Real-time, commercial implementation of machine learning for distributed acoustic sensing over ∼100 000 km of cumulative optical fiber deployments
TL;DR
This work overviews the real-time, commercial implementation of machine learning for DAS, including novel, domain-knowledge-based representations of DAS data in multiple dimensions, their use in deep learning-based artificial neural networks, and real-time deployment via edge-based GPUs.
Abstract
In distributed acoustic sensing (DAS), a 100 km-scale optical fiber is transformed into ∼100 000 independent, synchronized, and meter-spaced acoustic sensors, each having the combined bandwidth of geophones, accelerometers, and microphones. Recently, there has been significant interest in applying machine learning to DAS. Machine learning can extract actionable information from otherwise esoteric DAS data. However, real-time, commercial implementation remains a challenge due to the intensive computation required for ∼100 000 sensors sampling at kHz rates (Gbps-scale data). This work overviews the real-time, commercial implementation of machine learning for DAS. This includes novel, domain-knowledge-based representations of DAS data in multiple dimensions (e.g., space, time, and frequency), their use in deep learning-based artificial neural networks, and real-time deployment via edge-based GPUs. We overview ∼100 000 km of cumulative commercial and academic DAS deployments that are disruptively innovating situational awareness in physical security, physical monitoring of the natural environment, and physical monitoring of critical infrastructure, such as telecommunications, transportation, and energy.