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Abstract

Disclosed are machine learning (ML) based Distributed Fiber Optic Sensing (DFOS) systems, methods, and structures for Sonic Alert Pattern (SNAP) event detection performed in real time including an intelligent SNAP informatic system in conjunction with DFOS/Distributed Acoustic Sensing (DAS) and machine learning technologies that utilize SNAP vibration signals as an indicator. Without installation of additional sensors, vibration signals indicative of SNAP events are detected along a length of an existing optical fiber through DAS. Raw DFOS data is utilized—and not DFOS waterfall data—resulting in faster and more accurate information derivation as rich, time-frequency information in the raw DFOS/DAS waveform data is preserved. A deep learning module Temporal Relation Network (TRN) that accurately detects SNAP events from among chaotic signals of normal traffic is employed, making it reliable when applied to busy roads with dense traffic and vehicles of different speed.

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First Claim