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Статті в журналах з теми "Distributed Acoustic Sensing (DAS)"

Автор: Grafiati

Опубліковано: 12 квітня 2025

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1

Chambers, Derrick, Peiyao Li, Harpreet Sethi, and Jeffery Shragge. "Monitoring industrial acoustics with distributed acoustic sensing." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A58. http://dx.doi.org/10.1121/10.0010648.

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True-phase distributed acoustic sensing (DAS), a technique which uses low-power laser pulses to monitor along-fiber strain in optical cable, has proven useful in many geophysical research areas, including down-hole monitoring in oil/gas extraction, near-surface characterization, detecting and locating regional and global earthquakes, urban monitoring. Most of the geophysical applications to date, however, have focused on recording elastic waves propagating through solid media. In this work, we explore the response of DAS for recording acoustic propagation in air, as a function of fiber type and configuration, over frequency bands useful for monitoring industrial environments. We also present methods of creating simple fiber-composite sensing units for improving sensitivity, and strategies for combining solid-earth and acoustic monitoring to create an effective seismoacoustic array with a single DAS interrogator.

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2

Shang, Ying, Maocheng Sun, Chen Wang, Jian Yang, Yuankai Du, Jichao Yi, Wenan Zhao, Yingying Wang, Yanjie Zhao, and Jiasheng Ni. "Research Progress in Distributed Acoustic Sensing Techniques." Sensors 22, no. 16 (August 13, 2022): 6060. http://dx.doi.org/10.3390/s22166060.

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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.

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3

Abadi, Shima, William S. Wilcock, and Brad P. Lipovsky. "Detecting hydro-acoustic signals using Distributed Acoustics Sensing technology." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A201. http://dx.doi.org/10.1121/10.0016027.

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Distributed Acoustic Sensing (DAS) is a relatively new technology that transforms fiber optic cables, typically used for telecommunications, into dense sensor arrays, capable of meter-scale recordings up to ∼100 km. The interest in these technologies for ocean exploration and monitoring has risen in recent years. These systems enable continuous and highly sensitive measurements of both temporal and spatial acoustic data. In this presentation, we use data recorded during a 4-day DAS experiment on the twin cables of the Ocean Observatories Initiative (OOI) Regional Cabled Array (RCA) extending off central Oregon. We demonstrate the capabilities of DAS in recording a wide range of acoustic signals including the 20-Hz call of fin whales, the 15-Hz calls and harmonics of the Northeast Pacific blue whale, and ship noises. We use beamforming and the time difference of arrival (TDOA) algorithm to find the bearing and the location of the signal of interest. We also explain the DAS array response and its sensitivity to paths arriving parallel or perpendicular to the cable and discuss the best practices to overcome the challenges in analyzing this large data set.

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4

Shen, Zhichao, Wenbo Wu, and Ying-Tsong Lin. "High-resolution observations of shallow-water acoustic propagation with distributed acoustic sensing." Journal of the Acoustical Society of America 156, no. 4 (October 1, 2024): 2237–49. http://dx.doi.org/10.1121/10.0030400.

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Distributed acoustic sensing (DAS), converting fiber-optic cables into dense acoustic sensors, is a promising technology that offers a cost-effective and scalable solution for long-term, high-resolution studies in ocean acoustics. In this paper, the telecommunication cable of Martha's Vineyard Coastal Observatory (MVCO) is used to explore the feasibility of cable localization and shallow-water sound propagation with a mobile acoustic source. The MVCO DAS array records coherent, high-quality acoustic signals in the frequency band of 105–160 Hz, and a two-step inversion method is used to improve the location accuracy of DAS channels, reducing the location uncertainty to ∼2 m. The DAS array with refined channel positions enables the high-resolution observation of acoustic modal interference. Numerical simulations that reproduce the observed interference pattern suggest a compressional speed of 1750 m/s in the sediment, which is consistent with previous in situ geoacoustic measurements. These findings demonstrate the long-term potential of DAS for high-resolution ocean acoustic studies.

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5

Ellmauthaler, Andreas, Brian C. Seabrook, Glenn A. Wilson, John Maida, Jeff Bush, Michel LeBlanc, James Dupree, and Mauricio Uribe. "Distributed acoustic sensing of subsea wells." Leading Edge 39, no. 11 (November 2020): 801–7. http://dx.doi.org/10.1190/tle39110801.1.

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Topside distributed acoustic sensing (DAS) of subsea wells requires advanced optical engineering solutions to compensate for reduced acoustic bandwidth, optical losses, and back reflections that are accumulated through umbilicals, multiple wet- and dry-mate optical connectors, splices, optical feedthrough systems, and downhole fibers. To address these issues, we introduce a novel DAS solution based on subsea fiber topology consisting of two transmission fibers from topside and an optical circulator deployed in the optical flying lead at the subsea tree. This solution limits the sensing fiber portion to the downhole fiber, located below the subsea tree, and enables dry-tree-equivalent acoustic sampling frequencies of more than 10 kHz while eliminating all back reflections from multiple subsea connectors above the tree. When combined with enhanced backscatter single-mode fiber, this gives rise to a DAS interrogation system that is capable of providing dry-tree-equivalent acoustic sensing performance over the entire length of the subsea well, regardless of the tie-back distance. It also enables the same spectral-based DAS processing algorithms developed for seismic, sand control, injector/producer profiling, and well integrity on dry-tree wells to be applied directly to subsea DAS data. The performance of this subsea DAS system has been validated through a series of laboratory and field trials. We show the results of the tests and discuss how the system is deployed within subsea infrastructure.

