<ul data-eligibleForWebStory="true"><li>A new physics-informed DAS neural network paradigm is proposed for diverse distributed acoustic sensing applications.</li><li>This paradigm does not require real-world events data for training, as it generates DAS events data through physical modeling.</li><li>The network is trained to remove background noise in DAS data, showing effectiveness in event identification and fault monitoring applications.</li><li>The paradigm demonstrates generalization in different sites and achieves a fault diagnosis accuracy of 91.8% in belt conveyor field without test site data for training.</li></ul>

Physics-informed network paradigm with data generation and background noise removal for diverse distributed acoustic sensing applications

Discover more