes with DAS. In this study, we analyze earthquake data recorded by different DAS arrays and develop a data‐driven method to obtain an empirical relation between the earthquake magnitude and the amplitude of DAS signals. We show that this empirical relation can accurately estimate the earthquake magnitude directly from the DAS data. Furthermore, the empirical relation we obtain from one area can also be applied to new regions with slight calibrations. Our empirical relation can significantly expand the applications of DAS in earthquake research, such as seismic hazard assessment and earthquake early warning. Key Points We present the first data‐based scaling relation between Distributed Acoustic Sensing (DAS) amplitude and earthquake magnitude Earthquake magnitude can be reliably estimated from DAS amplitude with the scaling relation The DAS scaling relation can be transferred from one region to another with minor calibrations

Distributed acoustic sensing (DAS) utilizes standard optical fibers as continuous distributed seismic sensors, enabling the acquisition of subsurface vibration information with high spatial sampling density and long-distance coverage. Existing numerical simulation methods for DAS data typically rely on solving elastic wave equations and are implemented through simplified processes such as axial projection along the fiber and spatial averaging. However, this approach neglects the influence of optical systems on DAS responses, including Rayleigh scattering, coherent detection, and signal demodulation, making it difficult to comprehensively reflect the response characteristics of actual DAS systems. To address this deficiency, this study proposes a DAS seismic response numerical simulation framework that integrates physical processes. We introduce Rayleigh scattering modeling and demodulation mechanisms for coherent OTDR (optical time domain reflectometry) in the simulation, and systematically consider key parameters including pulse width, gauge length, polarization fading, laser frequency drift, phase noise, and detector noise. Simulation results demonstrate that, under ideal noise-free conditions, the new model yields result highly consistent with those of the traditional spatial averaging model, validating its accuracy. In the presence of environmental noise, the proposed method produces smoother responses compared to traditional approaches, indicating a certain denoising advantage. When optical system noise is introduced, the physics-based simulation can reproduce complex phenomena observed in actual measurements, such as phase fading noise and frequency drift noise. These findings underscore the necessity and effectiveness of a physical model in capturing the true behavior of DAS systems. The proposed simulation framework provides a critical foundation for optimizing DAS system design, enhancing signal-to-noise ratios, and improving DAS data inversion. It holds significant guiding implications for the future application of DAS in seismic monitoring.

Distributed acoustic sensing (DAS) exploiting fibre optic cables provides high-density sampling of the seismic wavefield. Scattered returns from multiple laser pulses provide local averages of strain rate over a finite gauge length. The nature of the signal depends on the orientation of the cable with respect to the passing seismic waves. For local events, the dominant part of the strain rate can be extracted from the difference of ground velocity resolved along the fibre at the ends of the gauge interval. For more distant events the response at seismic frequencies can be represented as the acceleration along the fibre modulated by the wave slowness resolved in the same direction, which means there is a strong dependence on cable orientation. Slowness–frequency representations of the wavefield provide insight, via modelling, into the character of the DAS wavefield in a range of situations from a local jump source, through a regional earthquake to teleseismic recording. The slowness-domain representation of the DAS signal allows analysis of the array response of cable configurations indicating a bias due to the slowness weighting associated with the effect of gauge length. Unlike seismometer arrays the response is not described by a single generic stacking function.