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6

Schmidt, Henrik. "Distributed acoustic sensing in shallow water." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 3297. http://dx.doi.org/10.1121/1.4778019.

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7

Rosalie, Cedric, Nik Rajic, Patrick Norman, and Claire Davis. "Acoustic Source Localisation Using Distributed Sensing." Procedia Engineering 188 (2017): 499–507. http://dx.doi.org/10.1016/j.proeng.2017.04.514.

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8

Schick, Yannik, Guilherme H. Weber, Marco Da Silva, Cicero Martelli, and Mark W. Hlawitschka. "Flow monitoring in a bubble column reactor by Distributed Acoustic Sensing." tm - Technisches Messen 91, s1 (August 1, 2024): 14–19. http://dx.doi.org/10.1515/teme-2024-0048.

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Анотація:

Zusammenfassung Im Rahmen dieser Publikation berichten wir über den experimentellen Einsatz von Distributed Acoustic Sensing zur Überwachung eines Blasensäulenreaktors. Für diese Art von chemischen Reaktoren gibt es eine Vielzahl von grundlegenden Anwendungen, die eine detaillierte Überwachung der internen Strömungsdynamik erfordern. Im Zuge experimenteller Untersuchungen zeigt Distributed Acoustic Sensing die Fähigkeit, Messungen eines Hydrophons auf nicht-intrusiveWeise zu reproduzieren und mechanische Vibrationsmuster, die mit großen und kleinen Blasen verbunden sind, mit einer hohen räumlichen Auflösung zu erkennen. Die vielversprechenden Ergebnisse unterstreichen, dass Distributed Acoustic Sensing für die Echtzeitüberwachung von Blasensäulenreaktoren geeignet ist und räumlich aufgelöste Erkenntnisse über die Strömungseigenschaften liefert. Diese Studie ebnet denWeg für die weitere Erforschung der Anwendung von Distributed Acoustic Sensing in Blasensäulenreaktoren.

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9

Becker, Matthew, Thomas Coleman, Christopher Ciervo, Matthew Cole, and Michael Mondanos. "Fluid pressure sensing with fiber-optic distributed acoustic sensing." Leading Edge 36, no. 12 (December 2017): 1018–23. http://dx.doi.org/10.1190/tle36121018.1.

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10

Douglass, Alexander S., John Ragland, and Shima Abadi. "Overview of distributed acoustic sensing technology and recently acquired data sets." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A64. http://dx.doi.org/10.1121/10.0018174.

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Fiber optic distributed acoustic sensing (DAS) is a recent innovation utilized primarily in the seismic community for measuring seismic acoustics signals at low frequencies (single digit Hz and below). The technique utilizes strain rates in a fiber optic cable, observed via the backscatter of light pulses, to measure the acoustic field. Recently, the capabilities of this technology to measure higher frequency acoustic fields (10s to 100s of Hz) have been explored. Low frequency marine mammals calls at ∼20 Hz and ship noises have been successfully recorded, and a recent experiment demonstrated the capability to record up to ∼500 Hz. This talk provides an overview of DAS technology and introduces two recent experiments for studying water column acoustics with DAS. A 4-day experiment conducted in November 2020 as part of the Ocean Observatories Initiative (OOI) provides data along two fiber optic cables extending west from the coast of Oregon by 65 km and 95 km, reaching depths of 590 m and 1575 m, respectively. DASCAL22, a recent experiment from October 2022, simultaneously recorded data using DAS at 2 kHz sampling rate on a cable extending 3.54km at ∼100 m depth and multiple moored hydrophones placed close to the DAS cable, allowing direct comparison between a new and existing technology.

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11

Hua, Liwei, Xuran Zhu, Baokai Cheng, Yang Song, Qi Zhang, Yongji Wu, Lawrence C. Murdoch, Erin R. Dauson, Carly M. Donahue, and Hai Xiao. "Distributed Acoustic Sensing Based on Coherent Microwave Photonics Interferometry." Sensors 21, no. 20 (October 13, 2021): 6784. http://dx.doi.org/10.3390/s21206784.

