Advanced Signal Processing in Distributed Acoustic Sensors Based on Submarine Cables for Seismology Applications

Chen, Shaoyi; Jun, Han; Sui, Qi; Zhu, Kun; Lü, Chao; Li, Zhaohui

doi:10.1109/jlt.2023.3273268

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…Freeland [25] Geophysical survey the system hardware [12,13], and digital signal processing methods [14][15][16]. Furthermore, as a sensor of the DAS system, the internal structure, mechanical properties, and environmental coupling of the optical cable will all affect the SNR of the system.…”

Section: References Application Cross-sectionmentioning

confidence: 99%

“…Various methods have been proposed to enhance the SNR of the system. These methods include modifications to the design of the optical fiber [10,11], updating the system hardware [12,13], and digital signal processing methods [14][15][16]. Furthermore, as a Sensors 2023, 23, 7340 2 of 14 sensor of the DAS system, the internal structure, mechanical properties, and environmental coupling of the optical cable will all affect the SNR of the system.…”

Section: Introductionmentioning

confidence: 99%

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How the Material Characteristics of Optical Fibers and Soil Influence the Measurement Results of Distributed Acoustic Sensing

Jiang,

Liang,

Tong

et al. 2023

Sensors

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Fiber optic distributed acoustic sensing (DAS) technology is widely used in security surveillance and geophysical survey applications. The response of the DAS system to external vibrations varies with different types of fiber optic cable connections. The mechanism of mutual influence between the cable’s characteristics and DAS measurement results remains unclear. This study proposed a dynamic model of the interaction between the optical cable and the soil, analyzed the impact of the dynamic parameters of the optical cable and soil on the sensitivity of the DAS system, and validated the theoretical analysis through experiments. The findings suggest that augmenting the cable’s bending stiffness 5.5-fold and increasing its unit mass 4.2-fold result in a discernible reduction of the system’s response to roughly 0.15 times of its initial magnitude. Cables with lower unit mass and bending stiffness are more sensitive to vibration signals. This research provides a foundation for optimizing vibration-enhanced fiber optic cables and broadening the potential usage scenarios for DAS systems.

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Section: References Application Cross-sectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How the Material Characteristics of Optical Fibers and Soil Influence the Measurement Results of Distributed Acoustic Sensing

Jiang,

Liang,

Tong

et al. 2023

Sensors

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State-of-The-Art application and challenges of optical fibre distributed acoustic sensing in civil engineering

Ghazali,

Mohamad,

Nasir

et al. 2024

Optical Fiber Technology

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Classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals

Matthaiou,

Masoudi,

Araki

et al. 2024

Geophysical Journal International

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Distributed acoustic sensing (DAS) is an optoelectronic technology that utilises fibre optic cables to detect disturbances caused by seismic waves. Using DAS, seismologists can monitor geophysical phenomena at high spatial and temporal resolutions over long distances in inhospitable environments. Field experiments using DAS, are typically associated with large volumes of observations, requiring algorithms for efficient processing and monitoring capabilities. In this study, we present a supervised classifier trained to recognise seismic activity from other sources of hydroacoustic energy. Our classifier is based on a 2D convolutional neural network architecture. The 55km-long ocean-bottom fibre optic cable, located off Cape Muroto in south-west of Japan, was interrogated using DAS. Data were collected during two different monitoring time-periods. Optimisation of the model’s hyperparameters using Gaussian Processes Regression was necessary to prevent issues associated with small sizes of training data. Using a test set of 100 annotated images, we have shown that the top performing model is around $92\%$ accurate in classifying geophysical data from other sources of hydroacoustic energy and ambient noise.

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Advanced Signal Processing in Distributed Acoustic Sensors Based on Submarine Cables for Seismology Applications

Cited by 8 publications

References 47 publications

How the Material Characteristics of Optical Fibers and Soil Influence the Measurement Results of Distributed Acoustic Sensing

How the Material Characteristics of Optical Fibers and Soil Influence the Measurement Results of Distributed Acoustic Sensing

State-of-The-Art application and challenges of optical fibre distributed acoustic sensing in civil engineering

Classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals

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