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.

Posts

Sarper Ozharar Receives Achievement in Science and Technology Award from Koç University

Sarper Ozharar was awarded an Achievement in Science and Technology Award from Koç University on their notable 30th anniversary.  As an alumnus of this esteemed institution, Sarper shared that this recognition is especially meaningful to him, marking a significant milestone in his professional journey.

Beyond Communication: Telecom Fiber Networks for Rain Detection and Classification

We present the field trial of an innovative neural network and DAS-based technique, employing a pre-trained CNN fine-tuning strategy for effective rain detection and classification within two practical scenarios.

Utility Pole Localization by Learning From Ambient Traces on Distributed Acoustic Sensing

Utility pole localization by learning from ambient traces on distributed acoustic sensing Utility pole detection and localization is the most fundamental application in aerial-optic cables using distributed acoustic sensing (DAS). The existing pole localization method recognizes the hammer knock signal on DAS traces by learning from knocking vibration patterns. However, it requires many efforts for data collection such as knocking every pole and manually labeling the poles’ locations, making this labor-intensive solution expensive, inefficient, and highly error prone. In this paper, we propose a pole localization solution by learning the ambient data collected from a DAS system, which are vibration patterns excited by random ambient events, such as wind and nearby traffic. In detail, we investigate a universal framework for learning representations of ambient data in the frequency domain by contrastive learning of the similarity of low and high-frequency series. A Gaussian-based data reweighting kernel is employed for eliminating the effect of the label noise. Experimental results demonstrate the proposed methods outperform the existing contrastive learning methods on the real-world DAS ambient dataset.

Drone Detection and Localization using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology

Drone Detection and Localization using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology In recent years, the widespread use of drones has led to serious concerns about safety and privacy. Drone detection using microphone arrays has proven to be a promising method. However, it is challenging for microphones to serve large-scale applications due to the issues of synchronization, complexity, and data management. Moreover, distributed acoustic sensing (DAS) using optical fibers has demonstrated its advantages in monitoring vibrations over long distances but does not have the necessary sensitivity for weak airborne acoustics. In this work, we present, to the best of our knowledge, the first fiber-optic quasi-distributed acoustic sensing demonstration for drone surveillance. We develop enhanced fiber-optic acoustic sensors (FOASs) for DAS to detect drone sound. The FOAS shows an ultra-high measured sensitivity of −101.21 re. 1rad/µPa, as well as the capability for high-fidelity speech recovery. A single DAS can interrogate a series of FOASs over a long distance via optical fiber, enabling intrinsic synchronization and centralized signal processing.We demonstrate the field test of drone detection and localization by concatenating four FOASs as DAS. Both the waveforms and spectral features of the drone sound are recognized. With acoustic field mapping and data fusion, accurate drone localization is achieved with a root-mean-square error (RMSE) of 1.47 degrees. This approach holds great potential in large-scale sound detection applications, such as drone detection or city event monitoring.

Distributed fiber optic sensing over readily available telecom fiber networks

Distributed fiber optic sensing over readily available telecom fiber networks Distributed Fiber Optic Sensing (DFOS) systems rely on measuring and analyzing different properties of the backscattered light of an optical pulse propagating along a fiber cable. DFOS systems can measure temperature, strain, vibrations, or acoustic excitations on the fiber cable and to their unique specifications, they have many applications and advantages over competing technologies. In this talk we will focus on the challenges and applications of DFOS systems using outdoor grade telecom fiber networks instead of standard indoor or some specialty fiber cables.

Using Global Fiber Networks for Environmental Sensing

Using Global Fiber Networks for Environmental Sensing We review recent advances in distributed fiber optic sensing (DFOS) and their applications. The scattering mechanisms in glass, which are exploited for reflectometry-based DFOS, are Rayleigh, Brillouin, and Raman scatterings. These are sensitive to either strain and/or temperature, allowing optical fiber cables to monitor their ambient environment in addition to their conventional role as a medium for telecommunications. Recently, DFOS leveraged technologies developed for telecommunications, such as coherent detection, digital signal processing, coding, and spatial/frequency diversity, to achieve improved performance in terms of measurand resolution, reach, spatial resolution, and bandwidth. We review the theory and architecture of commonly used DFOS methods. We provide recent experimental and field trial results where DFOS was used in wide-ranging applications, such as geohazard monitoring, seismic monitoring, traffic monitoring, and infrastructure health monitoring. Events of interest often have unique signatures either in the spatial, temporal, frequency, or wavenumber domains. Based on the temperature and strain raw data obtained from DFOS, downstream postprocessing allows the detection, classification, and localization of events. Combining DFOS with machine learning methods, it is possible to realize complete sensor systems that are compact, low cost, and can operate in harsh environments and difficult-to-access locations, facilitating increased public safety and smarter cities.

Rain Intensity Detection and Classification with Pre-existing Telecom Fiber Cables

Rain Intensity Detection and Classification with Pre-existing Telecom Fiber Cables For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.

Simultaneous Fiber Sensing and Communications

Simultaneous Fiber Sensing and Communications We review recent advances aimed at increasing the reach of distributed fiber optic sensing with simultaneous data transmission. We review two methods based on measurement of accumulated phase on telecom signals, and chirp-pulsed DAS with inline amplification and frequency diversity.

Distributed Acoustic Sensing for Datacenter Optical Interconnects using Self-Homodyne Coherent Detection

Distributed Acoustic Sensing for Datacenter Optical Interconnects using Self-Homodyne Coherent Detection We demonstrate distributed acoustic sensing (DAS) over a bidirectional datacenter link which uses self-homodyne coherent detection for the data signal. Frequency multiplexing allows sharing the optoelectronic hardware, and enables DAS as an auxiliary function.

Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets

Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.