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A microwave photonics method has been developed for measuring distributed acoustic signals. This method uses microwave-modulated low coherence light as a probe to interrogate distributed in-fiber interferometers, which are used to measure acoustic-induced strain. By sweeping the microwave frequency at a constant rate, the acoustic signals are encoded into the complex microwave spectrum. The microwave spectrum is transformed into the joint time–frequency domain and further processed to obtain the distributed acoustic signals. The method is first evaluated using an intrinsic Fabry Perot interferometer (IFPI). Acoustic signals of frequency up to 15.6 kHz were detected. The method was further demonstrated using an array of in-fiber weak reflectors and an external Michelson interferometer. Two piezoceramic cylinders (PCCs) driven at frequencies of 1700 Hz and 3430 Hz were used as acoustic sources. The experiment results show that the sensing system can locate multiple acoustic sources. The system resolves 20 nε when the spatial resolution is 5 cm. The recovered acoustic signals match the excitation signals in frequency, amplitude, and phase, indicating an excellent potential for distributed acoustic sensing (DAS).

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12

Zhou, Ran, Konstantin Osypov, Andrej Bona, Yingping Li, Mark Willis, Roman Pevzner, Michel Verliac, and Ge Zhan. "Introduction to special section: Distributed acoustic sensing." Interpretation 9, no. 4 (October 11, 2021): SJi—SJii. http://dx.doi.org/10.1190/int-2021-0909-spseintro.1.

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13

Gabai, Haniel, and Avishay Eyal. "On the sensitivity of distributed acoustic sensing." Optics Letters 41, no. 24 (December 5, 2016): 5648. http://dx.doi.org/10.1364/ol.41.005648.

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14

Dreyer, Uilian Jose, Guilherme Dutra, Guilherme Heim Weber, Rafael Jose Daciuk, Manoel Feliciano da Silva, Ricardo Munoz Freitas, Daniel Rodrigues Pipa, Jean Carlos Cardozo da Silva, Marco Jose da Silva, and Cicero Martelli. "Horse Gait Identification Using Distributed Acoustic Sensing." IEEE Sensors Journal 21, no. 3 (February 1, 2021): 3058–65. http://dx.doi.org/10.1109/jsen.2020.3027922.

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15

Lim Chen Ning, Ivan, and Paul Sava. "High-resolution multi-component distributed acoustic sensing." Geophysical Prospecting 66, no. 6 (May 29, 2018): 1111–22. http://dx.doi.org/10.1111/1365-2478.12634.

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16

Shang, Haiyan, Lin Zhang, and Shaoyi Chen. "Field Experiments of Distributed Acoustic Sensing Measurements." Photonics 11, no. 11 (November 18, 2024): 1083. http://dx.doi.org/10.3390/photonics11111083.

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Modern, large bridges and tunnels represent important nodes in transportation arteries and have a significant impact on the development of transportation. The health and safety monitoring of these structures has always been a significant concern and is reliant on various types of sensors. Distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of sensing and communication. DAS provides an effective and low-cost approach for the detection of various resources and seismic activities. In this study, field experiments are elucidated, using DAS for the Hong Kong–Zhuhai–Macao Bridge, and for studying vehicle trajectories, earthquakes, and other activities. The basic signal-processing methods of filtering and normalization are adopted for analyzing the data obtained with DAS. With the proposed DAS technology, the activities on shore, vehicle trajectories on bridges and in tunnels during both day and night, and microseisms within 200 km were successfully detected. Enabled by DAS technology and mass fiber networks, more studies on sensing and communication systems for the monitoring of bridge and tunnel engineering are expected to provide future insights.

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17

Fernández-Ruiz, María R., Marcelo A. Soto, Ethan F. Williams, Sonia Martin-Lopez, Zhongwen Zhan, Miguel Gonzalez-Herraez, and Hugo F. Martins. "Distributed acoustic sensing for seismic activity monitoring." APL Photonics 5, no. 3 (March 1, 2020): 030901. http://dx.doi.org/10.1063/1.5139602.

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18

Poletto, Flavio, Daniel Finfer, Piero Corubolo, and Biancamaria Farina. "Dual wavefields from distributed acoustic sensing measurements." GEOPHYSICS 81, no. 6 (November 2016): D585—D597. http://dx.doi.org/10.1190/geo2016-0073.1.

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Distributed acoustic sensing (DAS) using fiber optic cables is an emerging seismic acquisition technology for the oil and gas industry, geothermal resource exploration, and underground fluid-storage monitoring. This technology offers the advantage of improving seismic acquisition by enabling massive arrays for monitoring of seismic wavefields at reduced cost with respect to conventional methods. In general, it is accepted that this method provides acoustic signals comparable with conventional seismic data, however, without the multicomponent directional information typical of geophones. We have developed a modified data extraction method and found that, as a result of the dense spatial distribution of recording points along the optic cable, DAS can provide two linked wavefield components in the axial direction, even when using a single 1D cable line. These signal pairs consist of dual components that are related to native strain rate (or strain) and particle acceleration (or velocity) fields at a given recording location. These dual signals are easily usable for wavefield separation purposes simply performing a trace-by-trace combination by appropriate scaling coefficient. The analysis performed with borehole data from linear and helically wound cables demonstrates the effectiveness of polarity recovery and dual-wavefield separation. We show real examples in which the data can be combined to provide separation of up- and downgoing wavefields. The ratio of the dual components provides information on local slowness properties in the formation.

