Figure | Schematic of the proposed physics-informed DAS neural network paradigm. (IMAGE)

Light Publishing Center, Changchun Institute of Optics, Fine Mechanics And Physics, CAS

Figure | Schematic of the proposed physics-informed DAS neural network paradigm.

Caption

Figure | Schematic of the proposed physics-informed DAS neural network paradigm. a Schematic diagram of the DAS system and its application. SMF single-mode fiber, RF radio frequency, AWG arbitrary waveform generator, AOM acousto-optic modulator, EDFA erbium-doped fiber amplifier, BPD balanced photodetector, ADC analog-to-digital converter, FUT fiber under test. The DAS system simultaneously detects both background and event signals in practical applications. b Traditional design process of DAS signal processing algorithms. ANN artificial neural network. A long-term data accumulation process is required to obtain a sufficient amount of event data, whether labeled or not. c Physics-informed DAS neural network paradigm. PIGN physics-informed generative network. The training of PIGN, noise-removal net, and classification network in the network paradigm does not require any real-world event data. PIGN is used to generate data, the noise-removal net is used to remove background noise, and the classification network is used for event recognition.

Credit

Yangyang Wan et al.

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CC BY

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