Distributed Acoustic Sensing (DAS) is a technology used to monitor and analyze acoustic signals along a fiber-optic cable. It has a wide range of applications in various industries, including oil and gas, infrastructure monitoring, security, and environmental monitoring. DAS works by using the fiber-optic cable itself as a sensor to detect acoustic disturbances and vibrations.
Eric Blow of NEC Labs will address how machine-learning methods applied to distributed acoustic-sensing data can monitor facility perimeters and detect intrusion via walk, dig, or drive events over buried optical fibre—for example achieving ~90% classification accuracy.
We report a record long 200.6 km distributed acoustic sensing (DAS) link without inline ampli-fication, 28.6% improvement of sensing range has been achieved by using three segments of enhanced-scattering fibre (ESF) with progressively higher scattering enhancements.
The 2021 emergence of Brood X cicadas was monitored in situ in our testbed using a DAS system connected to an outdoor telecom fiber over a 16-day period. The spectral and energy characteristics of the cicada calling signal has been measured and analyzed.
Passive-Optical-Networks (PON) have emerged as a pivotal technology for broadband access network and are now expanding to wireless communication, supporting 5G and development of future 6G frameworks. PON systems are expected to find many new applications, including in electrical power grids, modern industrial factories, and smart city infrastructure. With the growing capabilities and increasing complexity and extent of the optical distribution network, effective surveillance of fiber infrastructure has become increasingly important to ensure long-term viability and dependability. Simultaneously, there is increasing demand for effective distributed monitoring systems for the power-grid elements and machinery in automated factories operating within PON environments. This paper discusses the challenges and potential solutions for implementing distributed acoustic sensing (DAS) within PON architecture. We will present design and experimental demonstrations of a co-existing DAS and 10G PON (XGS-PON) system with a 23.5 km feeder fiber (FF) and a 1 × 16 splitter. A unique signature from each distributed fiber (DF) and optical network units (ONU) is detected by utilizing a coded Enhanced Scatter Fiber (ESF). Vibration events originating from up to three DF/ONUs are identified using a novel scheme using the coded ESFs in conjunction with fiber delay lines. We further investigated the sensing performance and potential crosstalk between XGS-PON and DAS signals within this co-existing DAS and XGS-PON system.
We present techniques to extend the sensing range in unrepeatered submarine cable systems by utilizing Enhanced-Scattering Fibre (ESF), large-area ultra-low-loss (ULL) fibre, and a digital Distributed Acoustic Sensing (DAS) interrogator. A DAS sensing range of up to 200.6 km has been achieved using 156km SCUBA125 fibre, followed by three segments of ESF. Additionally, we demonstrate long-range sensing capabilities and high-capacity data transmission over a 270.6 km unrepeatered submarine system, where DAS and 400G DWDM data transmission coexist. The impact of Distributed Raman Amplification (DRA) on sensing performance, and crosstalk between DAS and 400G DWDM channels in coexistence of DAS and unrepeatered transmission system are studied. Finally, we briefly discuss the potential application scenarios for monitoring undersea cables using ESFs.
A 100-meter-long fiber optic cable was installed at the bottom of a water tank at the Davidson Laboratory, together with a hydrophone for reference. The water tank is approximately 2.5 meters deep and 95 meters long; the tank also employs a 6-paddle wavemaker which can generate programmable surface waves. A 155-cm-long model boat weighing 6.5 kilograms was automatically dragged on the surface of the tank via an electrical towing mechanism. The movement of the model boat along the fiber cable and over the hydrophone was recorded using a commercially available NEC Distributed Acoustic Sensing (DAS) system and simultaneously by a hydrophone. The experiments were repeated with and without the artificially generated surface waves. The data obtained from the hydrophone and the DAS system are presented and compared. The results show the compatibility between the DAS data and the hydrophone data. More importantly, ourresults show that it is possible to measure the surface waves and to detect a surface vessel approaching the sensor by only using the time domain analysis in terms of detected total energy over time.
Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiberoptic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory recorded fiber-optic ESC-50 datasets and a real-world fiber optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.
We demonstrate fiber-optic acoustic data transmission using distributed acoustic sensing technology in an underwater environment. An acoustic orthogonal frequencydivisionmultiplexing (OFDM) signal transmitted through a fiber-optic cable deployed in a standard 40-meter-scale underwater testbed.
Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiber-optic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory-recorded fiber-optic ESC-50 datasets and a real-world fiber-optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.
Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In DAS, observed data, which is comprised of multichannel data, has suffered from severe noise levels because of the optical noise or the installation methods. In conventional methods for denoising DAS data, signal-processing- or deep-neural-network (DNN)-based models have been studied. The signal-processing-based methods have the interpretability, i.e., non-black box. The DNN-based methods are good at flexibility designing network architectures and objective functions, that is, priors. However, there is no balance between the interpretability and the flexibility of priors in the DAS studies. The DNN-based methods also require a large amount of training data in general. To address the problems, we propose a DNN-structure signal-processing-based denoising method in this paper. As the priors of DAS, we employ spatial knowledge; low rank and channel-dependent sensitivity using the DNN-based structure.The result of fiber-acoustic sensing shows that the proposed method outperforms the conventional methods and the robustness to the number of the spatial ranks. Moreover, the optimized parameters of the proposed method indicate the relationship with the channel sensitivity; the interpretability.
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NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.