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19

Lim Chen Ning, Ivan, and Paul Sava. "Multicomponent distributed acoustic sensing: Concept and theory." GEOPHYSICS 83, no. 2 (March 1, 2018): P1—P8. http://dx.doi.org/10.1190/geo2017-0327.1.

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Distributed acoustic sensing (DAS) data are increasingly used in geophysics. Lower in cost and higher in spatial resolution, DAS data are appealing, especially in boreholes in which optical fibers are readily available. DAS has the potential to become a permanent reservoir monitoring tool with a reduced sensing time interval. To accomplish this goal, it is critical that DAS can record all wave modes to fully characterize reservoir properties. This goal can be achieved by recording the complete strain tensor consisting of 6C. Conventional DAS provides projections of these components along the optical fiber by observing deformation along the fiber. To obtain the entire 6C strain tensor, we have developed an approach using multiple strain projections measured along optical fibers with judiciously chosen geometry specifically. We evaluate designs combining multiple helical configurations or a single helical configuration together with a straight optical fiber that allow access to multiple strain projections. We group multiple strain projections in a given spatial window to perform reconstruction of the entire strain tensor in a least-squares sense under the assumption that the seismic wavelength is larger than the analysis window size. We determine how optimal optical fiber parameters can be selected using a scan of the entire configuration space and analyzing the condition number associated with the geometry of the optical fibers. We develop our method through synthetic experiments using realistic fiber geometry and wavefields of arbitrary complexity.

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20

Liu, Xiaohui, Chen Wang, Ying Shang, Chang Wang, Wenan Zhao, Gangding Peng, and Hongzhong Wang. "Distributed acoustic sensing with Michelson interferometer demodulation." Photonic Sensors 7, no. 3 (December 29, 2016): 193–98. http://dx.doi.org/10.1007/s13320-017-0363-y.

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21

Su, Yuqi, Fusang Zhang, Kai Niu, Tianben Wang, Beihong Jin, Zhi Wang, Yalan Jiang, Daqing Zhang, Lili Qiu, and Jie Xiong. "Embracing Distributed Acoustic Sensing in Car Cabin for Children Presence Detection." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 1 (March 6, 2024): 1–28. http://dx.doi.org/10.1145/3643548.

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Contactless acoustic sensing has been actively exploited in the past few years to enable a large range of applications, ranging from fine-grained vital sign monitoring to coarse-grained human tracking. However, existing acoustic sensing systems mainly work on smartphone or smart speaker platforms. In this paper, we envision an exciting new acoustic sensing platform, i.e., car cabin which is inherently embedded with a large number of speakers and microphones. We propose the new concept of distributed acoustic sensing and develop novel designs leveraging the unique characteristics of rich multi-path in car cabin to enable fine-grained sensing even when the primary reflection is totally blocked. By using child presence detection as the application example, we show that child presence can be detected through body motions or even subtle breath (when the child is sleeping or in coma) at all locations in the cabin without any blind spots. We further show that the proposed system can robustly work in different car cabins, achieving an average detection accuracy of 97% and a false alarm rate always below 2% under different scenarios including those challenging ones such as rear-facing seat blockage. We believe the proposed distributed sensing modality in car cabin pushes acoustic sensing one big step towards real-life adoption.

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22

Jiajing, Liang, Wang Zhaoyong, Lu Bin, Wang Xiao, Li Luchuan, Ye Qing, Qu Ronghui, and Cai Haiwen. "Distributed acoustic sensing for 2D and 3D acoustic source localization." Optics Letters 44, no. 7 (March 25, 2019): 1690. http://dx.doi.org/10.1364/ol.44.001690.

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23

Xie, Zhiyuan, Yuwei Sun, Anqiang Lv, and Qian Xu. "Measurement and Evaluation Method of Distributed Optical Fiber Acoustic Sensing Performance." Photonics 11, no. 2 (February 8, 2024): 166. http://dx.doi.org/10.3390/photonics11020166.

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Distributed acoustic sensing incorporates multiple indicators, and there exists a mutually constraining relationship among these indicators. Different application fields have varying requirements for indicators. Therefore, indicator testing and comprehensive evaluations are crucial for engineering applications. In this paper, we conducted a theoretical analysis of key indicators, including frequency response, sensitivity, spatial resolution, sensing distance, multi-point perturbation, and temperature influence. The indicator test scheme was developed, and a test system was constructed. The test data were analyzed and compared in the time-frequency domain. A performance evaluation method for distributed acoustic sensing, based on the analytic hierarchy process, is proposed, and a comprehensive evaluation example focused on high-frequency applications is presented. The results show that the test scheme and method presented in this paper can accurately measure the upper limits of each indicator of distributed acoustic sensing. The proposed comprehensive evaluation method enables the assessment of sensor performance and applicability based on engineering practices. It addresses the challenge of evaluating distributed acoustic sensing with multiple indicators and offers an efficient approach for equipment development and engineering applications.

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24

SAM, A. ROBERT, G. PUNITHAVATHY, and G. S. AYYAPPAN. "Distributed Acoustic Sensing Signal Model Under Static Fiber Conditions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 08 (August 6, 2024): 1–6. http://dx.doi.org/10.55041/ijsrem36976.

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This paper presents a statistical model for distributed acoustic sensor interrogation units that utilizes laser pulses transmitted into fiber optics. Interactions within the fiber lead to localized acoustic energy, resulting in backscatter, which is a reflection of the light. Explicit equations were used to calculate the amplitudes and phases of backscattered signals. The proposed model accurately predicts the amplitude signal spectrum and autocorrelation, aligning well with experimental observations. This study also explores the phase signal characteristics relevant to optical time-domain reflectometry (OTDR) system sensing applications, demonstrating consistency with the experimental results. The experiments were conducted using Python coding, enabling the analysis of the individual components of the Distributed Acoustic Sensing (DAS) system. The assumptions of the model include the static condition of the fiber, implying the absence of external forces or vibrations. Consequently, no external acoustic disturbances were considered. The backscattered signal comprises a random noise component resulting from intrinsic fiber imperfections, and a coherent component arising from the interplay between the laser pulse and the fiber. Keywords: distributed acoustic sensing, fiber optics, optical time-domain reflectometry, Rayleigh scattering.

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25

Ragland, John, Alexander S. Douglass, and Shima Abadi. "Using distributed acoustic sensing for ocean ambient sound analysis." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A64. http://dx.doi.org/10.1121/10.0018176.

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Distributed acoustic sensing (DAS) is a technique that utilizes the back scattering in fiber optic cables to densely sample the strain rate in both space and time. This technique has been widely demonstrated as a powerful tool for seismic sensing, but the efficacy of submerged, under-sea cables for ocean acoustic sensing remains underexplored. The ocean observatories initiative (OOI) conducted a distributed acoustic sensing experiment in November of 2021, where two of the fiber optic cables continuously recorded the strain rate for four days. In this talk, the ambient sound field recorded by the OOI DAS experiment will be explored. A statistical comparison of hydrophone measurements and DAS measurements will be presented. Additionally, the possibility of using ocean ambient sound techniques, such as ambient noise interferometry will be explored and compared to hydrophone analysis. [Work supported by ONR.]

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26

Chen, Wenjie, Junfeng Jiang, Kun Liu, Shuang Wang, Zhe Ma, Guanhua Liang, Zhenyang Ding, Tianhua Xu, and Tiegen Liu. "Self-copy-shift-based differential phase extracting method for fiber distributed acoustic sensing." Chinese Optics Letters 18, no. 8 (2020): 081201. http://dx.doi.org/10.3788/col202018.081201.

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27

Li, Wenmin, Yang Lu, Yu Chen, Yan Liang, and Zhou Meng. "Directivity Research of Sensing Channels in a Distributed Fiber Optic Hydrophone." Journal of Physics: Conference Series 2486, no. 1 (May 1, 2023): 012082. http://dx.doi.org/10.1088/1742-6596/2486/1/012082.

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Abstract An acoustic orientation method using the directivity of sensing channels in distributed fiber optic hydrophone (DFOH) is presented and demonstrated. Theoretical analysis shows that the sensing channel of DFOH is directional. Based on the directivity function of the channel, the direction of the acoustic signal can be obtained by scanning the length of the sensing channel, which is confirmed by experiments.

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28

Abadi, Shima, Alexander S. Douglass, and John Ragland. "Comparing distributed acoustic sensing data with hydrophone recordings." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A64. http://dx.doi.org/10.1121/10.0018175.

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Distributed acoustic sensing (DAS) is a technology that transforms telecommunication fiber optic cables into dense sensor arrays by continuously transmitting pulses of light down the cable and measuring backscattering from natural inhomogeneities within the fiber cable. The technology can densely sample the acoustic field over long ranges (up to 100 km), providing a means for large scale passive acoustic monitoring. To evaluate the capabilities of DAS, it is necessary to benchmark and calibrate the technology relative to traditional hydrophone data. The DAS Calibration Experiment 2022 (DASCAL22) recorded 9 days of both DAS and hydrophone data in Puget Sound, WA in October 2022. The DAS data were recorded with a sample rate of 2 kHz, and the cable extended 3.5 km on the seafloor between two islands, reaching depths of 100 m, and the hydrophones were moored adjacent to the DAS cable at 5 m and 25 m from the seafloor. The recordings include impulses from an active source at 1 m, 5 m, and 10 m depths, and an abundance of passive acoustic data corresponding to ship traffic, wind, and rain. This work aims to draw comparisons between the hydrophone and DAS recordings to evaluate the capability of DAS at detecting sounds at frequencies as high as 1 kHz.

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29

Haile, Mulugeta A., Nathaniel E. Bordick, and Jaret C. Riddick. "Distributed acoustic emission sensing for large complex air structures." Structural Health Monitoring 17, no. 3 (June 20, 2017): 624–34. http://dx.doi.org/10.1177/1475921717714614.

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The vast majority of existing work on acoustic emission–based structural health monitoring is for geometrically simple structures with uninterrupted propagation path and constant wave speed. Realistic systems such as a full-scale fuselage, however, are built from interconnected pieces of acoustically mismatched parts such as sandwich core panels, stringer stiffened skin, and fastener holes. The geometric complexity and dynamic operating environment of realistic systems mean that the acoustic emission wave undergoes multiple reflections, refractions, and mode changes resulting in overlapped transducer outputs with no clear beginning and end. The objective of this paper is to outline the fundamental limitations of acoustic emission as applied to complex systems and present a new distributed data-centric acoustic emission sensing network for durability health monitoring and damage tolerance applications in large and complex systems. The study considers the case of a full-scale composite rotorcraft fuselage to introduce several new concepts on acoustic emission data acquisition time control for alleviating effects of wave distortion as well as methods for improving event location analysis in large quasi-isotropic materials. Methods for adaptive front-end signal processing and data volume control are presented. Despite the size and complexity of realistic full-scale systems and the acoustic emission data, we show that it is possible to locate damage with acceptable accuracy.

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30

Geary, Andrew. "Seismic Soundoff: What geophysicists and engineers need to know about DAS." Leading Edge 41, no. 10 (October 2022): 740. http://dx.doi.org/10.1190/tle41100740.1.

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In this episode, Mark Willis discusses his Distinguished Instructor Short Course, “Distributed acoustic sensing for seismic measurements — What geophysicists and engineers need to know.” Willis helps attendees build intuition and understanding of the value, limitations, and applications of distributed acoustic sensing (DAS) seismic technology. Hear the full episode at https://seg.org/podcast/post/15802 .

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Turov, Artem T., Yuri A. Konstantinov, D. Claude, Vitaliy A. Maximenko, Victor V. Krishtop, Dmitry A. Korobko, and Andrei A. Fotiadi. "Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing." Applied Sciences 14, no. 22 (November 6, 2024): 10147. http://dx.doi.org/10.3390/app142210147.

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Standard single-mode telecommunication optical fiber is still one of the most popular in distributed acoustic sensing. Understanding the acoustic, mechanical and optical features of various fibers available currently can lead to a better optimization of distributed acoustic sensors, cost reduction and adaptation for specific needs. In this paper, a study of the performances of seven fibers with different coatings and production methods in a distributed acoustic sensor setup is presented. The main results include the amplitude–frequency characteristic for each of the investigated fibers in the range of acoustic frequencies from 100 to 7000 Hz. A single-mode fiber fabricated using the modified chemical vapor deposition technique together with a polyimide coating has shown the best sensitivity to acoustic events in the investigated range of frequencies. All of this allows us to both compare the studied specialty fibers with the standard single-mode fiber and choose the most suitable fiber for a specific application, providing an enhancement for the performance of distributed acoustic sensors and better adaptation for the newly aroused potential applications.

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32

Sun, Yixiang, Hao Li, Cunzheng Fan, Baoqiang Yan, Junfeng Chen, Zhijun Yan, and Qizhen Sun. "Review of a Specialty Fiber for Distributed Acoustic Sensing Technology." Photonics 9, no. 5 (April 20, 2022): 277. http://dx.doi.org/10.3390/photonics9050277.

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Specialty fibers have introduced new levels of flexibility and variability in distributed fiber sensing applications. In particular, distributed acoustic sensing (DAS) systems utilized] the unique functions of specialty fibers to achieve performance enhancements in various distributed sensing applications. This paper provides an overview of recent preparations and developments of specialty-fiber-based DAS systems and their sensing applications. The specialty-fiber-based DAS systems are categorized and reviewed based on the differences in scattering enhancement and methods of preparation. The prospects of using specialty fibers for DAS systems are also discussed.

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33

Gu Jinfeng, 顾金凤, 卢斌 Lu Bin, 杨竣淇 Yang Junqi, 王照勇 Wang Zhaoyong, 叶蕾 Ye Lei, 叶青 Ye Qing, 瞿荣辉 Qu Ronghui, and 蔡海文 Cai Haiwen. "Distributed Acoustic Sensing Based on Multi-Core Fiber." Acta Optica Sinica 41, no. 7 (2021): 0706003. http://dx.doi.org/10.3788/aos202141.0706003.

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34

Costley, Richard D., Gustavo Galan-Comas, Kent K. Hathaway, Stephen A. Ketcham, and Clay K. Kirkendall. "Distributed acoustic sensing for near-surface seismic applications." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1702–3. http://dx.doi.org/10.1121/1.5067562.

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35

Mateeva, Albena, Jorge Lopez, Jeff Mestayer, Peter Wills, Barbara Cox, Denis Kiyashchenko, Zhaohui Yang, Wilfred Berlang, Rocky Detomo, and Samantha Grandi. "Distributed acoustic sensing for reservoir monitoring with VSP." Leading Edge 32, no. 10 (October 2013): 1278–83. http://dx.doi.org/10.1190/tle32101278.1.

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36

Madsen, Karen Nørgaard, Richard Tøndel, and Øyvind Kvam. "Data-driven depth calibration for distributed acoustic sensing." Leading Edge 35, no. 7 (July 2016): 610–14. http://dx.doi.org/10.1190/tle35070610.1.

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37

Nishiguchi, Ken’ichi. "Phase unwrapping for fiber-optic distributed acoustic sensing." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2016 (2016): 81–87. http://dx.doi.org/10.5687/sss.2016.81.

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38

Dagallier, Adrien, Sylvain Cheinet, Daniel Juvé, Timothée Surgis, and Thierry Broglin. "Distributed acoustic sensing of shots in realistic environments." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2906. http://dx.doi.org/10.1121/1.5137082.

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39

Konsbruck, Robert L., Emre Telatar, and Martin Vetterli. "On Sampling and Coding for Distributed Acoustic Sensing." IEEE Transactions on Information Theory 58, no. 5 (May 2012): 3198–214. http://dx.doi.org/10.1109/tit.2012.2184849.

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40

Bona, Andrej, and Roman Pevzner. "Distributed Acoustic Sensing for Mineral Exploration: Case Study." ASEG Extended Abstracts 2018, no. 1 (December 2018): 1–4. http://dx.doi.org/10.1071/aseg2018abw8_4f.

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41

Martins, Wallace A., Marcello L. R. de Campos, Rafael da Silva Chaves, Carlos P. V. Lordelo, Andreas Ellmauthaler, Leonardo O. Nunes, and David A. Barfoot. "Communication Models for Distributed Acoustic Sensing for Telemetry." IEEE Sensors Journal 17, no. 15 (August 1, 2017): 4677–88. http://dx.doi.org/10.1109/jsen.2017.2714023.

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42

Zhu, Tieyuan, Ariel Lellouch, and Kyle T. Spikes. "Introduction to this special section: Distributed acoustic sensing." Leading Edge 39, no. 11 (November 2020): 775. http://dx.doi.org/10.1190/tle39110775.1.

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Distributed acoustic sensing (DAS) has seen many advances in recent years with many different applications. This special section contains six papers featuring applications that vary from microseismic event detection to subsea applications to surface deployments, fracture characterization, and well irregularity identification. Each paper introduces a unique problem and then poses the use of DAS in an appropriate way to solve the problem. Special sections on DAS are relatively frequent in the literature currently, so these papers are a snapshot of the work done with the technology. We hope you enjoy these publications.

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43

Yang, Jihyun, Jeffrey Shragge, and Ge Jin. "Filtering Strategies for Deformation-Rate Distributed Acoustic Sensing." Sensors 22, no. 22 (November 14, 2022): 8777. http://dx.doi.org/10.3390/s22228777.

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Deformation-rate distributed acoustic sensing (DAS), made available by the unique designs of certain interrogator units, acquires seismic data that are theoretically equivalent to the along-fiber particle velocity motion recorded by geophones for scenarios involving elastic ground-fiber coupling. While near-elastic coupling can be achieved in cemented downhole installations, it is less obvious how to do so in lower-cost horizontal deployments. This investigation addresses this challenge by installing and freezing fiber in shallow backfilled trenches (to 0.1 m depth) to achieve improved coupling. This acquisition allows for a reinterpretation of processed deformation-rate DAS waveforms as a “filtered particle velocity” rather than the conventional strain-rate quantity. We present 1D and 2D filtering experiments that suggest 2D velocity-dip filtering can recover improved DAS data panels that exhibit clear surface and refracted arrivals. Data acquired on DAS fibers deployed in backfilled, frozen trenches were more repeatable over a day of acquisition compared to those acquired on a surface-deployed DAS fiber, which exhibited more significant amplitude variations and lower signal-to-noise ratios. These observations suggest that deploying fiber in backfilled, frozen trenches can help limit the impact of environmental factors that would adversely affect interpretations of time-lapse DAS observations.

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44

Chambers, Derrick, Alexander Ankamah, Ahmad Tourei, Eileen R. Martin, Tim Dean, Jeffery Shragge, John A. Hole, et al. "Distributed acoustic sensing (DAS) for longwall coal mines." International Journal of Rock Mechanics and Mining Sciences 189 (May 2025): 106090. https://doi.org/10.1016/j.ijrmms.2025.106090.

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45

Le Calvez, Joël, and Erkan Ay. "Introduction to this special section: Fiber optics." Leading Edge 43, no. 11 (November 2024): 719. http://dx.doi.org/10.1190/tle43110719.1.

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Fiber-optic technology has revolutionized various fields, including telecommunications, medicine, and geophysics. One of the most promising advancements within fiber optics is distributed sensing, which enables the continuous measurement (versus space-limited point sensing) of physical parameters over long distances. Distributed temperature, strain, acoustic, and pressure sensing (DxS) are cutting-edge technologies that revolutionize the way we monitor and analyze temperature, acoustic, and pressure signals over large distances. Unlike traditional sensors, DxS utilizes optical fibers as sensitive elements, transforming them into continuous, high-resolution, and distributed sensors. This innovative approach opens a range of applications across various industries.

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46

Dejneka, Zach, Daniel Homa, Joshua Buontempo, Gideon Crawford, Eileen Martin, Logan Theis, Anbo Wang, and Gary Pickrell. "Magnetic Field Sensing via Acoustic Sensing Fiber with Metglas® 2605SC Cladding Wires." Photonics 11, no. 4 (April 10, 2024): 348. http://dx.doi.org/10.3390/photonics11040348.

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Magnetic field sensing has the potential to become necessary as a critical tool for long-term subsurface geophysical monitoring. The success of distributed fiber optic sensing for geophysical characterization provides a template for the development of next generation downhole magnetic sensors. In this study, Sentek Instrument’s picoDAS is coupled with a multi-material single mode optical fiber with Metglas® 2605SC cladding wire inclusions for magnetic field detection. The response of acoustic sensing fibers with one and two Metglas® 2605SC cladding wires was evaluated upon exposure to lateral AC magnetic fields. An improved response was demonstrated for a sensing fiber with in-cladding wire following thermal magnetic annealing (~400 °C) under a constant static transverse magnetic field (~200 μT). A minimal detectable magnetic field of ~500 nT was confirmed for a sensing fiber with two 10 μm cladding wires. The successful demonstration of a magnetic field sensing fiber with Metglas® cladding wires fabricated via traditional draw processes sets the stage for distributed measurements and joint inversion as a compliment to distributed fiber optic acoustic sensors.

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47

Yun, Changho. "Underwater Multi-Channel MAC with Cognitive Acoustics for Distributed Underwater Acoustic Networks." Sensors 24, no. 10 (May 10, 2024): 3027. http://dx.doi.org/10.3390/s24103027.

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The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture.

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48

Masoudi, Ali, and Gilberto Brambilla. "Distributed acoustic sensing with optical fibres: Fundamentals and applications." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A260. http://dx.doi.org/10.1121/10.0016204.

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Because of their intrinsic geometrical features and extraordinary transparency, optical fibers allow for a continuous and dynamic monitoring of strain along all their length. In state of the art systems, 100 000 points can be resolved over a range of the order of 50 km, providing an extremely cost effective solution per sensing unit. In its basic implementation, a light pulse is injected into the sensing optical fibre and the Rayleigh backscattered light is continuously monitored, in a process similar to a radar system. While the phase relation between different point along the fiber is used to reconstruct the strain (thus the acoustic information), the time of flight (e.g., delay between injected pulse and received backscattered light) provides information about the physical location along the fiber. In the last decade, research on novel detection schemes and fibers has allowed to extend the detection range to over 170km, to decrease the spatial resolution below 10 cm and significantly increase the signal-to-noise-ratio. Because of this, distributed acoustic optical fiber sensing (DAS) has found applications in the oil and gas industry for oil well monitoring and geoseismics and more widely for the monitoring of border and perimeter security, resulting in market of the order of $1 billion. More recent applications include monitoring of railways and road traffic, structural health, earthquakes and other geophysical events.

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49

Carpenter, Chris. "SAGD and Fiber-Optic Distributed Acoustic and Temperature Sensing." Journal of Petroleum Technology 68, no. 09 (September 1, 2016): 78–80. http://dx.doi.org/10.2118/0916-0078-jpt.

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50

Atterholt, James, Zhongwen Zhan, Zhichao Shen, and Zefeng Li. "A unified wavefield-partitioning approach for distributed acoustic sensing." Geophysical Journal International 228, no. 2 (October 7, 2021): 1410–18. http://dx.doi.org/10.1093/gji/ggab407.

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SUMMARY While distributed acoustic sensing (DAS) has been demonstrated to have great potential in seismology, DAS data often have much higher levels of stochastic and coherent noise (e.g. instrument noise, traffic vibrations) than data collected by traditional seismometers. The linearly, densely spaced nature of DAS arrays presents a suite of opportunities for more innovative processing techniques that can be used to address this issue. One way to take advantage of DAS’s array architecture is through the use of curvelets. Curvelets have a non-uniform scaling property that makes them an excellent tool for representing images with discontinuities along piecewise, twice continuously differentiable curves. This anisotropic scaling property makes curvelets an ideal processing tool for DAS data, for which the measured wavefield can be represented as an image composed of curved features. Here, we use the curvelet frame as a tool for the manipulation of DAS signal and demonstrate how this manipulation can improve our ability to identify important features in DAS data sets. We use the curvelet representation to partition the measured wavefield using DAS data collected near Ridgecrest, CA, following the 2019 Mw7.1 Ridgecrest earthquake. Here, we isolate the earthquake-induced wavefield from coherent and stochastic noise using the curvelet frame in an effort to improve the results of template matching of the Ridgecrest aftershock sequence. We show that our wavefield-partitioning technique facilitates the identification of over 30 per cent more aftershocks and greatly reduces the magnitude of diurnal depressions in the aftershock catalogue due to cultural noise.

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